Experts Matter. Find Yours.

Google's New AI Overviews Isn’t Just Another Search Update

Google's recent rollout of AI Overviews (previously called “Search Generative Experience”) at its annual developer conference is being hailed as the biggest transformation in search since the company was founded. This isn’t a side project for Google — it fundamentally alters how content gets discovered, consumed, and valued online. If you're in marketing, PR, content strategy, or run a business that depends on online visibility, this requires a fundamental shift in your thinking. What Is AI Overviews? Instead of showing users a familiar list of blue links and snippets, Google now uses artificial intelligence to generate a summary answer at the very top of many search results pages. This AI-generated box pulls together content from across the web and tries to answer the user’s question instantly—without requiring them to click through to individual websites. Here’s what that looks like: You type in a question like “What are the best strategies for handling a media crisis?” Instead of just links, you see a big AI-generated paragraph with summarized strategies, possibly quoting or linking to 3-5 sources—some of which might not even be visible unless you scroll or expand the summary.  Welcome to the new digital gatekeeper. Elizabeth Reid, VP of Search at Google states "Our new Gemini model customized for Google Search brings together Gemini’s advanced capabilities — including multi-step reasoning, planning and multimodality — with our best-in-class Search systems. Let's breakdown this technobabble. Think of Gemini as the brain behind Google’s search engine that’s now: Even More Focused on User intent For years, SEO strategies were built around guessing and gaming the right keywords: “What exact phrase are people typing into Google?” That approach led to over-optimized content — pages stuffed with phrases like “best expert speaker Boston cleantech” — written more for algorithms than actual humans.  But with Google Gemini and other AI models now interpreting search queries like a smart research assistant, the game has changed entirely.  Google is no longer just matching phrases — it’s interpreting what the user wants to do and why they’re asking. Here’s What That Looks Like: Let’s say someone searches: “How do I find a reputable expert on fusion energy who can speak at our cleantech summit?” In the old system, pages that mentioned “renewable energy,” “expert,” and “speaker” might rank — regardless of whether they actually helped the user solve their problem. Now Google more intuitively understands: • The user wants to evaluate credibility • The user is planning an event • The user needs someone available to speak • The context is likely professional or academic If your page simply has the right keywords but doesn’t send the right signals — you’re invisible. Able to plan ahead Google and AI search platforms now go beyond just grabbing facts. They string together pieces of information to answer more complex, multi-step queries.  In traditional search, users ask one simple question at a time. But with multi-step queries, users are increasingly expecting one search to handle a series of related questions or tasks all at once — and now Google can actually follow along and reason through those steps.  So imagine you’re planning a conference. A traditional search might look like: "Best conference venues in Boston” But a multi-step query might be: “Find a conference venue in Boston with breakout rooms, check availability in October, and suggest nearby hotels with group rates.” This used to require three or four different searches, and you’d piece it together yourself. Now Google can handle that entire chain of related tasks, plan the steps behind the scenes, and return a highly curated answer — often pulling from multiple sources of structured and unstructured data. Even Better at understanding context Google now gets the difference between ‘a speaker at a conference’ and ‘a Bluetooth speaker’ — because it understands what you mean, not just what you type.”  In the past, Google would match keywords literally. If your page had the word “speaker,” it might rank for anything from event keynotes to audio gear. That’s why so many search results felt off or required extra digging. Now Google reads between the lines. It understands that “conference speaker” likely refers to a person who gives talks, possibly with credentials, experience, and a bio.  And that “Bluetooth speaker” is a product someone might want to compare or buy. Why this matters for marketers: If you’re relying on vague or generic content — or just “keyword-stuffing” — your pages will fall flat. Google is no longer fooled by superficial matches. It wants depth, clarity, and specificity. Reads More Than Just Text Google now processes images, videos, charts, infographics, and even audio — and uses that multimedia information to answer search queries more completely.  This now means that your content isn’t just being read like a document — it’s being watched, listened to, and interpreted like a human would. For example: • A chart showing rising enrollment in nursing programs might get picked up as supporting evidence for a story about healthcare education trends. • A YouTube video of your CEO speaking at a conference might be indexed as proof of thought leadership. • An infographic explaining how your service works could surface in an AI-generated summary — even if the keyword isn’t mentioned directly in text. Ignoring multimedia formats?  Then, your competitors’ visual storytelling could be outperforming your plain content.  Because you're not giving Google the kind of layered, helpful content that Gemini is now designed to highlight. Why This Matters There's a big risk here.  Marketers who ignore these developments are in danger of becoming invisible in search. Your old SEO tricks won’t work. Your content won’t appear in AI summaries. Your organization won’t be discovered by journalists, customers, or partners who now rely on smarter search results to make decisions faster. If you’re in communications, PR, media relations, or digital marketing, here’s the key message. You are no longer just fighting for links. You need to fight to be included in the Google AI summary itself at the top of search results - that's the new #1 goal.  Why? Journalists can now find their answers before ever clicking on your beautifully written news page. Prospective students, donors, and customers will often just see the AI’s version of your content. Your brand’s visibility now hinges on being seen as “AI-quotable.” If your organization isn’t optimized for this new AI-driven landscape, you risk becoming invisible at the very moment people are searching for what you offer. How You Can Take Action (and Why Your Role Is More Important Than Ever) This isn’t just an IT or SEO problem. It’s a communications strategy opportunity—and you are central to the solution. What You Can Do Now to Prepare for AI Overviews 1. Get Familiar with How AI “Reads” Your Content AI Overviews pull content from websites that are structured clearly, written credibly, and explain things in simple language. Action Items: Review your existing content: Is it jargon-heavy? Outdated? Lacking expert quotes or explanations? Then, it's time to clean house. 2. Collaborate with your SEO and Web Teams Communicators and content creators now need to work hand-in-hand with technical teams. Action Items: Check your pages to see if you are using proper schema markup.  Are you creating topic pages that explain complex ideas in simple, scannable formats? 3. Showcase Human Expertise AI values content backed by real people—especially experts with credentials. Action Items: Make sure your expert profiles are up to date.  Make sure you continue to enhance them with posts, links to media coverage, short videos, images and infographics that highlight the voices behind your brand and make you stand out in search. 4. Don’t Just Publish—Package AI favors content that it can easily digest and display such as summary paragraphs, FAQs, and bold headers that provide structure for search engines.  This also makes your content more scannable and engaging to humans. Action Items: Repurpose your best content into AI-friendly formats: think structured lists, how-tos, and definitions. 5. Monitor Your Presence in AI Overviews Regularly search key topics related to your organization and see what shows up. Action Items: Is your content featured? If not, whose is—and identify what they doing differently. A New Role for Communications: From Media Pitches to Machine-Readable Influence This isn’t the end of communications as we know it—it’s an evolution. Your role now includes helping your organization communicate clearly to machines as well as to people. Think of it as “PR for the algorithm.”  You’re not just managing narratives for the public—you’re shaping what AI systems say about you and your brand. That means: • Ensuring your best ideas and experts are front and center online. • Making complex information simple and quotable. • Collaborating cross-functionally like never before. Final Thought: AI Search Rewards the Prepared Google’s new AI Overviews are here. They’re not a beta test. This is the future of search, and it’s already rolling out. If your institution, company, or nonprofit wants to be discovered, trusted, and quoted, you can no longer afford to ignore how AI interprets your online presence. Communications and media professionals are now at the front lines of discoverability. And the best way to lead is to act now, work collaboratively, and elevate your role in this new era of search. Want to see how leading organizations are getting ahead in the age of AI search? Discover how ExpertFile is helping corporations, universities, healthcare institutions and industry associations transform their knowledge into AI-optimized assets — boosting visibility, credibility, and media reach. Get your free download of our app at www.expertfile.com

NASA Asks Researchers to Help Define Trustworthiness in Autonomous Systems

A Florida Tech-led group of researchers was selected to help NASA solve challenges in aviation through its prestigious University Leadership Initiative (ULI) program. Over the next three years, associate professor of computer science and software engineering Siddhartha Bhattacharyya and professor of aviation human factors Meredith Carroll will work to understand the vital role of trust in autonomy. Their project, “Trustworthy Resilient Autonomous Agents for Safe City Transportation in the Evolving New Decade” (TRANSCEND), aims to establish a common framework for engineers and human operators to determine the trustworthiness of machine-learning-enabled autonomous aviation safety systems. Autonomous systems are those that can perform independent tasks without requiring human control. The autonomy of these systems is expected to be enhanced with intelligence gained from machine learning. As a result, intelligence-based software is expected to be increasingly used in airplanes and drones. It may also be utilized in airports and to manage air traffic in the future. Learning-enabled autonomous technology can also act as contingency management when used in safety applications, proactively addressing potential disruptions and unexpected aviation events. TRANSCEND was one of three projects chosen for the latest ULI awards. The others hail from Embry-Riddle Aeronautical University in Daytona Beach – researching continuously updating, self-diagnostic vehicle health management to enhance the safety and reliability of Advanced Air Mobility vehicles – and University of Colorado Boulder – investigating tools for understanding and leveraging the complex communications environment of collaborative, autonomous airspace systems. Florida Tech’s team includes nine faculty members from five universities: Penn State; North Carolina A&T State University; University of Florida; Stanford University; Santa Fe College. It also involves the companies Collins Aerospace in Cedar Rapids, Iowa and ResilienX of Syracuse, New York. Carroll and Bhattacharyya will also involve students throughout the project. Human operators are an essential component of aviation technology – they monitor independent software systems and associated data and intervene when those systems fail. They may include flight crew members, air traffic controllers, maintenance personnel or safety staff monitoring overall system safety. A challenge in implementing independent software is that engineers and operators have different interpretations of what makes a system “trustworthy,” Carroll and Bhattacharyya explained. Engineers who develop autonomous software measure trustworthiness by the system’s ability to perform as designed. Human operators, however, trust and rely on systems to perform as they expect – they want to feel comfortable relying on a system to make an aeronautical decision in flight, such as how to avoid a traffic conflict or a weather event. Sometimes, that reliance won’t align with design specifications. Equally important, operators also need to trust that the software will alert them when it needs a human to take over. This may happen if the algorithm driving the software encounters a scenario it wasn’t trained for. “We are looking at how we can integrate trust from different communities – from human factors, from formal methods, from autonomy, from AI…” Bhattacharyya said. “How do we convey assumptions for trust, from design time to operation, as the intelligent systems are being deployed, so that we can trust them and know when they’re going to fail, especially those that are learning-enabled, meaning they adapt based on machine learning algorithms?” With Bhattacharyya leading the engineering side and Carroll leading the human factors side, the research group will begin bridging the trust gap by integrating theories, principles, methods, measures, visualizations, explainability and practices from different domains – this will build the TRANSCEND framework. Then, they’ll test the framework using a diverse range of tools, flight simulators and intelligent decision-making to demonstrate trustworthiness in practice. This and other data will help them develop a safety case toolkit of guidelines for development processes, recommendations and suggested safety measures for engineers to reference when designing “trustworthy,” learning-enabled autonomous systems. Ultimately, Bhattacharyya and Carroll hope their toolkit will lay the groundwork for a future learning-enabled autonomous systems certification process. “The goal is to combine all our research capabilities and pull together a unified story that outputs unified products to the industry,” Carroll said. “We want products for the industry to utilize when implementing learning-enabled autonomy for more effective safety management systems.” The researchers also plan to use this toolkit to teach future engineers about the nuances of trust in the products they develop. Once developed, they will hold outreach events, such as lectures and camps, for STEM-minded students in the community. If you're interested in connecting with Meredith Carroll or Siddhartha Bhattacharyya - simply click on the expert's profile or contact  Adam Lowenstein, Director of Media Communications at Florida Institute of Technology at adam@fit.edu to arrange an interview today.

Expert Perspective: Mitigating Bias in AI: Sharing the Burden of Bias When it Counts Most

Whether getting directions from Google Maps, personalized job recommendations from LinkedIn, or nudges from a bank for new products based on our data-rich profiles, we have grown accustomed to having artificial intelligence (AI) systems in our lives. But are AI systems fair? The answer to this question, in short—not completely. Further complicating the matter is the fact that today’s AI systems are far from transparent. Think about it: The uncomfortable truth is that generative AI tools like ChatGPT—based on sophisticated architectures such as deep learning or large language models—are fed vast amounts of training data which then interact in unpredictable ways. And while the principles of how these methods operate are well-understood (at least by those who created them), ChatGPT’s decisions are likened to an airplane’s black box: They are not easy to penetrate. So, how can we determine if “black box AI” is fair? Some dedicated data scientists are working around the clock to tackle this big issue. One of those data scientists is Gareth James, who also serves as the Dean of Goizueta Business School as his day job. In a recent paper titled “A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification” Dean James—along with coauthors Bradley Rava, Wenguang Sun, and Xin Tong—have proposed a new framework to help ensure AI decision-making is as fair as possible in high-stakes decisions where certain individuals—for example, racial minority groups and other protected groups—may be more prone to AI bias, even without our realizing it. In other words, their new approach to fairness makes adjustments that work out better when some are getting the short shrift of AI. Gareth James became the John H. Harland Dean of Goizueta Business School in July 2022. Renowned for his visionary leadership, statistical mastery, and commitment to the future of business education, James brings vast and versatile experience to the role. His collaborative nature and data-driven scholarship offer fresh energy and focus aimed at furthering Goizueta’s mission: to prepare principled leaders to have a positive influence on business and society. Unpacking Bias in High-Stakes Scenarios Dean James and his coauthors set their sights on high-stakes decisions in their work. What counts as high stakes? Examples include hospitals’ medical diagnoses, banks’ credit-worthiness assessments, and state justice systems’ bail and sentencing decisions. On the one hand, these areas are ripe for AI-interventions, with ample data available. On the other hand, biased decision-making here has the potential to negatively impact a person’s life in a significant way. In the case of justice systems, in the United States, there’s a data-driven, decision-support tool known as COMPAS (which stands for Correctional Offender Management Profiling for Alternative Sanctions) in active use. The idea behind COMPAS is to crunch available data (including age, sex, and criminal history) to help determine a criminal-court defendant’s likelihood of committing a crime as they await trial. Supporters of COMPAS note that statistical predictions are helping courts make better decisions about bail than humans did on their own. At the same time, detractors have argued that COMPAS is better at predicting recidivism for some racial groups than for others. And since we can’t control which group we belong to, that bias needs to be corrected. It’s high time for guardrails. A Step Toward Fairer AI Decisions Enter Dean James and colleagues’ algorithm. Designed to make the outputs of AI decisions fairer, even without having to know the AI model’s inner workings, they call it “fairness-adjusted selective inference” (FASI). It works to flag specific decisions that would be better handled by a human being in order to avoid systemic bias. That is to say, if the AI cannot yield an acceptably clear (1/0 or binary) answer, a human review is recommended. To test the results for their “fairness-adjusted selective inference,” the researchers turn to both simulated and real data. For the real data, the COMPAS dataset enabled a look at predicted and actual recidivism rates for two minority groups, as seen in the chart below. In the figures above, the researchers set an “acceptable level of mistakes” – seen as the dotted line – at 0.25 (25%). They then compared “minority group 1” and “minority group 2” results before and after applying their FASI framework. Especially if you were born into “minority group 2,” which graph seems fairer to you? Professional ethicists will note there is a slight dip to overall accuracy, as seen in the green “all groups” category. And yet the treatment between the two groups is fairer. That is why the researchers titled their paper “a burden shared is a burdened halved.” Practical Applications for the Greater Social Good “To be honest, I was surprised by how well our framework worked without sacrificing much overall accuracy,” Dean James notes. By selecting cases where human beings should review a criminal history – or credit history or medical charts – AI discrimination that would have significant quality-of-life consequences can be reduced. Reducing protected groups’ burden of bias is also a matter of following the laws. For example, in the financial industry, the United States’ Equal Credit Opportunity Act (ECOA) makes it “illegal for a company to use a biased algorithm that results in credit discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because a person receives public assistance,” as the Federal Trade Commission explains on its website. If AI-powered programs fail to correct for AI bias, the company utilizing it can run into trouble with the law. In these cases, human reviews are well worth the extra effort for all stakeholders. The paper grew from Dean James’ ongoing work as a data scientist when time allows. “Many of us data scientists are worried about bias in AI and we’re trying to improve the output,” he notes. And as new versions of ChatGPT continue to roll out, “new guardrails are being added – some better than others.” “I’m optimistic about AI,” Dean James says. “And one thing that makes me optimistic is the fact that AI will learn and learn – there’s no going back. In education, we think a lot about formal training and lifelong learning. But then that learning journey has to end,” Dean James notes. “With AI, it never ends.” Gareth James is the John H. Harland Dean of Goizueta Business School. If you're looking to connect with him - simply click on his icon now to arrange an interview today.

AI-powered model predicts post-concussion injury risk in college athletes

Athletes who suffer a concussion have a serious risk of reinjury after returning to play, but identifying which athletes are most vulnerable has always been a bit of a mystery, until now. Using artificial intelligence (AI), University of Delaware researchers have developed a novel machine learning model that predicts an athlete’s risk of lower-extremity musculoskeletal (MKS) injury after concussion with 95% accuracy. A recent study published in Sports Medicine details the development of the AI model, which builds on previously published research showing that the risk of post-concussion injury doubles, regardless of the sport. The most common post-concussive injuries include sprains, strains, or even broken bones or torn ACLs. “This is due to brain changes we see post-concussion,” said Thomas Buckley, professor of kinesiology and applied physiology at the College of Health Sciences. These brain changes affect athletes’ balance, cognition, and reaction times and can be difficult to detect in standard clinical testing. “Even a minuscule difference in balance, reaction time, or cognitive processing of what’s happening around you can make the difference between getting hurt and not,” Buckley said. How AI is changing injury risk assessment Recognizing the need for enhanced injury reduction risk tools, Buckley collaborated with colleagues in UD’s College of Engineering, Austin Brockmeier, assistant professor of electrical and computer engineering, and César Claros, a fourth-year doctoral student; Wei Qian, associate professor of statistics in the College of Agriculture and Natural Resources; and former KAAP postdoctoral fellow Melissa Anderson, who’s now an assistant professor at Ohio University. To assess injury risk, Brockmeier and Claros developed a comprehensive AI model that analyzes more than 100 variables, including sports and medical histories, concussion type, and pre- and post-concussion cognitive data. “Every athlete is unique, especially across various sports,” said Brockmeier. “Tracking an athlete’s performance over time, rather than relying on absolute values, helps identify disturbances, deviations, or deficits that, when compared to their baseline, may signal an increased risk of injury.” While some sports, such as football, carry higher injury risk, the model revealed that individual factors are just as important as the sport played. “We tested a version of the model that doesn’t have access to the athlete’s sport, and it still accurately predicted injury risk,” Brockmeier said. “This highlights how unique characteristics—not just the inherent risks of a sport—play a critical role in determining the likelihood of future injury,” said Brockmeier. The research, which tracked athletes over two years, also found that the risk of MSK injury post-concussion extends well into the athlete’s return to play. “Common sense would suggest that injuries would occur early in an athlete’s return to play, but that’s simply not true,” said Buckley. “Our research shows that the risk of future injury increases over time as athletes compensate and adapt to small deficits they may not even be aware of.” The next step for Buckey’s Concussion Research Lab is to further collaborate with UD Athletics’ strength and conditioning staff to design real-time interventions that could reduce injury risk. Beyond sports: AI’s potential in aging research The implications of the UD-developed machine-learning model extend far beyond sports. Brockmeier believes the algorithm could be used to predict fall risk in patients with Parkinson’s disease. Claros is also exploring how the injury risk reduction model can be applied to aging research with the Delaware Center for Cognitive Aging. “We want to use brain measurements to investigate whether baseline lifestyle measurements such as weight, BMI, and smoking history are predictive of future mild cognitive impairment or Alzheimer’s disease,” said Claros. To arrange an interview with Buckley, email UD's media relations team at MediaRelations@udel.edu

NASA Grant Funds Research Exploring Methods of Training Vision-Based Autonomous Systems

Conducting research at 5:30 a.m. may not be everybody’s first choice. But for Siddhartha Bhattacharyya and Ph.D. students Mohammed Abdul, Hafeez Khan and Parth Ganeriwala, it’s an essential part of the process for their latest endeavor. Bhattacharyya and his students are developing a more efficient framework for creating and evaluating image-based machine learning classification models for autonomous systems, such as those guiding cars and aircraft. That process involves creating new datasets with taxiway and runway images for vision-based autonomous aircraft. Just as humans need textbooks to fuel their learning, some machines are taught using thousands of photographs and images of the environment where their autonomous pupil will eventually operate. To help ensure their trained models can identify the correct course to take in a hyper-specific environment – with indicators such as centerline markings and side stripes on a runway at dawn – Bhattacharyya and his Ph.D. students chose a December morning to rise with the sun, board one of Florida Tech’s Piper Archer aircraft and photograph the views from above. Bhattacharyya, an associate professor of computer science and software engineering, is exploring the boundaries of operation of efficient and effective machine-learning approaches for vision-based classification in autonomous systems. In this case, these machine learning systems are trained on video or image data collected from environments including runways, taxiways or roadways. With this kind of model, it can take more than 100,000 images to help the algorithm learn and adapt to an environment. Today’s technology demands a pronounced human effort to manually label and classify each image. This can be an overwhelming process. To combat that, Bhattacharyya was awarded funding from NASA Langley Research Center to advance existing machine learning/computer vision-based systems, such as his lab’s “Advanced Line Identification and Notation Algorithm” (ALINA), by exploring automated labeling that would enable the model to learn and classify data itself – with humans intervening only as necessary. This measure would ease the overwhelming human demand, he said. ALINA is an annotation framework that Hafeez and Parth developed under Bhattacharyya’s guidance to detect and label data for algorithms, such as taxiway line markings for autonomous aircraft. Bhattacharyya will use NASA’s funding to explore transfer learning-based approaches, led by Parth, and few-shot learning (FSL) approaches, led by Hafeez. The researchers are collecting images via GoPro of runways and taxiways at airports in Melbourne and Grant-Valkaria with help from Florida Tech’s College of Aeronautics. Bhattacharyya’s students will take the data they collect from the airports and train their models to, in theory, drive an aircraft autonomously. They are working to collect diverse images of the runways – those of different angles and weather and lighting conditions – so that the model learns to identify patterns that determine the most accurate course regardless of environment or conditions. That includes the daybreak images captured on that December flight. “We went at sunrise, where there is glare on the camera. Now we need to see if it’s able to identify the lines at night because that’s when there are lights embedded on the taxiways,” Bhattacharyya said. “We want to collect diverse datasets and see what methods work, what methods fail and what else do we need to do to build that reliable software.” Transfer learning is a machine learning technique in which a model trained to do one task can generalize information and reuse it to complete another task. For example, a model trained to drive autonomous cars could transfer its intelligence to drive autonomous aircraft. This transfer helps explore generalization of knowledge. It also improves efficiency by eliminating the need for new models that complete different but related tasks. For example, a car trained to operate autonomously in California could retain generalized knowledge when learning how to drive in Florida, despite different landscapes. “This model already knows lines and lanes, and we are going to train it on certain other types of lines hoping it generalizes and keeps the previous knowledge,” Bhattacharyya explained. “That model could do both tasks, as humans do.” FSL is a technique that teaches a model to generalize information with just a few data samples instead of the massive datasets used in transfer learning. With this type of training, a model should be able to identify an environment based on just four or five images. “That would help us reduce the time and cost of data collection as well as time spent labeling the data that we typically go through for several thousands of datasets,” Bhattacharyya said. Learning when results may or may not be reliable is a key part of this research. Bhattacharyya said identifying degradation in the autonomous system’s performance will help guide the development of online monitors that can catch errors and alert human operators to take corrective action. Ultimately, he hopes that this research can help create a future where we utilize the benefits of machine learning without fear of it failing before notifying the operator, driver or user. “That’s the end goal,” Bhattacharyya said. “It motivates me to learn how the context relates to assumptions associated with these images, that helps in understanding when the autonomous system is not confident in its decision, thus sending an alert to the user. This could apply to a future generation of autonomous systems where we don’t need to fear the unknown – when the system could fail.” Siddhartha (Sid) Bhattacharyya’s primary area of research expertise/interest is in model based engineering, formal methods, machine learning engineering, and explainable AI applied to intelligent autonomous systems, cyber security, human factors, healthcare, explainable AI, and avionics. His research lab ASSIST (Assured Safety, Security, and Intent with Systematic Tactics) focuses on the research in the design of innovative formal methods to assure performance of intelligent systems, machine learning engineering to characterize intelligent systems for safety and model based engineering to analyze system behavior. Siddhartha Bhattacharyya is available to speak with media. Contact Adam Lowenstein, Director of Media Communications at Florida Institute of Technology at adam@fit.edu to arrange an interview today.

A Beginner’s Guide to Expertise Marketing

Audiences today are consuming more digital content than ever, but they’ve also become far more discerning. Algorithms, AI search summaries, and social platforms have changed how information is discovered and trusted. The result is that organizations often get caught up in pushing out content quickly—only to be overlooked when it lacks depth or credibility. From misinformation to shallow click-driven posts, audiences are quick to disengage. What they’re seeking now are authoritative voices backed by proven expertise. That’s where Expertise Marketing comes in: a strategy focused on showcasing real knowledge, research, and experience in ways that build trust, attract attention, and strengthen reputation. According to Edelman’s Trust Barometer Study, experts play a vital role in establishing credibility amongst audiences and developing more meaningful interactions with businesses and organizations. As far back as their 2019 report results showed that 56% of people trust businesses as a source of news and information while only 47% trusted the government. On top of that, they also reported that 73% of participants were worried about false information or fake news being used as a weapon. This distrust has only gotten worse since COVID and the polarizing politics of recent years. With this in mind, there’s a real opportunity for knowledge-based organizations to step up and show their smarts through expertise marketing. What is Expertise Marketing? Expertise marketing is the practice of making the knowledge and skills of your human resources more visible to your partners and audiences. It draws attention to the value that your people can bring as brand ambassadors and strategically leverages the work your experts are doing to tell a more personal story. In many cases, expertise marketing can also be used to showcase your strengths in research and innovation. Creating a stronger digital presence, expertise marketing more effectively uses your channels to connect with audiences such as media, customers, partners and donors. It builds a sense of trust with your audiences and above all else, it helps establish your reputation as an industry leader. Expertise Marketing Defined: The practice of collectively promoting an organization's experts as brand ambassadors to demonstrate their skills or knowledge. Best practices to publish and connect organizational expertise in ways that foster internal discovery, collaboration, shared knowledge and diversity. Activities that leverage expertise to nurture conversations and connections with audiences such as media, customers, partners, government and funding agencies. How to Make Your Expertise More Visible Properly executed, expertise marketing is about harnessing your in-house expertise and making it more visible. By delivering comprehensive, relevant information in a visually engaging format, you can create a window into your organization that helps audiences better understand your offering and encourages more meaningful conversations. Here are three areas where expertise excels: On Your Website There’s a good chance that you already created touchpoints for expertise marketing but they’re just not optimized for audiences. For example, many organizations are unaware that the “About Page” is the second most visited page on a website and may overlook its potential for attracting audiences. Other webpages that strongly benefit from expert content include: Speaker’s Bureaus Media Rooms Employee Directories Faculty Directories Blogs Employee Intranets Awards Recognition Research & Technology Transfer Through Search Engines Content marketing and search engine optimization (SEO) go hand-in-hand – and it’s key to making your expertise more visible. In Google’s search algorithm, factors like trust and authority are significantly impacted by items such as content and expertise. While SEO is no small task, tools like the ExpertFile Platform are designed to make aggregating and optimizing expert content as seamless as possible. In addition, organizations can also improve their rankings by: Identifying and showcasing a range of expertise Using rich media to display expert content Regularly updating your website with expert content Producing content related to current trends and emerging news Through Distribution Networks Showcasing your experts isn’t just about hosting profiles on your own website—it’s about ensuring they are discoverable where key audiences are already looking. By publishing expert content on dedicated search engines such as expertfile.com and the ExpertFile Mobile App, organizations dramatically expand their reach beyond their immediate networks. These channels are designed for the very audiences that matter most—media, event organizers, research partners, donors, and prospective clients—who are actively searching for credible voices to inform stories, shape agendas, and build partnerships. Leveraging these distribution networks amplifies visibility, positioning your experts as go-to authorities well beyond the boundaries of your institutional website. In The Media For many organizations, media opportunities are an afterthought but it’s the perfect way to highlight your expertise and attract a broad range of audiences. Media outlets are constantly on the hunt for topic-specific experts to speak at conferences, weigh-in on their editorials and enhance the overall quality of their reporting. By making your experts more visible to this audience, you’re not only building your brand reputation as an industry authority but you’re also creating opportunities for new revenue. Starting an Expertise Marketing Program Bringing an expertise marketing program to life starts by taking a deeper look at your human resources and pinpointing the people in your organization who can support your expertise marketing initiatives. This post on Identifying Expertise is a great starting point for understanding what makes someone an expert and how you can position them for various tasks in your expertise marketing program. From there, it’s about getting buy-in from key stakeholders, collaborating across departments to surface expert content and strategizing with your team about where your expertise is best served. Download The Complete Guide to Expertise Marketing For a comprehensive look at how expertise marketing benefits the entire organization and drives measurable return on investment, follow the link below to download a copy of ExpertFile’s Complete Guide to Expertise Marketing for Corporate & Professional Services, Higher Education Institutions, Healthcare Institutions or Association & Not-for-Profits.

SEO: Why Expertise Ranks Higher

When the internet took off in the mid-90s, finding content wasn’t for the faint of heart. There were no directories or search engines and if you didn’t know where you wanted to go, you weren’t going very far. In the wild west of URLs, it became abundantly clear that we needed a better way to search. Yahoo brought us our first directory but in a list of websites, everyone’s content looked equal. That’s when Google stepped up to the plate. Right from the early days of search algorithms, they understood that people valued expert content and we needed a way to rank the credibility and integrity of a webpage. Drawing on his academic background, Larry Page introduced the concept that links could act like citations in a research paper. The original idea operated like a voting system; the more links, the higher the rank. While Google still places tremendous value on expertise, their algorithm for search engine optimization (SEO) has become significantly more complex. We know that it’s combination of on-page and off-page factors but at the end of the day, it boils down to delivering the quality content people are searching for. Source: Search Engine Land Give the People What They Want Today’s audiences want to build more meaningful connections with the institutions and businesses they engage, and that requires information. It’s not enough to put out an unimaginative website or tri-fold brochure. While these tactics may have worked a short time ago, traditional marketing often fails to deliver the level of detail audiences need to make purchasing decisions. From transparency to accountability, they have high expectations for organizations and the content they provide. They want to know how a product or service will uniquely work for them and how your organization aligns with their vision and values. Most of all, they need expert perspectives and proof you can deliver results. If you want to remain competitive, it’s essential to keep up with these rising demands for easy-to-find, high-quality content and secure you a key spot on Google’s SERP. Climbing the Ranks with Expertise Organizations, particularly those in knowledge-based industries (academia, consulting, professional services, medicine etc.), need to pay special attention to how Google is tuning its search algorithm to index information that is attributed to experts. Factors like quality, keyword research and freshness are all on-page tactics that help webpages improve their rank. With this in mind, here are just a few ways your experts and their content are contributing to your rank on Google: Meta-Tagging: Meta tags are snippets of text or rich media that help audiences understand what’s on your webpage from the Google SERP. To ensure they deliver the most relevant search results, both Google and YouTube have been updating their meta tagging and schema options to allow expert content to be indexed more descriptively. This includes everything from author attribution to expert answers the prestigious Rank Zero which highlights the best possible result to users at the very top of the page. Freshness: The freshness of content is correlated with content relevance, and it’s something Google highly values when ranking search. Not only will outdated employee biographies and profiles on your site negatively impact your ranking on search engines, but failing to deliver timely, relevant content on trending topics will also hold you back. By regularly updating your website with expert content, you’re showing that you’re an active contributor to that topic – building trust your audiences and increasing your rank on Google. Keywords: Google collects and indexes searches from users every day, so the richness of your keywords is critical to your search ranking. In many cases, you’ll find that expert content has a holistic way of providing information about high-searched topics in your industry. When an expert creates content, they not only include the targeted keywords, but they naturally incorporate latent semantic indexing as well which accounts for related terms. This all feeds into Google’s desire to surface the most relevant content and who better to do that than your experts. All of these factors are important considerations for improving your organic search rank. As a key driver in inbound traffic, improving your organic rank will not only increase brand awareness, but it also delivers higher-quality leads. And by sharing expert content on your digital channels, you’re showing your audiences and Google exactly what makes you an industry authority and why they should engage your business. Download The Complete Guide to Expertise Marketing For a comprehensive look at how expertise marketing benefits the entire organization and drives measurable return on investment, follow the link below to download a copy of ExpertFile’s Complete Guide to Expertise Marketing for Corporate & Professional Services, Higher Education Institutions, Healthcare Institutions or Association & Not-for-Profits.

Villanova Professor at the Forefront of Work to Tackle Quantum Threats

Securing Our Future Against Quantum Threats Security and privacy are values that everyone cherishes. No tech user wants their personal information getting into the wrong hands, which is why we have security measures in place to protect our private data: face ID to unlock our phones, two-factor authentication to log into banking apps and fingerprint technology to securely enter any system—from a computer to your front door. Encryption codes are used on each of these platforms to encode private data and allow only authorized users to access it. These measures are put in place to protect us, but new advancements in technology could soon challenge these secure systems that we have come to know and trust. Quantum computers are extraordinary machines capable of solving problems far beyond the scope of today’s standard computers. Although these computers are not commercially available, scientists harness their power for experimentation and data storage. Quantum computers excel in scientific development, but they may also prove to be a threat to existing technology that we use in our daily lives. Experts predict that by 2035, quantum computers could crack the very encryption codes that secure everyday transactions and data. Jiafeng Xie, PhD, associate professor of Electrical and Computer Engineering at Villanova University, is at the forefront of this battle, using his Security and Cryptography Lab to strengthen security measures against the threat of quantum computers. The Rise of Post-Quantum Cryptography Since quantum computer advancements are accelerating at an unprecedented pace, post-quantum cryptography (PQC) has emerged as a critical area of research and development. Scientists who study PQC are working to come up with new algorithms to encode our sensitive data, with a goal of being installed after quantum computers crack our current encryption systems. Without these new algorithms, once quantum computers break our current codes, sensitive data—whether personal, corporate or governmental—could be left vulnerable to malicious actors. The core problem of our current encryption system lies in the foundation of public-key cryptosystems. Public-key cryptography is a method of encryption where the user logs into a system using their own private “key”, and the back end of the system has a “key” as well. A “key” is a large numerical value that scrambles data so that it appears random. When a user logs in, their “key” can decrypt private information held by the public “key” in the system to ensure a secure login. This security method is safe right now, but these systems rely on mathematical principles that, while secure against classical computing attacks, are vulnerable to the immense processing power of quantum computers. At the heart of the vulnerability is Shor's algorithm, developed by MIT computer scientist Peter Shor in 1994. As Dr. Xie explained, “Shor invented an algorithm to solve prime factors of an integer that can supposedly run on a quantum computer. This algorithm, if run on a large-scale mature quantum computer, can easily solve all these existing cryptosystems' mathematical formulation, which is a problem." The realization of this potential threat has spurred an increased focus on the development of post-quantum cryptography over the past decade. The goal is clear: "We want to have some sort of cryptosystem that is resistant to quantum computer attacks," says Dr. Xie. In 2016, the National Institute of Standards and Technology (NIST) began the process of standardizing post-quantum cryptography. In July 2022, NIST selected four algorithms to continue on to the standardization process, where they are currently being tested for safety and security against quantum computers. The standardization process for these new algorithms is intensive, and two of the candidates that were announced for testing have already been broken during the process. Scientists are in a race against time to increase the diversity of their algorithms and come up with alternate options for standardization. The urgency of this shift to post-quantum cryptography is underscored by recent government action. The White House released a national security memo in 2022 stating that the U.S. government must transition to quantum-resistant algorithms by 2035. This directive emphasizes the critical nature of post-quantum cryptography in maintaining not just personal but national security. Villanova’s Security and Cryptography Lab Once a new algorithm is selected by NIST, it will need to be embedded into various platforms that need to be secured—this is where Dr. Xie’s Security and Cryptography Lab comes in. This lab is actively conducting research into how the newly selected algorithm can be implemented in the most effective and resourceful way. The lab team is working on developing techniques for this new algorithm so that it can be embedded into many different types of platforms, including credit cards and fingerprint technology. However, there are significant challenges in this process. As Dr. Xie explains, "Different platforms have different constraints. A chip-based credit card, for example, has limited space for embedding new encryption systems. If the implementation technique is too large, it simply won’t work.” Another arising issue from this research is security. During the application of this new algorithm, there's a risk of information or security leakage, so Dr. Xie is always on the lookout for developing security issues that could cause problems down the road. The Future of Post-Quantum Cryptography The implications of PQC are widespread and extend far beyond academic research. As Dr. Xie points out, "All existing cryptosystems, as long as they have some sort of function—for example, signing in or entering a password for login—all of these systems are vulnerable to quantum attacks." This vulnerability affects everything from banking systems to small-scale security measures like fingerprint door locks. The scope of this transition is massive, requiring updates to encrypted systems across all sectors of technology. His goal is to ensure that these new cryptographic systems are flexible enough to be applied to everything from small devices like credit cards and drones to large-scale infrastructure like data centers and military equipment. Although researchers are hard at work now, the future of post-quantum cryptography is not without uncertainties. Dr. Xie raises an important question: "When quantum computers become available, will the algorithms we develop today be broken?" While the newly developed algorithms will theoretically be secure, vulnerabilities can emerge when implementing any kind of new security system. These potential vulnerabilities highlight the importance of conducting this research now so that the new algorithms can go through intensive testing prior to being implemented. Despite these challenges, Dr. Xie emphasizes the importance of being prepared for this new reality. "Society as a whole needs to be prepared with this kind of knowledge,” he says. “A new era is coming. With our current security systems, we need to have revolutionized change. On the other hand, we should not be panicked. We just need continued support to do more related research in this field.” More extensive research is required to ensure that our privacy is protected as we enter a new era of quantum computing, but labs like the Security and Cryptography Lab at Villanova are a step in the right direction. Although the “years to quantum” clock is ticking down, researchers like Dr. Xie are well on their way to ensuring that our digital infrastructure remains secure in the face of evolving technological threats.

Expert Q&A: Should We Permit AI to Determine Gender and Race from Resumes?

The banner ads on your browser, the route Google maps suggests for you, the song Spotify plays next: algorithms are inescapable in our daily lives. Some of us are already aware of the mechanisms behind a targeted ad or a dating profile that lights up our phone screen. However, few of us may actually stop to consider how this technology plays out in the hiring sector. As with any major technological advancement, it usually takes society (and legislation) a while to catch up and adjust for unintended consequences. Ultimately, algorithms are powerful tools. Like any tool, they have the potential for societal benefit or harm, depending on how they’re wielded. Here to weigh in on the matter is Assistant Professor of Information Systems & Operations Management Prasanna Parasurama, who recently joined Emory Goizueta Business School’s faculty in fall of 2023. This interview has been edited for clarity. Describe your research interests in six words. Six words…that’s difficult to do on the spot. How about “the impact of AI and other digital technologies on hiring.” Is that condensed enough? That works! What first interested you in the intersection of AI and hiring practices? Before I did my PhD, I was working as a data scientist in the HR analytics space at a start-up company. That is where my interest in the topic began. But this was a long time ago. People hadn’t started talking much about AI, or algorithmic hiring. The conversation around algorithmic bias and algorithmic fairness picked up steam in the second or third year of my PhD. That had a strong influence on my dissertation focus. And naturally, one of the contexts in which both these matters have large repercussions is in the hiring space. What demographics does your research focus on (gender identity, race/ethnicity, socioeconomic status, all of the above)? Do you focus on a particular job sector? My research mostly looks at gender and race for two main reasons. First, prior research has typically looked at race and gender, which gives us a better foundation to build on. Second, it’s much easier to measure gender and race based on the data that we have available—from resumes, from hiring data, like what we collect from the Equal Employment Opportunity Commission. They typically collect data on gender and race, and our research requires those really large data sets to draw patterns. They don’t ask for socioeconomic status or have an easy way to quantify that information. That’s not to say those are less important factors, or that no one is looking at them. One of the papers you’re working on examines resumes written by self-identified men and women. It looks at how their resumes differ, and how that influenced their likelihood of being contacted for an interview. So in this paper, we’re essentially looking at how men and women write their resumes differently and if that impacts hiring outcomes. Take resume screening algorithms, for example. One proposed way to reduce bias in these screening algorithms is to remove names from resumes to blind the applicant’s gender to the algorithm. But just removing names does very little, because there are so many other things that serve as proxies to someone’s gender. While our research is focused on people applying to jobs in the tech sector, this is true across occupations. "We find it’s easy to train an algorithm to accurately predict gender, even with names redacted." Prasanna Parasurama What are some of those gendered “tells” on a resume? People write down hobbies and extracurricular activities, and some of those are very gendered. Dancing and ballet tend to denote female applicants; you’re more likely to see something like wrestling for male applicants. Beyond hobbies, which is sort of obvious, is just how people write things, or the language they use. Female applicants tend to use a lot more affective words. Men, on the other hand, use more of what we call agentic words. Can you explain that a little more? In social psychology, social role theory argues that men are stereotyped to be more agentic, whereas women are stereotyped to be more communal, and that their communication styles reflect this. There’s essentially a list of agentic words that researchers have come up with that men use a lot more than women. And women are more likely to use affective words, like “warmly” or “closely,” which have to do with emotions or attitudes.  These communication differences between men and women have been demonstrated in social sciences before, which has helped inform our work. But we’re not just relying on social science tools—our conclusions are driven by our own data. If a word is able to predict that an applicant’s resume belongs to a female versus male applicant, then we assign different weights, depending on how accurately it can predict that. So we’re not just operating on theories. Were there any gendered patterns that surprised you? If you were to assign masculinity and femininity to particular words, an algorithm would likely assign “married” to be a feminine term in most contexts. But in this particular case, it’s actually more associated with men. Men are much more likely to use it in resumes, because it signals something different to society than when women use it. "One of the most predictive terms for men was references to parenthood. It’s much easier for men to reference kids than for women to reveal information about their household status. Women face a penalty where men receive a boost." Prasanna Parasurama Studies show that people perceive fathers as being more responsible employees, whereas mothers are regarded as less reliable in the workplace. We haven’t studied this, but I would speculate that if you go on a platform like LinkedIn, men are more likely to disclose details about fatherhood, marriage, and kids than women are. There were some other tidbits that I didn’t see coming, like the fact that women are much less likely to put their addresses on their resume. Can AI predict race from a resume as easily as it can predict gender? There’s surprisingly very little we know on that front. From existing literature outside of algorithmic literature, we know differences exist in terms of race, not just on the employer side, where there might be bias, but we also on the worker side. People of different races search for jobs differently. The question is, how do we take this into account in the algorithm? From a technical standpoint, it should be feasible to do the same thing we do with gender, but it just becomes a little bit harder to predict race in practice. The cues are so variable. Gender is also more universal – no matter where you live, there are probably men and women and people who identify as in between or other. Whereas the concept of race can be very specific in different geographic regions. Racial identities in America are very different from racial identities in India, for instance. And in a place like India, religion matters a lot more than it does in the United States. So this conversation around algorithms and bias will look different across the globe. Beyond screening resumes, how does AI impact people’s access to job opportunities? A lot of hiring platforms and labor market intermediaries such as LinkedIn use AI. Their task is to match workers to these different jobs. There’s so many jobs and so many workers. No one can manually go through each one. So they have to train algorithms based on existing behavior and existing design decisions on the platform to recommend applicants to particular jobs and vice versa. When we talk about algorithmic hiring, it’s not just hiring per se, but spaces like these which dictate what opportunities you’re exposed to. It has a huge impact on who ends up with what job. What impact do you want your research to have in the real world? Do you think that we actually should use algorithms to figure out gender or race? Is it even possible to blind AI to gender or race? Algorithms are here to stay, for better or worse. We need them. When we think about algorithmic hiring, I think people picture an actual robot deciding who to hire. That’s not the case. Algorithms are typically only taking the space of the initial part of hiring. "I think overall, algorithms make our lives better. They can recommend a job to you based on more sophisticated factors than when the job was chronologically posted. There’s also no reason to believe that a human will be less biased than an algorithm." Prasanna Parasurama I think the consensus is that we can’t blind the algorithm to gender or other factors. Instead, we do have to take people’s demographics into account and monitor outcomes to correct for any sort of demonstrable bias. LinkedIn, for example, does a fairly good job publishing research on how they train their algorithms. It’s better to address the problem head on, to take demographic factors into account upfront and make sure that there aren’t drastic differences in outcomes between different demographics. What advice would you give to hopeful job candidates navigating these systems? Years of research have shown that going through a connection or a referral is by far the best way to increase your odds of getting an interview—by a factor of literally 200 to 300 percent. Hiring is still a very personal thing. People typically trust people they know. Prasanna Parasurama is an Assistant Professor of Information Systems & Operations Management at Emory University’s Goizueta Business School. Prasanna’s research areas include algorithmic hiring, algorithmic bias and fairness, and human-AI interaction. His research leverages a wide array of quantitative methods including econometrics, machine learning, and natural language processing. Prasanna is available to talk about this important and developing topic - simply click on his icon now to arrange an interview today.

Expert Insight: Training Innovative AI to Provide Expert Guidance on Prescription Medications

A new wave of medications meant to treat Type II diabetes is grabbing headlines around the world for their ability to help people lose a significant amount of weight. They are called GLP-1 receptor agonists. By mimicking a glucagon-like peptide (GLP) naturally released by the body during digestion, they not only lower blood sugar but also slow digestion and increase the sense of fullness after eating. The two big names in GLP-1 agonists are Ozempic and Wegovy, and both are a form of semaglutide. Another medication, tirzepatide, is sold as Mounjaro and Zepbound. It is also a glucose-dependent insulinotropic polypeptide (GIP) agonist as well as GLP-1. Physicians have been prescribing semaglutide and tirzepatide with increasing frequency. However, both medications come with a host of side effects, including nausea and stomach pain, and are not suitable for every patient. Many clinics and physicians do not have immediate access to expert second opinions, as do the physicians at Emory Healthcare. Creating a Digital Twin That lack of an expert is one of the reasons Karl Kuhnert, professor in the practice of organization and management at Emory University’s Goizueta Business School, is using artificial intelligence to capture the expertise of physicians like Caroline Collins MD through the Tacit Object Modeler™, or TOM. By using TOM, developed by Merlynn Intelligence Technologies, Kuhnert and Collins can create her “decision-making digital twin.” This allows Collins to reveal her expertise as a primary care physician with Emory Healthcare and an Assistant Professor at Emory School of Medicine, where she has been leading the field in integrating lifestyle medicine into clinical practices and education. Traditional AI, like ChatGPT, uses massive amount of data points to predict outcomes using what’s known as explicit knowledge. But it isn’t necessarily learning as it goes. According to Kuhnert, TOM has been designed to learn how an expert, like Collins, decides whether or not to prescribe a drug like semaglutide to a patient. Wisdom or tacit knowledge is intuitive and rooted in experience and context. It is hard to communicate, and usually resides only in the expert’s mind. TOM’s ability to “peek into the expert’s mind makes it a compelling technology for accessing wisdom.” “Objective or explicit knowledge is known and can be shared with others,” says Kuhnert. "For example, ChatGPT uses explicit knowledge in its answers. It’s not creating something new. It may be new to you as you read it, but somebody, somewhere, before you, has created it. It’s understood as coming from some source." Karl Kuhnert “Tacit knowledge is subjective wisdom. Experts offer this, and we use their tacit know-how, their implicit knowledge, to make their decisions. If it were objective, everyone could do it. This is why we hire experts: They see things and know things others don’t; they see around corners.” Mimicking the Mind of a Medical Expert Teaching TOM to see around the corners requires Collins to work with the AI over the course of a few days. “Essentially what I do is I sit down with, in this case, a physician, and ask them, ‘What are thinking about when you make this decision?'” says Kuhnert. “The layperson might think that there are hundreds of variables in making a medical decision like this. With the expert’s tacit knowledge and experience, it is usually between seven and twelve variables. They decide based on these critical variables,” he says. "These experts have so much experience, they can cut away a lot of the noise around a decision and get right to the point and ask, ‘What am I looking at?’" Karl Kuhnert As TOM learns, it presents Collins with more and different scenarios for prescribing semaglutide. As she makes decisions, it remembers the variables present during her decision-making process. “Obviously, some variables are going to be more important than other variables. Certain combinations are going to be challenging,” says Collins. “Sometimes there are going to be some variables where I think, yes, this patient needs a GLP-1. Then there may be some variables where I think, no, this person really doesn’t need that. And which ones are going to win out? That’s really where TOM is valuable. It can say, okay, when in these difficult circumstances where there are conflicting variables, which one will ultimately be most important in making that decision?” The Process: Trusting AI After working with TOM for several hours, Collins will have reacted to enough scenarios for TOM to learn to make her decision. The Twin will need to demonstrate that it can replicate her decision-making with acceptable accuracy—high 90s to 100 percent. Once there, Collins’ Twin is ready to use. “I think it’s important to have concordance between what I would say in a situation and then what my digital twin would say in a situation because that’s our ultimate goal is to have an AI algorithm that can duplicate what my recommendation would be given these circumstances for a patient,” Collins says. “So, someone, whether that be an insurance company, or a patient themselves or another provider, would be able to consult TOM, and in essence, me, and say, in this scenario, would you prescribe a GLP-1 or not given this specific patient’s situation?” The patient’s current health and family history are critical when deciding whether or not to prescribe semaglutide. For example, according to Novo Nordisk, the makers of Ozempic, the drug should not be prescribed to patients with a history of problems with the pancreas or kidneys or with a family history of thyroid cancer. Those are just the start of a list of reasons why a patient may or may not be a good candidate for the medication. Kuhnert says, “What we’re learning is that there are so many primary care physicians right now that if you come in with a BMI over 25 and are prediabetic, you’re going to get (a prescription). But there’s much more data around this to suggest that there are people who are health marginalized, and they can’t do this. They should not have this (medication). It’s got to be distributed to people who can tolerate it and are safe.” Accessing the Digital Twin on TOM Collins’s digital twin could be available via something as easy to access as an iPhone app. “Part of my job is to provide the latest information to primary care physicians. Now, I can do this in a way that is very powerful for primary care physicians to go on their phones and put it in. It’s pretty remarkable, according to Colllins.” It is also transparent and importantly sourced information. Any physician using a digital twin created with TOM will know exactly whose expertise they are accessing, so anyone asking for a second opinion from Colllins will know they are using an expert physician from Emory University. In addition to patient safety, there are a number of ways TOM can be useful to the healthcare industry when prescribing medications like semaglutide. This includes interfacing with insurance companies and the prior approval process, often lengthy and handled by non-physician staff. “Why is a non-expert at an insurance company determining whether a patient needs a medication or not? Would it be better to have an expert?” says Collins. “I’m an expert in internal medicine and lifestyle medicine. So, I help people not only lose weight, but also help people change their behaviors to optimize their health. My take on GLP-1 medications is not that everyone needs them, it’s that they need to be utilized in a meaningful way, so patients will get benefit, given risks and benefits for these medications.” The Power of a Second Opinion Getting second, and sometimes third, opinions is a common practice among physicians and patients both. When a patient presents symptoms to their primary care physician, that physician may have studied the possible disease in school but isn’t necessarily an expert. In a community like Emory Healthcare, the experts are readily available, like Collins. She often serves as a second opinion for her colleagues and others around the country. “What we’re providing folks is more of a second opinion. Because we want this actually to work alongside someone, you can look at this opinion that this expert gave, and now, based on sourced information, you can choose. This person may be one of the best in the country, if not the world, in making this decision. But we’re not replacing people here. We’re not dislocating people with this technology. We need people. We need today’s and tomorrow’s experts as well,” according to Kuhnert. But also, you now have the ability to take an Emory physician’s diagnosing capabilities to physicians in rural areas and make use of this information, this knowledge, this decision, and how they make this decision. We have people here that could really help these small hospitals across the country. Caroline Collin MD Rural Americans have significant health disparities when compared to those living in urban centers. They are more likely to die from heart disease, cancer, injury, chronic respiratory disease, and stroke. Rural areas are finding primary care physicians in short supply, and patients in rural areas are 64 percent less likely to have access to medical specialists for needed referrals. Smaller communities might not have immediate access to experts like a rheumatologist, for example. In addition, patients in more rural areas might not have the means of transportation to get to a specialist, nor have the financial means to pay for specialized visits for a diagnosis. Collins posits that internal medicine generalists might suspect a diagnosis but want to confirm before prescribing a course of treatment. “If I have a patient for whom I am trying to answer a specific question, ‘Does this patient have lupus?’, for instance. I’m not going to be able to diagnose this person with lupus. I can suspect it, but I’m going to ask a rheumatologist. Let’s say I’m in a community where unfortunately, we don’t have a rheumatologist. The patient can’t see a rheumatologist. That’s a real scenario that’s happening in the United States right now. But now I can ask the digital twin acting as a rheumatologist, given these variables, ‘Does this patient have lupus?’ And the digital twin could give me a second opinion.” Sometimes, those experts are incredibly busy and might not have the physical availability for a full consult. In this case, someone could use TOM to create the digital twin of that expert. This allows them to give advice and second opinions to a wider range of fellow physicians. As Kuhnert says, TOM is not designed or intended to be a substitute for a physician. It should only work alongside one. Collins agreed, saying, “This doesn’t take the place of a provider in actual clinical decision-making. That’s where I think someone could use it inappropriately and could get patients into trouble. You still have to have a person there with clinical decision-making capacity to take on additional variables that TOM can’t yet do. And so that’s why it’s a second opinion.” “We’re not there yet in AI says Collins. We have to be really careful about having AI make actual medical decisions for people without someone there to say, ‘Wait a minute, does this make sense?’” AI Implications in the Classroom and Beyond Because organizations use TOM to create digital twins of their experts, the public cannot use the twins to shop for willing doctors. “We don’t want gaming the system,” says Collins. “We don’t want doctor shopping. What we want is a person there who can utilize AI in a meaningful way – not in a dangerous way. I think we’ll eventually get there where we can have AI making clinical decisions. But I don’t think I’d feel comfortable with that yet.” The implications of using decision-making digital twins in healthcare reach far beyond a second opinion for prescription drugs. Kuhnert sees it as an integral part of the future of medical school classrooms at Emory. In the past, teaching case studies have come from books, journals, and papers. Now, they could come alive in the classroom with AI simulation programs like TOM. "I think this would be great for teaching residents. Imagine that we could create a simulation and put this in a classroom, have (the students) do the simulation, and then have the physician come in and talk about how she makes her decisions." Karl Kuhnert “And then these residents could take this decision, and now it’s theirs. They can keep it with them. It would be awesome to have a library of critical health decisions made in Emory hospitals,” Kuhnert says. Collins agreed. “We do a lot of case teaching in the medical school. I teach both residents and medical students at Emory School of Medicine. This would be a really great tool to say, okay, given these set of circumstances, what decision would you make for this patient? Then, you could see what the expert’s decision would have been. That could be a great way to see if you are actually in lockstep with the decision-making process that you’re supposed to be learning.” Kuhnert sees decision-making twins moving beyond the healthcare system and into other arenas like the courtroom, public safety, and financial industries and has been working with other experts to digitize their knowledge in those fields. "The way to think about this is: say there is a subjective decision that gets made that has significant ramifications for that company and maybe for the community. What would it mean if I could digitize experts and make it available to other people who need an expert or an expert’s decision-making?" Karl Kuhnert “You think about how many people aren’t available. Maybe you have a physician who’s not available. You have executives who are not available. Often expertise resides in the minds of just a few people in an organization,” says Kuhnert. “Pursuing the use of technologies like TOM takes the concept of the digital human expert from simple task automation to subjective human decision-making support and will expand the idea of a digital expert into something beyond our current capabilities,” Kuhnert says. “I wanted to show that we could digitize very subjective decisions in such areas as ethical and clinical decision-making. In the near future, we will all learn from the wisdom codified in decision-making digital twins. Why not learn from the best? There is a lot of good work to do.” Karl Kuhnert is a Professor in the Practice of Organization & Management and Associate Professor of Psychiatry, School of Medicine and Senior Faculty Fellow of the Emory Ethics Center. If you're looking to connect with Karl to know more - simply click on his icon now to arrange a time to talk today.