Question 1

Can we conclude that trust causes higher vaccination rates?

Accepted Answer

_Correlation found, but causation works both ways with trust._

While we have shown the impact of trust on covid vaccination, correlations do not indicate causation. Trust is a two-way street. If governments do an excellent job vaccinating their population, it might alter people's overall trust in their institutions. Appropriate, transparent responses will improve the ability of countries to deal with the effects of the COVID pandemic in a virtuous cycle, suggesting the relationship flows both ways.

— [Get Vaccinated? How Trust In Institutions Determines COVID Vaccination Rates In The EU](https://www.forbes.com/sites/lutzfinger/2021/05/06/get-vaccinated-how-trust-into-the-institutions-determines-vaccination-rates-in-the-eu/) · _Forbes_

Question 2

Did you find other factors that influenced vaccination rates besides trust?

Accepted Answer

_Other factors tested but trust stood out statistically._

There are other factors besides trust that might influence vaccination rates, including accessibility and vulnerability. We analyzed the old-age dependency ratio and health care expenditure per inhabitant but did not find statistically significant dependencies. This suggests that while these factors could theoretically matter, in our analysis trust emerged as the key differentiator when controlling for these other variables across EU member states.

— [Get Vaccinated? How Trust In Institutions Determines COVID Vaccination Rates In The EU](https://www.forbes.com/sites/lutzfinger/2021/05/06/get-vaccinated-how-trust-into-the-institutions-determines-vaccination-rates-in-the-eu/) · _Forbes_

Question 3

How did racial bias in a healthcare algorithm affect Black patients according to the Science study?

Accepted Answer

_Training data bias reduced Black patient care identification by half._

A study published in Science revealed that a healthcare algorithm showed racial bias in patient care. Specifically, Black patients who were assigned the same risk level as White patients were actually sicker. This bias had serious consequences, reducing the number of Black patients identified for extra care by more than half. The study points to initial training data as often being the culprit for such algorithmic bias, demonstrating how AI systems can perpetuate inequities in critical areas like healthcare.

— [President Biden Is Man, Woman And 40 Years Old - Why We Need Algorithmic Transparency](https://www.forbes.com/sites/lutzfinger/2021/02/04/president-biden-is-man-woman-and-40-years-old---why-we-need-algorithmic-transparency/) · _Forbes_

Question 4

How did you isolate trust as a factor in COVID vaccination rates?

Accepted Answer

_Using the EU to study trust's role in vaccination._

We looked towards the EU because the distribution of vaccines to Member States happened simultaneously and under the same conditions. But not all member states show the same trust, allowing us to isolate trust as a variable. We used the European Social Survey, a bi-annually collected survey of thousands of EU citizens from 29 countries, to measure trust levels. We then correlated that data with vaccination data from the European Centre for Disease Prevention and Control.

— [Get Vaccinated? How Trust In Institutions Determines COVID Vaccination Rates In The EU](https://www.forbes.com/sites/lutzfinger/2021/05/06/get-vaccinated-how-trust-into-the-institutions-determines-vaccination-rates-in-the-eu/) · _Forbes_

Question 5

How do existing laws already address AI concerns without new AI-specific regulation?

Accepted Answer

_Current laws already cover many AI use cases effectively._

Existing frameworks already address biases and ensure fairness without reinventing the wheel. If an AI-driven healthcare app misdiagnoses a patient, current product liability laws hold companies accountable. AI tools used in hiring must not introduce bias, and anti-discrimination laws like the Civil Rights Act already govern hiring practices and can be applied to AI-driven decisions. Instead of focusing on the technology itself, regulations should target how AI is used through these established legal frameworks.

— [Emmanuel Macron On AI: We Are Regulating Ourselves Out Of The Market](https://www.forbes.com/sites/lutzfinger/2024/10/22/emmanuel-macron-is-right-about-ai-over-regulatingi-have-no-doubt/) · _Forbes_

Question 6

How do you measure quality in healthcare data?

Accepted Answer

_Quality metrics for healthcare ML._

> Knowing quality is therefore of utmost importance. Three broad measurement areas in today's healthcare: (1) Metrics about quality of care — related to treatment effectiveness and patient satisfaction. (2) Metrics about need and utilization of care — gauging health requirements of a population and how often one uses what type of service. (3) Metrics about value-based healthcare — they measure disease prevention rather than disease…

— [Healthcare And Data - How To Measure Quality](https://www.forbes.com/sites/lutzfinger/2023/06/27/healthcare-and-datahow-to-measure-quality/) · _Forbes_

Question 7

How does AI change healthcare?

Accepted Answer

_AI applications, value-based care, regulation._

> I just got back from the JP Morgan conference. A few learnings that apply to any industry, not just healthcare:Humans + AI will change the industry. Not AI alone.Cash flow matters. Not AI hype.The real AI revolution is operational.One of the most hyperbolic promises in healthcare in the last month was the claim that: "AI is going to design our drugs and will replace researchers." In my view, this is not true.

— [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/) · _Forbes_

Question 8

How does AI find patterns in data that humans miss, like in healthcare?

Accepted Answer

_AI detects invisible patterns in healthcare data to prevent emergencies._

AI takes data and finds tiny clues that you and I might not see. At Google Health, we worked with electronic health records to predict whether somebody would end up in an emergency room six months before it happened. The AI sees the writing on the wall in the data. Roughly one third of people in hospitals shouldn't be there if we would have just known. The computer gets trained on data and figures out those tiny clues to predict preventable hospitalizations.

— [AI, Clones, Deep Fakes, and Neural Networks](https://www.lutzfinger.com/transcript/2024-09-12-ai-clones-deep-fakes-and-neural-networks/) · _The Keynote on AI_

Question 9

How does Earli's data flywheel work in practice?

Accepted Answer

_Oracle predicts, wet labs test, winners feed back in_

Earli's proprietary prediction model called Oracle proposes candidate genetic switches. A wet lab then tests up to 250,000 of them in a single batch, each tagged with a DNA barcode like a product at a checkout. The rare winners, the true outliers, get read out and fed back into the model. Every turn of the wheel makes the system smarter. That loop is data to model to candidate to experiment, and back to data. The company that builds the machine to generate proprietary data and the loop to learn from it wins.

— [DNA Is Becoming Programmable. Curing Cancer With AI.](https://www.forbes.com/sites/lutzfinger/2026/05/25/dna-is-becoming-programmable-curing-cancer-with-ai/) · _Forbes_

Question 10

How does Marpai Connect use AI to overcome the small numbers problem?

Accepted Answer

_Pooling clients and predictive AI create scale for small employers._

Marpai Connect addresses the small numbers challenge by pooling all self-insured clients and anticipating an overall reduction in aggregate. We use various approaches from simple time series modeling to advanced deep learning models like Neural Prophet to predict risk based on longitudinal claims records. This pooling strategy allows us to achieve the statistical power that individual small employers lack, while still delivering personalized care through AI matchmaking that pairs each user with the most suitable value-based care partner.

— [Changing Healthcare - A Platform Approach Using Data & AI](https://www.forbes.com/sites/lutzfinger/2023/07/05/changing-healthcarea-platform-approach-using-data--ai/) · _Forbes_

Question 11

How does multimodal AI improve the healthcare experience for doctors dealing with overwhelming patient data?

Accepted Answer

_Summarizing mountains of patient data to surface what truly matters._

In healthcare, patients often overwhelm doctors with massive amounts of data. For example, a patient with atrial fibrillation might bring five years of sleep data from their smartwatch, or a cancer patient could arrive with a 20-pound stack of medical records. What's needed is an AI that can summarize and highlight the key points. Multimodal AI handles diverse data sources like text, images, audio, video, and time-series data simultaneously, offering a richer, more comprehensive understanding to help doctors focus on what matters most.

— [Multimodal AI In 2025: From Healthcare To eCommerce And Beyond](https://www.forbes.com/sites/lutzfinger/2025/01/06/multimodal-ai-in-2025-from-healthcare-to-ecommerce-and-beyond/) · _Forbes_

Question 12

How does Neuralens measure brain health non-invasively compared to current methods?

Accepted Answer

_Replacing skull drilling with light-based sensors for brain monitoring._

Currently, doctors literally drill a burr hole through your skull with a household drill and insert sensors through brain tissue to measure oxygen saturation, blood flow, and intracranial pressure. This invasive surgery is only done on 200,000 to 300,000 patients yearly, though over 10 million neurocritical care patients could benefit. Neuralens uses advanced methods of measuring how light interferes with brain tissue to quantify blood flow, blood volume, and intracranial pressure non-invasively. We can stick sensors on the head or shine a laser from 30 centimeters away to make these measurements.

— [Founder Coaching: Tips for your AI Journey](https://www.lutzfinger.com/transcript/2024-07-26-founder-coaching-tips-for-your-ai-journey/) · _The Edge_

Question 13

How does value-based care differ from traditional fee-for-service payment?

Accepted Answer

_Payment tied to outcomes, not just services rendered._

Value-based care shifts risk to providers by changing how they're paid. Since 2008, the Centers for Medicare & Medicaid Services has been moving away from fee-for-service, where providers are paid for services rendered. Instead, under value-based care, CMS only pays if health outcomes actually improve. Today all of Medicare and most of Medicaid are expected to adopt a VBC model by 2030. This approach aims to focus on keeping people healthy rather than just treating sickness.

— [Changing Healthcare - A Platform Approach Using Data & AI](https://www.forbes.com/sites/lutzfinger/2023/07/05/changing-healthcarea-platform-approach-using-data--ai/) · _Forbes_

Question 14

How does Zenith OS reduce costs in neuropsychiatry treatment?

Accepted Answer

_AI cuts neuropsychiatry costs by reducing required therapist headcount per session._

Neuropsychiatry treatments like ketamine and psilocybin for PTSD, depression, and anxiety are 30 to 40 percent more effective than SSRIs but remain inaccessible due to high human capital costs. Currently, two therapists must be present during treatment because patients are in a vulnerable state and there are sexual harassment concerns. Each therapist charges $100 to $150 per hour. Zenith OS uses AI to augment administrative tasks and help with patient recommendations, allowing clinics to operate with one therapist instead of two, significantly reducing costs and expanding access to these groundbreaking treatments.

— [Founder Coaching: Tips for your AI Journey](https://www.lutzfinger.com/transcript/2024-07-26-founder-coaching-tips-for-your-ai-journey/) · _The Edge_

Question 15

How is the key skill for doctors changing because of AI?

Accepted Answer

_Medical skills evolved from memorization to critical thinking._

During medical training, memorization was key. Then, as the internet made digital information easily accessible, effective summarization became the key skill. With AI, transformer models can summarize better than humans. The key skill now is critical thinking. But those advancements will free doctors to focus on human connection, improving medicine overall. This evolution shows how each technological wave shifts what matters most in medical practice, from memorizing facts to thinking critically and connecting with patients.

— [Is AI Replacing Us — Good News For Knowledge Workers](https://www.forbes.com/sites/lutzfinger/2025/01/22/is-ai-replacing-us-good-news-for-knowledge-workers/) · _Forbes_

Question 16

How is Value-Based Care different from traditional fee-for-service healthcare, and how fast is it growing?

Accepted Answer

_VBC shifts to fee for value, reaching 42% by 2030._

Value-Based Care, introduced in 2008, aims to shift risk from the payer to the provider. It incentivizes providers to deliver value, or fee for value, rather than just services under the traditional fee for service model. The VBC industry has been growing constantly, with the US expected to have 42% of national health expenditures in some form of risk sharing arrangement by 2030. This growth is significant because VBC providers are actively focused on reducing risk, which aligns perfectly with the needs of self-insured companies and the capabilities of AI predictions.

— [Three Trends Of Transformation: How AI & Value-Based Care (VBC) Will Drive Transformation Of Self-Insured Healthcare](https://www.forbes.com/sites/lutzfinger/2023/05/01/three-trends-of-transformation-how-ai--value-based-care-vbc-will-drive-transformation-of-self-insured-healthcare/) · _Forbes_

Question 17

How much capital was invested in the digital health startup landscape in 2021?

Accepted Answer

_Venture capitalists invested $29.1B in digital health startups in 2021._

In 2021 alone, the digital health startup landscape saw $29.1B of capital being invested by Venture Capitalists, and many of these startups are adding LLMs to their products. The landscape is exploding. At my day job as President of Product and Technology at Marpai Inc., our core aim is to help self-insured companies not only administer their claims but also help members find the best provider for their needs.

— [Large Language Models & AI In Healthcare](https://www.forbes.com/sites/lutzfinger/2023/03/27/large-language-models--ai-in-healthcare/) · _Forbes_

Question 18

How will Large Language Models impact healthcare according to Lutz?

Accepted Answer

_LLMs will save lives by transforming patient engagement and interaction._

Large Language Models will improve the last mile with patients, which is huge because engaging patients improves outcomes. In other words, LLMs will save lives. The major impact in healthcare is that we're about to change the way human-AI interaction works. LLMs allow a new way to interact with technology, enabling personalized messages, AI-powered chatbots that send tailored advice based on personal preferences, and knowledge base systems that explain diagnoses and educate patients on pathways.

— [Large Language Models & AI In Healthcare](https://www.forbes.com/sites/lutzfinger/2023/03/27/large-language-models--ai-in-healthcare/) · _Forbes_

Question 19

What are JEPA and world models, and how do they differ from token prediction?

Accepted Answer

_A research direction aiming for conceptual understanding beyond autocomplete._

JEPA stands for Joint-Embedding Predictive Architecture. Instead of predicting the next token, it aims to predict the latent embedding context. The goal is to understand predictable structure by predicting mechanisms rather than reconstructing raw pixels or words. It tries to learn more grounded representations like concepts, invariants, and causal structure, all in embedding space where the model keeps its knowledge. This is the direction Yann LeCun has been calling out, because token prediction doesn't equal conceptual understanding. However, it's still research and not yet a proven industrial foundation for mechanistic biology or end-to-end drug design.

— [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/) · _Forbes_

Question 20

What are some examples of how healthcare providers are using LLMs with patients?

Accepted Answer

_Chatbots and knowledge systems deliver personalized, educational patient engagement._

Healthcare providers are using AI-powered chatbots from Value-Based Care providers that send tailored advice based on a user's personal preferences. They're also using knowledge base systems that explain a potential diagnosis and educate patients on pathways. By combining AI technology with traditional methods such as care management programs or wellness initiatives, healthcare providers have an enhanced opportunity for achieving better outcomes at lower costs while providing personalized, human-centric care.

— [Large Language Models & AI In Healthcare](https://www.forbes.com/sites/lutzfinger/2023/03/27/large-language-models--ai-in-healthcare/) · _Forbes_

Question 21

What are the three broad measurement areas in today's healthcare?

Accepted Answer

_Quality, utilization, and value-based prevention are the three areas._

There are three broad measurement areas. First, metrics about quality of care, which relate to treatment effectiveness and patient satisfaction. Second, metrics about need and utilization of care, gauging health requirements of a population and how often one uses what type of service. Third, metrics about value-based healthcare, which measure disease prevention rather than disease treatment, measuring actions taken to prevent or reverse illnesses instead of just treating them.

— [Healthcare And Data - How To Measure Quality](https://www.forbes.com/sites/lutzfinger/2023/06/27/healthcare-and-datahow-to-measure-quality/) · _Forbes_

Question 22

What are the two main challenges value-based care aggregators must overcome?

Accepted Answer

_Small patient pools and personalization create unique VBC obstacles._

Value-based care aggregators face two key challenges. First is the issue of small numbers. Self-insured companies can't offer a large patient pool, only their own employees. VBC providers willing to risk fees with 66 million Medicare patients may see their approaches work on average, but with only 100 employees, those averages might not hold. Second is personalizing healthcare. While treatment is standardized, behavioral changes are unique to each individual. Weight loss alone has over 77,000 books, showing no one-size-fits-all solution exists.

— [Changing Healthcare - A Platform Approach Using Data & AI](https://www.forbes.com/sites/lutzfinger/2023/07/05/changing-healthcarea-platform-approach-using-data--ai/) · _Forbes_

Question 23

What did the data show about high trust versus low trust countries?

Accepted Answer

_High trust countries vaccinate more, and the gap widens._

We plotted a historical trend of vaccine uptake delineated into high and low trust countries. The plot shows clearly that high trust countries have a higher mean vaccine uptake over low trust countries, with the gap widening over time. We regressed vaccine uptake as of April 18, 2021 with trust in EU institutions. A positive coefficient for trust in EU institutions along with a p-value of less than 0.05 clearly indicates a statistically positive relationship.

— [Get Vaccinated? How Trust In Institutions Determines COVID Vaccination Rates In The EU](https://www.forbes.com/sites/lutzfinger/2021/05/06/get-vaccinated-how-trust-into-the-institutions-determines-vaccination-rates-in-the-eu/) · _Forbes_

Question 24

What does AI actually contribute to drug development today, according to what you saw at JP Morgan?

Accepted Answer

_AI accelerates iteration and improves selection, but humans still validate._

AI gives us better priors, better search, better filters, and faster loops. It narrows the hypothesis space, surfaces weak signals earlier, improves decision support, and accelerates the cycle of data to model to candidate to experiment and back to data. The headline-grabbing examples from Exscientia and Insilico Medicine don't mean AI replaces scientists. They fit this pattern: AI helps prioritize, propose, and optimize, while humans validate in wet labs and the clinic. The real impact shows up primarily as faster iteration and better selection under uncertainty, not autonomous discovery. It makes teams materially more effective.

— [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/) · _Forbes_

Question 25

What does Marpai Connect measure in value-based care, and what do you de-emphasize?

Accepted Answer

_Clinical markers and outcomes matter more than engagement metrics._

At Marpai Connect we use bio markers that are clinically proven, like HBA1C measurements for diabetes. We also use other measured outcomes such as pain reduction scores and functional health improvement scores. We include cost, which is easy for a third-party administrator to measure via claims, but cost alone is not sufficient. What we de-emphasize: engagement metrics, because they often lack evidence that they work. Our approach is working. 40% of the commercial market wants to get access to value-based care.

— [Healthcare And Data - How To Measure Quality](https://www.forbes.com/sites/lutzfinger/2023/06/27/healthcare-and-datahow-to-measure-quality/) · _Forbes_

Question 26

What does physical AI actually mean, and why does it matter?

Accepted Answer

_AI is only powerful when trained on real-world physical data_

Physical AI is the idea that models will finally step out of the chatbot and into the real world of robots, factories, and labs. Cyriac Roeding said physical AI is only interesting when you train it on physical-world data. A world model is only as good as the physical reality it learns from. In biology, that reality is generated by pipettes and a vivarium full of mice, not scraped from the internet. The loop fed by real-world data nobody else has is owned.

— [DNA Is Becoming Programmable. Curing Cancer With AI.](https://www.forbes.com/sites/lutzfinger/2026/05/25/dna-is-becoming-programmable-curing-cancer-with-ai/) · _Forbes_

Question 27

What does responsible AI development look like in healthcare, according to Earli's approach?

Accepted Answer

_Power demands aim, safety studies, and human accountability_

AI is like suddenly having a bazooka in your toolbox. You still have to aim, and you still have to pull the trigger. The tool does not tell you which problem to solve. Earli's genetic switches combine human judgment and software, because pure brute force would still be running a hundred years from now. Earli is moving toward Phase 1 human trials through monkey safety studies, manufacturing review, and the FDA, because in healthcare seems right is not good enough. Safety first, real-world validation, and a human accountable for the outcome is the discipline every industry deploying AI will need.

— [DNA Is Becoming Programmable. Curing Cancer With AI.](https://www.forbes.com/sites/lutzfinger/2026/05/25/dna-is-becoming-programmable-curing-cancer-with-ai/) · _Forbes_

Question 28

What example shows how data use in one industry could be inappropriate in another?

Accepted Answer

_Healthcare data belongs with doctors, not insurance pricing algorithms._

In healthcare, we want doctors to use health data to diagnose our potential issues. But we do not expect our doctor to share health information with an insurance company that will then change the pricing of our life insurance. The healthcare industry is regulated to prevent this kind of misuse. However, the social media industry is not similarly regulated. This example illustrates why we should focus on what data is used for, not just who has it, and why regulation matters.

— [WhatsApp: We Should Discuss What Our Data Is Used For, Not Who Has It](https://www.forbes.com/sites/lutzfinger/2021/01/26/whatsapp-we-should-discuss-what-our-data-is-used-for-not-who-has-it/) · _Forbes_

Question 29

What is BioAI?

Accepted Answer

_AI applied to biology — new molecules, new medicines._

> AI, Roeding said, is like suddenly having a bazooka in your toolbox. That means though, you still have to aim, and you still have to pull the trigger.

— [DNA Is Becoming Programmable. Curing Cancer With AI.](https://www.forbes.com/sites/lutzfinger/2026/05/25/dna-is-becoming-programmable-curing-cancer-with-ai/) · _Forbes_

Question 30

What is OpenAI's stated ambition for AI agents according to this article?

Accepted Answer

_OpenAI aims to become the universal interface for all internet activity._

According to a recent OpenAI memo, their aim is to become the dominant interface for everything on the internet. Sam Altman's vision is clear: he wants to be the agent for healthcare, finance, e-commerce, and many other areas. Some founders have described this vision as wanting to forward integrate into their business or be Everyone's Agent, much like Amazon once wanted to be The Everything Store. OpenAI is extending the idea that Perplexity started with Shop Like a Pro to cover everything users do online.

— [AI Agents - The Internet Is Being Rewritten - Survey Users Vs Brands?](https://www.forbes.com/sites/lutzfinger/2025/07/24/ai-agentsthe-internet-is-being-rewrittensurvey-users-vs-brands/) · _Forbes_

Question 31

What is the core discovery mechanism behind multimodal AI, and how does it relate to Amazon's recommendation approach?

Accepted Answer

_Finding close connections across data types, like Amazon's recommendations scaled up._

Multimodal AI is more of a discovery tool. Different data modalities are stored by the AI, and once a new data point is input, the AI finds topics that are close. For example, inputting sleep data from a smartwatch alongside information about atrial fibrillation episodes might find indications of sleep apnea. This is the scaled-up version of what Amazon popularized: "people who shopped for this item also bought this item." In this case, it becomes "People with this type of sleep pattern have also been diagnosed with AFIB."

— [Multimodal AI In 2025: From Healthcare To eCommerce And Beyond](https://www.forbes.com/sites/lutzfinger/2025/01/06/multimodal-ai-in-2025-from-healthcare-to-ecommerce-and-beyond/) · _Forbes_

Question 32

What is the difference between LLMs and traditional machine learning models in healthcare?

Accepted Answer

_LLMs go beyond prediction to transform how humans interact with technology._

Traditional AI is used for tasks like recommending movies on Netflix or detecting spam. In healthcare, traditional models predicted disease deterioration or helped detect cancer. LLMs can be used for those same tasks formerly done by traditional models, and foundation models might have better predictive accuracy and work with multimodal data. However, the major difference is that LLMs change the way human-AI interaction works, allowing a new way to interact with technology rather than just making predictions.

— [Large Language Models & AI In Healthcare](https://www.forbes.com/sites/lutzfinger/2023/03/27/large-language-models--ai-in-healthcare/) · _Forbes_

Question 33

What is the potential financial impact of addressing Type 2 Diabetes in self-insured populations?

Accepted Answer

_Type 2 Diabetes costs are avoidable with right interventions._

Looking at Marpai's data, about 6% of members have Type 2 Diabetes, costing self-insured clients 35 million USD. The key insight is that Type 2 Diabetes can be reversed and these costs are avoidable. This represents a significant opportunity for self-insured companies. By using AI and data to match the best intervention to the best use case, we can maximize therapeutic success and improve ROI. At Marpai we evaluated over 60 different healthcare programs to partner with, specifically to address conditions like this where costs are preventable.

— [Three Trends Of Transformation: How AI & Value-Based Care (VBC) Will Drive Transformation Of Self-Insured Healthcare](https://www.forbes.com/sites/lutzfinger/2023/05/01/three-trends-of-transformation-how-ai--value-based-care-vbc-will-drive-transformation-of-self-insured-healthcare/) · _Forbes_

Question 34

What is value-based care and how does AI enable it?

Accepted Answer

_VBC framing and AI's role._

> Healthcare is on the brink of a seismic shift, and AI is its catalyst. Three converging trends are creating a perfect opportunity for change: (1) Foundational Models' growing ability to predict risks further into the future compared to traditional machine learning, (2) the rise of the self-insured healthcare industry motivated to address these risks, and (3) a surge in Value-Based Care (VBC) providers focused on risk reduction. These…

— [Three Trends Of Transformation: How AI & Value-Based Care (VBC) Will Drive Transformation Of Self-Insured Healthcare](https://www.forbes.com/sites/lutzfinger/2023/05/01/three-trends-of-transformation-how-ai--value-based-care-vbc-will-drive-transformation-of-self-insured-healthcare/) · _Forbes_

Question 35

What outcome metrics does Marpai Connect use to evaluate care effectiveness?

Accepted Answer

_Specific metrics track adherence, improvement, and patient participation._

My team at Marpai designed outcome metrics with our value-based care partners to evaluate the effectiveness of care. These include medication adherence, functional health improvement, flu vaccination rate, and patient engagement. We also built financial alignment into the platform, where providers should only receive payment if they effectively help reduce costs and improve health on average. For example, NuvoAir committed to a total cost of care reduction of 35% based on claims analysis with their virtual-first care model.

— [Changing Healthcare - A Platform Approach Using Data & AI](https://www.forbes.com/sites/lutzfinger/2023/07/05/changing-healthcarea-platform-approach-using-data--ai/) · _Forbes_

Question 36

What percentage of healthcare costs are linked to reversible or manageable diseases?

Accepted Answer

_Nearly half of healthcare spending could be avoided with better care._

At Marpai Inc., we discovered that 23% of all healthcare costs are linked to reversible diseases. An additional 18% could remain stable with better management. That's a staggering 42% of essentially avoidable costs. This finding highlights the enormous opportunity to reduce healthcare spending by focusing on prevention and better disease management rather than just treating illness.

— [Changing Healthcare - A Platform Approach Using Data & AI](https://www.forbes.com/sites/lutzfinger/2023/07/05/changing-healthcarea-platform-approach-using-data--ai/) · _Forbes_

Question 37

What real example shows how AI agents are already changing user behavior in healthcare?

Accepted Answer

_A user spent $200 based solely on ChatGPT's blood test analysis._

During user research, one person uploaded his blood test to ChatGPT. The AI analyzed the results, suggested supplements, and recommended a web store. The user then spent over $200 at a retailer he had never visited before, simply because ChatGPT said so. In this single interaction, OpenAI became his personal health record, his doctor providing medical recommendations, his replacement for Google in searching where to buy, and his purchase guide, all while skipping traditional product pages entirely.

— [AI Agents - The Internet Is Being Rewritten - Survey Users Vs Brands?](https://www.forbes.com/sites/lutzfinger/2025/07/24/ai-agentsthe-internet-is-being-rewrittensurvey-users-vs-brands/) · _Forbes_

Question 38

What unintended consequences can quality metrics create in healthcare?

Accepted Answer

_Mortality metrics can discourage doctors from treating complex cases._

As Michael Howell reminded me, the real challenges are in finding the metrics that matter and preventing unintended consequences with any given metric. Take a simple metric on mortality for surgery: patients should not die during surgery. But what if the patient's situation was complex to start with? There are cases where a patient did not receive care and died because giving him appropriate care could have resulted in a worse quality score for the doctor.

— [Healthcare And Data - How To Measure Quality](https://www.forbes.com/sites/lutzfinger/2023/06/27/healthcare-and-datahow-to-measure-quality/) · _Forbes_

Question 39

What were Character AI users actually using the platform for?

Accepted Answer

_Lonely users seeking connection, not just entertainment or utility_

Character AI users were mostly people who wanted to flirt, kill time, or lonely people who just wanted someone to talk to. A fascinating study showed that the percentage of clinically depressed users of Character AI was more than tenfold higher than the average percentage of clinically depressed people in the US. People used Character AI because they wanted to have a relationship, and they used that relationship with a computer. From a clinical perspective, Character AI and similar chatbots actually helped people to not commit suicide.

— [The future of AI bots: winning with personalization, conviction, and cross-platform integration](https://www.lutzfinger.com/transcript/2024-08-22-the-future-of-ai-bots-winning-with-personalization-conviction-and-cross-platform/) · _The Edge_

Question 40

What were your main takeaways from JP Morgan about AI in healthcare and beyond?

Accepted Answer

_Cash flow and operational impact matter more than AI hype._

Three key learnings that apply to any industry, not just healthcare. First, humans plus AI will change the industry, not AI alone. Second, cash flow matters, not AI hype. Third, the real AI revolution is operational. As one leading investor said at the conference, 'I don't really care whether they use AI as long as they deliver the bottom line results.' It's not about hyping AI as such. It's about the impact, and to create impact we will need humans. AI plus humans making healthcare teams materially more effective is the massive change, not AI replacing people.

— [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/) · _Forbes_

Question 41

What's the difference between how transformers are used in healthcare interfaces versus drug discovery?

Accepted Answer

_Transformers shine at interface tasks, struggle with mechanistic understanding._

Transformers excel as interfaces to messy healthcare data because they're great at sequence modeling like language and longitudinal notes. They summarize records, find context, and support decisions. They work well over EHR data or ICD-10 codes to predict likely codes or find patients for trials. However, for drug discovery, most transformers just do next-token prediction through autoregression. This creates impressive language competence but doesn't automatically give you conceptual understanding or reliable causal reasoning. Drug development needs mechanism understanding and generalization under uncertainty, and in regulated settings, outputs need guardrails and real-world validation.

— [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/) · _Forbes_

Question 42

Why did you leave your healthcare company to start an AI e-commerce company?

Accepted Answer

_From healthcare prediction models to e-commerce AI startup founder_

I was president of a NASDAQ listed company in New York in the healthcare space, running product and technology for a third party administrator. We built predictive algorithms to reduce the burden of diseases for our members. I left to build a startup focused on fine-tuned large language models for e-commerce because I saw an opportunity. The technology behind it, transformer models, exists since 2017, but how everyone uses it creates new questions. I realized I could create value by knowing things the industry didn't know yet, specifically solving search and discovery problems in e-commerce with this new architecture better than anyone else.

— [After the Gold Rush, with Kurt Wagner](https://www.lutzfinger.com/transcript/2025-10-21-after-the-gold-rush-with-kurt-wagner/) · _The Keynote on AI_

Question 43

Why do self-insured companies have different incentives than traditional healthcare payers when it comes to preventing avoidable admissions?

Accepted Answer

_Self-insured companies bear actual costs, creating aligned health incentives._

Research shows that one third to one quarter of hospital admissions in the US are avoidable. In traditional healthcare, hospitals view admissions as revenue and insurance companies have already priced those risks into premiums, so there's little incentive to prevent them. Self-insured companies are different because they pay the actual care their employees receive and bear the actual cost of healthcare. This creates alignment between the payer and the patient, giving self-insured companies a vested interest in promoting healthier lifestyles and a shared commitment to health that didn't exist before.

— [Three Trends Of Transformation: How AI & Value-Based Care (VBC) Will Drive Transformation Of Self-Insured Healthcare](https://www.forbes.com/sites/lutzfinger/2023/05/01/three-trends-of-transformation-how-ai--value-based-care-vbc-will-drive-transformation-of-self-insured-healthcare/) · _Forbes_

Question 44

Why is AI autocomplete not the same as understanding in healthcare?

Accepted Answer

_LLMs excel at sequence modeling but cannot replace human judgment in medicine._

> AI makes clinicians and scientists much more effective: better priors, better search, better filters, faster loops. Humans stay in the driver's seat because biology is noisy, evidence is incomplete, incentives are asymmetric, and accountability is real.

Source: [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/)

Question 45

Why is it important to measure appropriateness of medical interventions, not just quality?

Accepted Answer

_Fee-for-service payment can incentivize unnecessary procedures._

As Marty Makary told me, it is not sufficient to just measure the quality of medical procedures, but we also need to evaluate the appropriateness of these interventions. The current fee-for-service payment structure might encourage some doctors to perform multiple surgeries rather than one, as they are paid per procedure. Transparency in correcting misalignments is key. This is why utilization metrics look at whether someone should have gotten a given intervention in the first place.

— [Healthcare And Data - How To Measure Quality](https://www.forbes.com/sites/lutzfinger/2023/06/27/healthcare-and-datahow-to-measure-quality/) · _Forbes_

Question 46

Why is it so hard to know the quality of healthcare we receive?

Accepted Answer

_Patients lack data because they are not the customer._

We rate Uber drivers, restaurants, and Amazon delivery, but when it comes to our own health experience, we have little to no data. The reason is structural: the business model of the healthcare provider is to get money from the insurance company, not the patient. The patient is not the customer. Because of this misalignment, knowing quality is of utmost importance, yet the information is not readily available to us as patients.

— [Healthcare And Data - How To Measure Quality](https://www.forbes.com/sites/lutzfinger/2023/06/27/healthcare-and-datahow-to-measure-quality/) · _Forbes_

Question 47

Why isn't autocomplete understanding in healthcare AI?

Accepted Answer

_LLM limitations in clinical settings._

> I just got back from the JP Morgan conference. A few learnings that apply to any industry, not just healthcare:Humans + AI will change the industry. Not AI alone.Cash flow matters. Not AI hype.The real AI revolution is operational.One of the most hyperbolic promises in healthcare in the last month was the claim that: "AI is going to design our drugs and will replace researchers." In my view, this is not true.

— [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/) · _Forbes_

Question 48

Why might minorities in the US have lower vaccination rates?

Accepted Answer

_Historical injustices create a healthcare trust gap for minorities._

These differences might be due to access or availability, but they might also be due to missing trust. Minorities in the US have experienced an unfair healthcare system: from the unethical Tuskegee Syphilis Study to racial bias in pain assessment to the ingrained racial skew in AI models. The Kaiser Family Foundation reported stark differences among different states and different ethnicities that could reflect this trust gap.

— [Get Vaccinated? How Trust In Institutions Determines COVID Vaccination Rates In The EU](https://www.forbes.com/sites/lutzfinger/2021/05/06/get-vaccinated-how-trust-into-the-institutions-determines-vaccination-rates-in-the-eu/) · _Forbes_

Q&A, Healthcare

Can we conclude that trust causes higher vaccination rates?

Did you find other factors that influenced vaccination rates besides trust?

How did racial bias in a healthcare algorithm affect Black patients according to the Science study?

How did you isolate trust as a factor in COVID vaccination rates?

How do existing laws already address AI concerns without new AI-specific regulation?

How do you measure quality in healthcare data?

How does AI change healthcare?

How does AI find patterns in data that humans miss, like in healthcare?

How does Earli's data flywheel work in practice?

How does Marpai Connect use AI to overcome the small numbers problem?

How does multimodal AI improve the healthcare experience for doctors dealing with overwhelming patient data?

How does Neuralens measure brain health non-invasively compared to current methods?

How does value-based care differ from traditional fee-for-service payment?

How does Zenith OS reduce costs in neuropsychiatry treatment?

How is the key skill for doctors changing because of AI?

How is Value-Based Care different from traditional fee-for-service healthcare, and how fast is it growing?

How much capital was invested in the digital health startup landscape in 2021?

How will Large Language Models impact healthcare according to Lutz?

What are JEPA and world models, and how do they differ from token prediction?

What are some examples of how healthcare providers are using LLMs with patients?

What are the three broad measurement areas in today's healthcare?

What are the two main challenges value-based care aggregators must overcome?

What did the data show about high trust versus low trust countries?

What does AI actually contribute to drug development today, according to what you saw at JP Morgan?

What does Marpai Connect measure in value-based care, and what do you de-emphasize?

What does physical AI actually mean, and why does it matter?

What does responsible AI development look like in healthcare, according to Earli's approach?

What example shows how data use in one industry could be inappropriate in another?

What is BioAI?

What is OpenAI's stated ambition for AI agents according to this article?

What is the core discovery mechanism behind multimodal AI, and how does it relate to Amazon's recommendation approach?

What is the difference between LLMs and traditional machine learning models in healthcare?

What is the potential financial impact of addressing Type 2 Diabetes in self-insured populations?

What is value-based care and how does AI enable it?

What outcome metrics does Marpai Connect use to evaluate care effectiveness?

What percentage of healthcare costs are linked to reversible or manageable diseases?

What real example shows how AI agents are already changing user behavior in healthcare?

What unintended consequences can quality metrics create in healthcare?

What were Character AI users actually using the platform for?

What were your main takeaways from JP Morgan about AI in healthcare and beyond?

What's the difference between how transformers are used in healthcare interfaces versus drug discovery?

Why did you leave your healthcare company to start an AI e-commerce company?

Why do self-insured companies have different incentives than traditional healthcare payers when it comes to preventing avoidable admissions?

Why is AI autocomplete not the same as understanding in healthcare?

Why is it important to measure appropriateness of medical interventions, not just quality?

Why is it so hard to know the quality of healthcare we receive?

Why isn't autocomplete understanding in healthcare AI?

Why might minorities in the US have lower vaccination rates?