Today, I had the privilege of speaking at the Charmalot Conference in Washington, D.C., where I addressed an audience of forward-thinking entrepreneurs and clinicians.
My keynote, “How AI Can Empower Patients & Doctors to Take Back Control of American Medicine,” centered on a concept that’s both exciting and urgent: the transformative power of generative AI in medicine. It is the central theme of my newest book ChatGPT, MD.
The entrepreneurs, doctors, nurses and administrators in the room already recognize the incredible potential of AI. Their focus, understandably, is on how to apply these innovations to improve patient health and make money while minimizing the liability exposure that all medical devices and digital applications create. This challenge is not unique to healthcare, but it is particularly pronounced in our field where the stakes are always life and death.
During my talk, I highlighted three distinct pathways for innovators and leaders trying to succeed in medicine with GenAI:
- Creating AI Tools For Patients: Many innovators are drawn to developing AI-driven solutions for healthcare, and the market is already rich with accomplished competitors, including Nvidia, Google, Helical, Cadence, Arm and others creating diagnostic AI systems. These tools offer opportunities to improve patient health and drive a profit, but they carry significant liability risks because they directly influence patient outcomes. Errors in diagnosis are both inevitable and potentially costly, as research shows they occur daily in medical practice. In fact, a study published in the New England Journal of Medicine found that 1 in 4 patients who die in a hospital or are transferred to the ICU have experienced a misdiagnosis. This underscores the complexity and legal risks associated with AI-driven diagnostics. When an AI bot is involved in making a diagnosis or providing treatment advice, determining responsibility in the event of an error becomes a legally complex issue. As a result, this pathway is high-risk, high-reward, offering the potential for significant financial gains alongside substantial legal exposure. However, the appeal of replacing an expensive human workforce with lower-cost technology is a compelling argument for CEOs and CFOs, making this an attractive option despite the risks.
- Supporting Patients Using AI: Another approach is for businesses to offer AI-assisted clinical programs that target chronic diseases such as diabetes and heart failure. In this model, clinicians—doctors, nurses, healthcare educators, and lifestyle medicine coaches—continue to play a central role in patient care, but their work is augmented by generative AI. For instance, AI can assist in assessing clinical progress by analyzing data from home monitors and providing recommendations for medication adjustments. However, all final decisions are reviewed and approved by a clinician, ensuring that human oversight remains a key component of care. Generative AI can also support patients in managing their diet and exercise, offering personalized grocery-shopping lists and recipes based on individual health needs. While this approach reduces liability (since clinicians are involved in the decision-making process), it comes with high program costs and uncertain financial returns. The benefits are largely theoretical, hinging on the promise that better clinical management will ultimately lead to lower healthcare premiums. As a result, while the risk is lower, monetization remains a challenge.
- Supporting Doctors Providing Medical Care: When making medical-care decisions, clinicians typically rely on their memory, intuition and established algorithms. However, the growing demands on their time often force them to rush and skip critical steps. Generative AI has the potential to alleviate these pressures by offering both time-saving tools and clinical support. Already, several companies provide software that converts conversations with patients into electronic health record (EHR) notes, saving physicians over an hour each day and allowing them to focus more on the patient rather than the computer screen. Looking ahead, generative AI will increasingly support clinicians by providing relevant, patient-specific information that can reduce medical errors and enhance the quality of care. In this model, liability remains low since the final decisions are always made by the doctor. However, despite the clear benefits, convincing insurance companies to pay for the improvements in care quality could be a significant challenge.
For entrepreneurial companies, higher risk and reward go hand in hand. For patients, each of these approaches offers a meaningful improvement over the current standard of care.
As I spoke, I also delved into the high costs and lagging quality of American medicine, problems I tackled in my previous books, Mistreated and Uncaring. While these works highlighted the systemic and cultural issues plaguing healthcare, they fell short in one crucial area: compelling national change.
Despite raising awareness, medical costs have continued to climb, clinical outcomes have languished, and patients are no closer to taking control of their health. But now, with the advent of generative AI, we have the tools to improve clinical quality, reduce costs and drive meaningful innovation—barriers that have long stood in the way to building a better healthcare system.
Of course, to harness this potential, we need more than just technology. We also need a shift in our healthcare payment model. That’s where capitation—paying providers a fixed amount per patient regardless of the number of services they provide—comes into play. Capitation incentivizes healthcare providers to focus on prevention and efficiency, aligning their goals with those of patients. And when capitation occurs at the delivery-system level, instead of through private or public insurance, doctors will assume more risks but also enjoy the rewards of greater autonomy in patient care, fewer administrative hurdles like prior authorization and the ability to make decisions that best serve their patients without external interference. This form of capitation is what makes the use of generative AI both beneficial and necessary.
We stand on the precipice of previously unimaginable progress. The journey ahead will require collaboration among innovators, healthcare professionals and policymakers. Together, we must ensure that AI becomes a force for good in medicine, empowering patients and doctors alike to create a more equitable, efficient and effective healthcare system.
I would like to extend my special thanks to Pramila Srinivasan, Ph.D., CEO of CharmHealth, and Ranjani Rangan, Digital Health Marketing Communications Strategist at CharmHealth and host of the podcast Digital Health Disruptors, for their support and leadership in making this conference a success.
About CharmHealth
CharmHealth is a leading provider of innovative healthcare technology solutions that empower healthcare organizations to deliver efficient, high-quality care. With a focus on interoperability, patient engagement, and streamlined workflows, CharmHealth offers a comprehensive suite of solutions designed to meet the evolving needs of modern healthcare. For more information on CharmHealth, visit www.charmhealth.com.
