As OpenAI and Anthropic move deeper into healthcare, experts say AI chatbots are becoming the new front door to medicine. This shift is shaking things up for some health tech startups, redefining the patient-provider relationship, and intensifying debates over safety, privacy and accountability.
AI rivals Anthropic and OpenAI are both expanding their large language models into healthcare. Anthropic is blending its enterprise and consumer tools in a single platform, while OpenAI is separating its consumer-facing ChatGPT Health from its industry-focused OpenAI for Healthcare. They are both targeting patients, providers and researchers with AI tools for tasks ranging from personal health insights to coding to prior authorization.
Greenway Health blends smart technology with human expertise to elevate revenue cycle.
A universal medical coder, applied consistently across care settings, offers a practical solution to the enduring challenge of data integrity.
It is our obligation to do our part to find ways to improve patient outcomes and lower health care costs. This often means continuously finding better, more efficient ways to solve problems, utilizing the most optimal tools and data we have at our disposal. And as of right now, and for the foreseeable future, that means finding appropriate use cases to leverage the power of AI and LLMs where it most makes sense.
By safety and cautiously integrating LLMs in a clinician setting, we have the potential for the ultimate levels of personalization: the perfect words for the right person at the right time.
The way forward isn’t bigger models. It’s smaller, smarter ones. Small Language Models (SLMs) are designed to do what LLMs can’t: learn from enterprise data and focus on specific problems.
In a landscape where complexity has long been the norm, the power of one lies not just in unification, but in intelligence and automation.
Mount Sinai researchers found that popular AI chatbots like ChatGPT and DeepSeek R1 can generate convincing but false medical information when given even a single fabricated term in a prompt. While the study underscored the need for stronger safeguards, its lead author noted that generative AI still holds major promise for reducing clinician workload if used responsibly.
As the adoption of artificial intelligence (AI) advances in healthcare, key considerations can help validate that the technology is purpose-built to help clinicians protect patient safety.
The American Cancer Society struck a multi-year collaboration with Layer Health, a healthcare AI company that uses LLMs to improve data abstraction and generate insights. The partners will abstract data from thousands of medical charts of patients enrolled in the American Cancer Society’s research studies, with the ultimate goal of shortening research timelines.
The current wave of generative AI has captured public imagination, but we must temper our expectations. While these models demonstrate impressive capabilities in processing vast amounts of medical literature and generating coherent text, they're far from perfect.
How can AI benefit and improve patient outcome in the emergency department? Watch the latest episode to find out.
The distinction between value-based care and value-based payment isn't merely semantic — it represents a crucial fork in the road for healthcare delivery. As AI and other technologies become more prevalent in healthcare, we must ensure they're deployed in service of improving patient care rather than merely optimizing revenue.
The future of healthcare will be one where technology doesn't just serve the industry but genuinely improves the lives of individuals, transforming how we interact with medical data, healthcare providers, and our personal well-being.
LLMs, with their remarkable conversational abilities, are uniquely positioned to help solve this crisis. But as with the dotcom boom before it, the surge of AI 'therapists' has created a turbulent landscape where separating legitimate innovation from dangerous shortcuts isn't straightforward.
Dr. Eric Topol pointed out that while initial findings on generative AI in clinical settings are promising, they may not withstand the complexities of real-world clinical practice.
Large language models will soon become a much bigger part of doctors’ clinical workflows, former FDA Commissioner Scott Gottlieb said Tuesday at the 3rd Annual Summit on the Future of Rural Health Care.