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.
Scheduling software is helpful for wellness business owners in numerous ways. They get everything they need to create appointments, reschedule clients and track cancellations in one platform.
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.
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.
Explore the top mobile IV therapy companies boosting immune health on demand, with standout services, expert care and innovative delivery across the U.S.
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.
Try a variety of LLM-based tools yourself. Understand the risks, limitations, and who is working on putting quality frameworks and guardrails in place. Engage your employees and partners in discussions about what might now be possible.
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No one AI technology will address everything and solve all problems, so systems should prioritize those that promise the greatest value and ROI.
It’s important that we focus attention on the areas where AI is already having a significant impact. These are the practical applications that will allow the best and brightest researchers to focus on science rather than data munging.
Researchers at the University of Massachusetts Amherst released a paper this week showing that large language models tend to hallucinate quite a bit when producing medical summaries.
Anthropic's Claude3-Opus performed better than GPT-4, but both fell short of humans on a test of objective medical knowledge. The study was conducted by a firm developing LLMs specifically for healthcare that claim to be incorporating peer-reviewed sources of information.
Hospitals need to start thinking about ways to build patients’ trust in generative AI in order for the healthcare industry to fully harness the technology’s potential. They can do this through methods like having transparent conversations, asking for patients’ consent to use the tools and training models on internal data, experts said.