As AI Augments Medical Research and Patient Care
Last week, the Stanford Medicine report "Applying Large Language Models to Assess Quality of Care" discussed how an AI tool analyzed 16,000 medical records to identify gaps in care, helping clinicians address potential treatment oversights. Comments from their press release included:
“Typically, experts seeking answers to questions about care need to pore over hundreds of medical charts. But new research shows that large language models — AI tools that can find patterns in complex written language — may be able to take over this busywork and that their findings could have practical uses. For instance, AI tools could monitor patients’ charts for mentions of hazardous interactions between drugs or could help doctors identify patients who will respond well or poorly to specific treatments.
“As scientists build more AI tools for medical research, they need to consider what the tools do well and what they do poorly. Some tasks, such as sorting through thousands of medical records, are ideal for an appropriately trained AI tool. Others, such as understanding the ethical pitfalls of the medical landscape, will require careful human thought.”
Separately, a report from University College London, titled 'Large language models surpass human experts in predicting neuroscience results' found that LLMs achieved 81% accuracy compared to 63% for human experts.
Lead author Ken Luo, PhD said “Our work investigates whether LLMs can identify patterns across vast scientific texts and forecast outcomes of experiments … Scientific progress often relies on trial and error, but each meticulous experiment demands time and resources. Even the most skilled researchers may overlook critical insights from the literature.
“We envision a future where researchers can input their proposed experiment designs and anticipated findings, with AI offering predictions on the likelihood of various outcomes … This would enable faster iteration and more informed decision-making in experiment design.”
OUR TAKE
As AI tools analyze larger population datasets, they will transform healthcare delivery and medical research by improving scientific rigor, accelerating discoveries, and enabling more personalized patient care.
Successful AI implementation in healthcare will require organizations to address technical challenges around data and infrastructure, build trust among healthcare providers, and navigate regulatory and ethical requirements to ensure equitable patient care.
While AI tools can enhance research outcomes through predictive capabilities, they should augment human expertise rather than replacing staff.