Category: IT in Healthcare

Johns Hopkins researchers find that despite pressure on clinicians to be early adopters of AI, many face scepticism from peers for using it

Doctors who use artificial intelligence at work risk having their colleagues deem them less competent for it, according to a recent Johns Hopkins University study.

While generative AI holds significant promise for advancing health care, a new study finds its use in medical decision-making impacts how physicians are perceived by their colleagues. The research shows that doctors who primarily rely on generative AI for decision-making face considerable scepticism from fellow clinicians, who correlate their use of AI with a lack of clinical skill and overall competence, resulting in a diminished perceived quality of patient care.

The research included a diverse group of clinicians from a major hospital system, involving attending physicians, residents, fellows, and advanced practice providers. Results of the study were published in Nature Digital Medicine.

Stigma stunts better care

The findings may indicate a social barrier to AI adoption in health care settings, which could slow advances that might improve patient care.

“AI is already unmistakably part of medicine,” says Tinglong Dai, professor of business at the Johns Hopkins Carey Business School and co-corresponding author of the study. “What surprised us is that doctors who use it in making medical decisions can be perceived by their peers as less capable. That kind of stigma, not the technology itself, may be an obstacle to better care.”

The study, conducted by researchers at Johns Hopkins University, involved a randomised experiment where 276 practicing clinicians evaluated different scenarios: a physician using no AI, one using AI as a primary decision-making tool, and another using it for verification. The research found that as physicians were more dependent on AI, they faced an increasing “competence penalty,” meaning they were viewed more sceptically by their peers than those physicians who did not rely on AI.

“In the age of AI, human psychology remains the ultimate variable,” says Haiyang Yang, first author of the study and academic program director of the Masters of Science in Management program at the Carey Business School. “The way people perceive AI use can matter just as much as, or even more than, the performance of the technology itself.”

Skipping AI equalled more respect

According to the study, peer perception suffers for doctors who rely on AI. Framing generative AI as a “second opinion” or a verification tool partially improved negative perceptions from peers, but it did not fully eliminate them. Not using GenAI, however, resulted in the most favourable peer perceptions.

The findings align with theories that suggest perceived dependence on an external source like AI can be seen as a weakness by clinicians.

Ironically, while GenAI’s visible use can undermine a physician’s perceived clinical expertise among peers, the study also found that clinicians still recognise AI as a beneficial tool for enhancing precision in clinical assessment. The research showed that clinicians still generally acknowledge the value of GenAI for improving the accuracy of clinical assessments, and they view institutionally customized GenAI as even more useful.

The collaborative nature of the study led to thoughtful suggestions for GenAI implementation in health care settings, which are crucial to balance innovation with maintaining professional trust and physician reputation, the researchers note.

“Physicians place a high value on clinical expertise, and as AI becomes part of the future of medicine, it’s important to recognise its potential to complement – not replace – clinical judgment, ultimately strengthening decision making and improving patient care,” said Risa Wolf, co-corresponding author of the research and associate professor of pediatric endocrinology at Johns Hopkins School of Medicine with a joint appointment at the Carey Business School.

Source: Johns Hopkins University

New paper critically examines the US Food and Drug Administration’s regulatory framework for artificial intelligence-powered healthcare products, highlighting gaps in safety evaluations, post-market surveillance, and ethical considerations.

An agile, transparent, and ethics-driven oversight system is needed for the U.S. Food and Drug Administration (FDA) to balance innovation with patient safety when it comes to artificial intelligence-driven medical technologies. That is the takeaway from a new report issued to the FDA, published this week in the open-access journal PLOS Medicine by Leo Celi of the Massachusetts Institute of Technology, and colleagues.

Artificial intelligence is becoming a powerful force in healthcare, helping doctors diagnose diseases, monitor patients, and even recommend treatments. Unlike traditional medical devices, many AI tools continue to learn and change after they’ve been approved, meaning their behaviour can shift in unpredictable ways once they’re in use.

In the new paper, Celi and his colleagues argue that the FDA’s current system is not set up to keep tabs on these post-approval changes. Their analysis calls for stronger rules around transparency and bias, especially to protect vulnerable populations. If an algorithm is trained mostly on data from one group of people, it may make mistakes when used with others. The authors recommend that developers be required to share information about how their AI models were trained and tested, and that the FDA involve patients and community advocates more directly in decision-making. They also suggest practical fixes, including creating public data repositories to track how AI performs in the real world, offering tax incentives for companies that follow ethical practices, and training medical students to critically evaluate AI tools.

“This work has the potential to drive real-world impact by prompting the FDA to rethink existing oversight mechanisms for AI-enabled medical technologies. We advocate for a patient-centred, risk-aware, and continuously adaptive regulatory approach – one that ensures AI remains an asset to clinical practice without compromising safety or exacerbating healthcare disparities,” the authors say.

Provided by PLOS