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Experts discussing AI in medical education at an October 15 summit said students’ use of AI is not automatically cheating, especially when instructors make its use part of the assignment. They also warned that relying on AI could weaken clinical reasoning, making training without technology an important concern.
Medical students are not automatically cheating when they use artificial intelligence, experts said at the Association of Health Care Journalists Fall Summit in Los Angeles on October 15. But educators must teach responsible use while ensuring trainees can still reason through clinical problems without AI, speakers said.
Graciela Gonzalez-Hernandez, a professor and vice chair for research and education in computational biomedicine at Cedars-Sinai Medical Center, argued that using AI is not the same as plagiarism. She described an approach in which instructors ask students to share their prompts and the resulting text, then work through the output together. The goal, she said, is to bring AI use into classroom discussion rather than have students conceal it.
Gonzalez-Hernandez said medical programs should be AI-friendly while setting expectations for how students use the tools. She said students will use AI and that teaching them how to do so properly is more productive than focusing only on whether they can hide its use. The report did not describe a specific school policy or a formal definition of acceptable use across medical schools.
Speakers also raised the possibility that AI may help clinicians notice gaps or biases in medical documentation. Gonzalez-Hernandez said language in clinical notes can reflect doubt about a patient’s account, and that such doubt may vary across patient groups. She suggested AI could prompt clinicians to ask questions they may have missed, while emphasizing that its output should itself be questioned.
Balancing AI With Clinical Judgment
The debate matters because medical students are training for work in which decisions affect patients, not just grades. If students lean on AI before they can independently interpret information, they may be less prepared when tools are unavailable or unreliable. At the same time, treating all AI use as misconduct could discourage students from learning to evaluate and use tools that may become part of clinical practice.
The speakers’ comments point to a distinction between transparent, guided use and concealing AI assistance. They also highlight a separate issue: even when an assignment permits AI, students need opportunities to demonstrate their own reasoning. The discussion did not establish a universal rule for when AI use counts as cheating; that judgment can depend on course instructions and institutional policies.
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Why Educators Are Weighing AI
The summit discussion brought together perspectives on medical education, clinical practice and technology. Gonzalez-Hernandez spoke in favor of making AI use visible in assignments. Tina Hernandez-Boussard, a Stanford professor of biomedical informatics, focused on the risk that repeated reliance on AI could reduce trainees’ confidence and ability to synthesize information independently.
Session moderator Eli Cahan, an investigative journalist and neonatal intensive care fellow at Stanford, questioned which forms of clinical reasoning doctors must retain as technology improves. He compared the issue to navigation: a skill once essential for travel can become less routinely used when tools such as GPS are available. Cahan said he was withholding judgment about which clinical reasoning tasks might be delegated, rather than arguing that physicians no longer need reasoning skills.
“I’m withholding judgment until we see whether and what kind of clinical reasoning actually is relevant for doctors to do.”
— Eli Cahan, session moderator
Policies and Skills Still Unsettled
The summit discussion did not establish a shared standard for when AI use in medical school is permitted, must be disclosed or violates academic rules. Students still need to follow the instructions and policies set by their own courses and institutions. The report also provided no research measuring whether AI use has already weakened clinical reasoning among medical students or residents.
The Uganda example was presented by Hernandez-Boussard as an account of residents’ difficulty synthesizing information during a power loss; details about the hospital, the residents and the episode were not provided. Speakers likewise did not specify which clinical tasks could safely be delegated to AI. Whether AI can reduce bias in documentation, and under what conditions, remains a proposition raised at the summit rather than a demonstrated outcome in the report.
How Training Could Adapt
The discussion pointed toward classroom approaches that make AI use visible: instructors can ask students to show their prompts, examine generated text and explain how they checked it. Separately, programs can test trainees’ ability to interpret information and make decisions in exercises that do not rely on AI or other technology.
No new policy, study or follow-up date was announced in the report. For now, the practical distinction is between learning to use AI openly and appropriately, and allowing it to replace the independent skills medical training is meant to develop. How schools define that balance remains for educators and institutions to work out.
Key Questions
Is using AI for medical school work automatically cheating?
No universal rule was announced at the summit. Gonzalez-Hernandez said AI is not plagiarism, but students should follow their instructors’ directions and school policies.
What classroom approach did Gonzalez-Hernandez describe?
She said instructors can ask students to share their prompts and AI-generated text, then review and work on the output together. The approach makes AI use visible and discussable.
What risk did experts raise about AI use?
They warned that relying on AI could contribute to deskilling, leaving trainees less able to interpret information or make decisions when the tools are unavailable.
Could AI also help doctors?
Gonzalez-Hernandez suggested AI might prompt clinicians to consider questions they missed or help identify patterns in clinical language. Those potential benefits were discussed at the summit, not established as proven outcomes in the report.
Source: rss
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