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AI should change how physicians learn and work, not lower the bar for what they must know and be able to do themselves. A clinician needs to assess a case, recognize uncertainty, communicate with a patient, and take responsibility for a decision even when an AI tool is unavailable, wrong, biased, or poorly suited to the patient. That is the practical meaning of “the physician is the only redundancy”: a case for resilient human competence, not a claim that AI has no value or that one clinician is the only safeguard.
What does the competency bar mean when AI is available?
Competency-based medical education (CBME) is an outcomes-based approach: it defines competencies as observable abilities and aims toward patient-centered outcomes. In December 2024, the Association of American Medical Colleges (AAMC), the American Association of Colleges of Osteopathic Medicine, and the Accreditation Council for Graduate Medical Education released shared foundational competencies for U.S. undergraduate medical education. The framework spans six areas:
- Professionalism
- Patient care and procedural skills
- Medical knowledge
- Practice-based learning and improvement
- Interpersonal and communication skills
- Systems-based practice
These domains are a useful way to frame the question: not merely whether a learner can produce an answer with AI, but whether they can demonstrate the relevant ability and use it safely in context. The AAMC describes CBME and the shared competencies at its CBME resource.
For educators, a reasonable implication is to distinguish assistance from substitution. If a learner uses a tool to draft a differential diagnosis, the assessment should still establish whether they can identify the patient’s important findings, notice when the output does not fit, explain the reasoning, and choose an appropriate next step. Those examples are applications of the competency framework, not additional competencies formally prescribed by the AAMC.
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What should physicians learn about AI?
AI literacy is more than knowing how to prompt a system. It includes understanding the tool’s intended use, checking its output against the patient and available evidence, recognizing uncertainty and possible bias, protecting confidential information, and explaining the role of technology in care. It does not mean delegating clinical judgment to the tool.
The AAMC’s Principles for the Responsible Use of Artificial Intelligence in and for Medical Education, version 2.0 completed July 31, 2025, offer guidance for institutions rather than a one-size-fits-all curriculum. They call for human-centered judgment, transparency about technology use, equitable learner access, educator development, interdisciplinary teaching, privacy safeguards, and frequent evaluation in the settings where tools are used. AAMC’s emerging competency work also includes AI alongside quality improvement and patient safety and telehealth across undergraduate, graduate, and continuing medical education.
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These principles make implementation part of the curriculum. A school or training program should consider how AI use affects admissions, teaching, workplace learning, coaching, simulation, assessment, and program evaluation—not only clinical decision support. Learners need a fair opportunity to develop AI-related skills, and faculty need enough preparation to teach and assess them. Use of a tool should also be evaluated in the actual setting where it will be used, rather than treated as safe or effective everywhere because it performed well elsewhere.
How should assessment work when a learner can use AI?
Assessment should reveal what the learner can do, not just what a learner-tool combination can produce. Programs can make the distinction explicit by stating when AI is permitted, what must be disclosed, and which parts of a task the learner must demonstrate independently. The right design depends on the learning objective: AI may be appropriate for practicing critique or workflow, while an assessment of unaided clinical reasoning needs conditions that let faculty observe that reasoning.
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- Assess the process as well as the output. Ask learners to identify relevant evidence, explain why they accept or reject an AI suggestion, and describe remaining uncertainty.
- Include cases where the tool may not fit. Evaluate whether a learner notices missing context, contradictory findings, or a mismatch between the intended use and the patient’s situation.
- Keep core abilities observable. Use appropriate direct observation, oral reasoning, simulation, or other assessment methods to determine what the trainee can demonstrate without relying on generated output.
- Make disclosure and privacy expectations clear. Learners should know when technology use must be communicated and what information may be entered into a system under institutional rules.
These are practical assessment choices, not a claim that one format or a human review step eliminates error. Human oversight matters, but it must be paired with tool evaluation, appropriate assessment, and safeguards suited to the setting.
Who remains accountable for an AI-assisted clinical decision?
In the United States, the American Medical Association’s June 10, 2026 policy announcement says AI should assist rather than replace physician judgment. It calls for physician oversight and accountability, along with transparency, evidence attribution, evaluation, validation, and explainability in clinical decision support. AMA CEO John Whyte, MD, MPH, said, “AI has enormous potential in healthcare, but it cannot replace physician judgment,” and that “there must always be transparency, accountability, and meaningful physician oversight.” The announcement is available from the AMA.
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Accountability also needs to be taught as a clinical practice. A physician should be able to explain what role a tool played, what evidence informed the decision, and why the final action was appropriate for the patient. A tool’s confident presentation is not evidence that its recommendation is correct, and a clinician’s review should not be reduced to accepting an output by default.
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How can educators judge whether an AI tool belongs in training?
Evaluate the tool against its intended purpose and deployment context; a general claim that a system is “AI-powered” says little about whether it is appropriate for a particular learning or clinical task. The AAMC, AMA, and Medical Council materials support a set of practical questions:
- Purpose: What task is the system intended to support, and is the planned use within that scope?
- Evidence and validation: What evidence supports its performance, and has it been evaluated in the relevant setting and population?
- Explainability and attribution: Can users understand the basis of a recommendation and identify the evidence behind it?
- Equity: Could performance or access differ across patients or learners, and how will those differences be identified?
- Privacy: What information is entered, how is it protected, and do institutional safeguards cover the specific use?
- Monitoring: How will the institution evaluate the tool frequently after adoption and respond if it performs poorly or its use changes?
These are evaluation axes, not evidence that any particular product or approach is superior. The cited guidance does not establish that AI improves or harms training outcomes, nor that oversight by itself prevents automation bias or clinical error. The strongest educational position is therefore neither blanket rejection nor uncritical adoption: teach learners to use appropriate tools while preserving demonstrable clinical competence and clear human responsibility.
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