Psychiatry’s Human Skills May Define the Doctor’s Role in the AI Era

Generative artificial intelligence can now generate differential diagnoses, answer patient questions, and draft treatment plans with a fluency that would have seemed like science fiction only a few years ago. As these systems absorb a growing share of medicine’s cognitive labor, a deceptively simple question is gaining urgency across hospitals and medical schools: what, exactly, are physicians for? A new correspondence published in Academic Psychiatry by Dr. Stephanie V. Ng of Mayo Clinic argues that the answer may lie in a specialty long dismissed as the least technology-driven branch of medicine. Psychiatry, she contends, has been quietly cultivating the very human capacities that will define the physician’s value in an age when information itself has become cheap, abundant, and instantly retrievable by machine.

The core of the argument rests on an uncomfortable observation about how medicine trains its practitioners. Medical education has long treated knowledge as the central currency of doctoring. Aspiring physicians spend years memorizing minutiae: the neuroanatomical pathways that link a left middle cerebral artery stroke to right-sided weakness and aphasia, the mechanisms by which Staphylococcus aureus evades beta-lactam antibiotics, and the serum thresholds at which lithium becomes acutely toxic. The system rewards immediate recall through full-day licensing and board certification examinations, as well as the ritual of rapid-fire questioning during clinical rounds. Yet fact retrieval, protocol recitation, and drug-interaction flagging are precisely the tasks that frontier AI systems now perform at or above expert level. Recent research cited in the correspondence shows that general-purpose large language models can outperform specialized clinical AI tools on medical benchmarks, a finding that underscores how quickly machine competence is spreading across the cognitive territory that medical training once monopolized.

Ng’s warning is stark in its implications: if medicine continues to define expertise primarily as the possession of specialized information, physicians may be training for a version of medicine that is going extinct. The literature on AI in medicine has focused heavily on what clinicians should know about AI, including its technical fundamentals, potential utility, and privacy and ethical risks. Far less attention has been paid to what clinicians should know about being clinicians when algorithms can do much of the thinking. That inversion, from machine literacy to human identity, frames the four clinical competencies the correspondence identifies as durable. Their expression may evolve alongside AI’s capabilities, but their underlying core, Ng argues, deserves continued emphasis in physician training.

The first competency is embodied presence, which Ng describes as a clinical tool in its own right. Studies have found that patients sometimes perceive AI-generated responses as more empathic than human ones, but empathy is only one facet of the physician’s relational engagement. Sitting with patients who are frightened about the future, ambivalent about their options, or genuinely difficult to like, physicians may notice their own countertransference: unexpected warmth, creeping anxiety, or even irritated boredom. These feelings are not distractions to be suppressed. They reveal how patients navigate the world, data that cannot easily be gleaned from the words in a chart. A physician’s embodied personhood, in this view, remains an instrument that guides decision-making, and reflective self-awareness about patient interactions should be valued as highly as diagnostic accuracy and pharmacology.

The second competency concerns participation, a capacity that detection alone cannot replace. AI-generated answers are bounded by the information provided to them. A standalone model can often produce the correct medical recommendation when all salient facts are entered, but research indicates that this accuracy decreases significantly when actual humans are involved, because patients frequently do not know which details are clinically relevant. Physicians therefore retain a critical role in eliciting and integrating the nuances of a patient’s narrative. Even as multimodal systems learn to analyze words, speech prosody, and facial expressions, and even if their capabilities eventually expand to encompass all five senses, Ng draws a sharp technical distinction: detection is different from participation. Diagnostic clarification and therapeutic work are co-created in the encounter, as physicians respond not only to what patients say but to how they say it, the hesitations, contradictions, posture shifts, and averted gazes, as well as to what remains unspoken.

The correspondence illustrates this point with vivid clinical vignettes. The patient who insists he is pursuing a healthier lifestyle while clutching a bag of fast food, or the adolescent who reports feeling close to her parents while sitting in the opposite corner of the room, present observations that shape interviews and treatment planning in ways not easily encoded into algorithms. These are not marginal details; they are often the hinge on which an accurate formulation turns. Human expertise, in its subtlety, still carries weight in the age of AI, precisely because the machine’s input is limited to what it is given, while the physician’s perception operates on the full, messy texture of the encounter as it unfolds in real time.

The third competency is discernment, which Ng predicts will matter more than information delivery as AI saturates the knowledge landscape. Patients will increasingly come to physicians not from a dearth of facts but from an overwhelm of them. Yet facts alone cannot explain to a family why the guideline-recommended treatment may still be the less ideal choice given their circumstances, nor can facts help a patient make peace with a decision when every option exacts a cost. Access to technically accurate information does not inherently produce effective decision-making. No matter how capable AI becomes at enumerating the full spectrum of risks, benefits, and alternatives, the physician is needed to help patients sift through the information, narrow the options, and arrive at decisions that are both medically and personally appropriate. In an era of infinite second opinions, the doctor becomes a filter and a guide rather than a source.

The fourth competency is the capacity to make decisions while acknowledging uncertainty, a skill Ng calls the psychiatrist’s native habitat. The pathophysiology of many mental illnesses remains incompletely understood, diagnostic boundaries can blur, and treatments are sometimes reached by trial and error. Medicine as a whole contains similar territory: answers that remain unknown, evidence limited by caveats, and professional consensus still evolving. The threshold between established knowledge and clinical intuition is not always clear, and learning to discern where that threshold lies, then helping patients navigate the ambiguous terrain beyond it, will remain essential. The contrast with machine behavior is pointed. AI chatbots may confidently hallucinate rather than admit not knowing, whereas good physicians acknowledge uncertainty and move forward toward a shared decision rather than attempting to conceal or prematurely resolve the unknown.

Ng’s conclusion is that AI literacy among clinicians is necessary but not sufficient. The practice of medicine should be built not only around what AI can do more efficiently but around what AI can never be: an embodied participant who helps patients navigate uncertainty and vulnerability. In short, the clinician’s humanity. These capacities are hard to measure and therefore easy to dismiss in an age of quantification, when board scores and benchmark accuracies dominate the conversation about medical competence. But they remain central to effective care, and the correspondence suggests that medical education should resist the gravitational pull toward training students as information processors whose output can be matched by a model. As information becomes ubiquitous and certitude can be cheerfully simulated by a chatbot, the relational skills from the psychiatrist’s toolkit, presence, participation, discernment, and comfort with ambiguity, may prove to be the most future-proof competencies in the entire medical curriculum.

The broader significance of the argument extends beyond psychiatry. Every specialty that touches human suffering, from oncology to geriatrics to primary care, will confront the same question as generative systems mature: when the machine can retrieve, reason, and recommend, what remains for the person in the white coat? Ng’s answer, drawn from a specialty that never depended on technology for its core work, is that the answer was never primarily in the facts. It was in the relationship. Whether medical educators act on that insight, and begin measuring and rewarding these relational capacities with the same rigor applied to factual recall, may determine whether the next generation of physicians enters a profession that still needs them, or one that has quietly redefined them out of existence.

Subject of Research: The enduring human competencies of physicians, particularly psychiatrists, in the age of generative artificial intelligence

Article Title: What Are Physicians For? How Psychiatry’s Competencies Endure Into the Age of AI

Article References: What Are Physicians For? How Psychiatry’s Competencies Endure Into the Age of AI. (n.d.). https://doi.org/10.1007/s40596-026-02448-z

Image Credits: AI Generated

DOI: 10.1007/s40596-026-02448-z

Keywords: generative AI, psychiatry, medical education, physician competencies, clinical empathy, countertransference, medical decision-making, clinical uncertainty, large language models, doctor-patient relationship, AI literacy, Mayo Clinic