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: Ng, S. V. (2026). What Are Physicians For? How Psychiatry’s Competencies Endure Into the Age of AI. Academic Psychiatry. 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

A new study published in Annals of Gastroenterological Surgery asks whether machine learning could tip that balance. Researchers from the University Hospital of Split in Croatia analyzed the records of 1,547 adults operated on for suspected appendicitis between January 2020 and June 2024, ultimately assembling a rigorously curated dataset of 623 patients. Of these, 66 had a histologically normal appendix, 248 had uncomplicated appendicitis, and 309 had complicated appendicitis confirmed by pathologists. Crucially, every case was verified by histopathology, eliminating the diagnostic guesswork that plagues many prior studies in which patients were classified by clinical or radiological impression alone.

The team built and compared four machine learning models: logistic regression, random forest, balanced random forest, and XGBoost, an extreme gradient boosting algorithm. All models relied exclusively on routinely available clinical and laboratory data—age, sex, symptom duration, fever, nausea, vomiting, rebound tenderness, pain migration, white blood cell count, neutrophil and lymphocyte percentages, platelet indices, C-reactive protein, and serum sodium. No imaging data were included. Missing values, which ranged from zero to 5.3 percent across variables, were imputed with a bagged trees algorithm carefully configured to prevent data leakage between training and test sets.

The methodological centerpiece of the study was a nested cross-validation design, with five inner and outer folds repeated ten times, yielding fifty independent test evaluations. Hyperparameters were tuned exclusively on inner validation data, and performance was then measured on untouched outer test folds, keeping performance estimates unbiased. On top of that, the researchers applied bootstrap resampling with 2,000 iterations to confirm that results were stable and not artifacts of a particular split of the data.

The most distinctive innovation, however, was threshold optimization. Rather than accepting a default probability cutoff of 0.5, the team deliberately shifted decision thresholds to meet six predefined minimum sensitivity targets, ranging from 0.95 to 0.995. Within each outer fold, thresholds were derived from pooled inner validation predictions, fixed, and then applied unchanged to the test set. The logic is clinical, not statistical: in appendicitis, missing a true case is far more dangerous than operating on a healthy appendix, so a decision support tool must be calibrated to catch virtually every case of appendicitis while identifying the small minority of patients who might safely avoid immediate surgery.

The results were revealing. For detecting acute appendicitis, logistic regression outperformed the more complex algorithms, achieving an area under the ROC curve of 0.765. At the strictest sensitivity target of 0.995, the model detected 99.8 percent of appendicitis cases, with specificity of just 0.038. Translated to a clinical scale, using the observed case distribution, this operating point would miss approximately two appendicitis cases per 1,000 patients while correctly flagging about four patients without appendicitis who could be spared surgery. At the more lenient sensitivity target of 0.95, roughly 41 appendicitis cases per 1,000 would be missed, but approximately 24 patients per 1,000 without appendicitis would avoid an unnecessary operation. The numbers lay bare the inherent trade-off between diagnostic safety and operative selectivity.

For distinguishing complicated from uncomplicated disease, the random forest model performed best, with an AUC of 0.785 and a maximum achievable sensitivity of 0.997 paired with specificity of 0.057. This capability carries real clinical weight: complicated appendicitis generally demands urgent surgery and carries higher morbidity and mortality, whereas some centers now attempt conservative, non-operative management of uncomplicated cases. A model that reliably identifies complicated disease could support more urgent operative decisions, temper enthusiasm for non-operative strategies, or prompt additional imaging and closer monitoring when the predicted risk is ambiguous.

To address the notorious black-box problem that undermines clinical trust in machine learning, the researchers applied SHAP analysis, a framework from explainable artificial intelligence that quantifies each feature’s contribution to individual predictions. For appendicitis detection, neutrophil percentage, the neutrophil-to-lymphocyte ratio, the platelet-to-lymphocyte ratio, sex, and lymphocyte percentage emerged as the most influential predictors. For complication prediction, C-reactive protein, age, symptom duration, and the neutrophil-to-lymphocyte ratio dominated. These rankings align closely with the pathophysiology of appendicitis, in which bacterial invasion of the appendiceal mucosa triggers a systemic inflammatory cascade, and they mirror the feature importances reported by other research groups using different algorithms and populations.

The authors are careful to frame what these models are and are not. The cohort consisted exclusively of adults already selected for surgery by a surgeon’s judgment, so the models should not be treated as stand-alone diagnostic tools for undifferentiated abdominal pain in the emergency department, nor as replacements for ultrasound or CT where those are routinely available. Instead, the researchers envision them as adjunctive decision-support and reassessment tools: a low-risk prediction should trigger repeat examination, laboratory reassessment, or additional imaging rather than discharge, particularly in settings with limited imaging access or after equivocal imaging findings. Notably, the superiority of the simple linear model over tree-based methods in this adult cohort contrasts with findings in pediatric populations, suggesting that inflammatory markers relate to appendicitis in a more linear fashion in adults and underscoring the need for population-specific models.

Limitations remain. The study was retrospective and single-center, and its performance may reflect local diagnostic pathways, surgeon thresholds, and imaging practices that do not transfer to other institutions. The dataset is also surgically enriched, meaning there are no true negative patients who avoided surgery, so specificity must be interpreted conditionally within the operated population. Still, the combination of histopathological verification, rigorous nested cross-validation, bootstrap stability testing, deliberate sensitivity-first threshold calibration, and transparent SHAP-based interpretability marks this work as a methodologically serious step toward machine learning that could meaningfully reduce negative appendectomies. The authors call for prospective, multicenter external validation before clinical deployment, along with future exploration of multimodal models integrating imaging and of tools to help select patients with uncomplicated appendicitis for non-operative management—a direction that could ultimately reshape how one of surgery’s most routine emergencies is decided.

Subject of Research: Machine learning with sensitivity-focused threshold optimization to reduce negative appendectomies in adults with suspected acute appendicitis

Article Title: Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

Article References: Males, I., Kumric, M., Boban, Z., Vrdoljak, J., Pecenkovic, D., Ivanda, M., Grahovac, M., Pogorelic, Z., & Bozic, J. (2026). Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults. Annals of Gastroenterological Surgery, 10(5), 1631-1645. https://doi.org/10.1002/ags3.70225

Image Credits: AI Generated

DOI: 10.1002/ags3.70225

Keywords: machine learning, appendicitis, negative appendectomy, logistic regression, random forest, XGBoost, threshold optimization, SHAP explainability, clinical decision support, C-reactive protein, nested cross-validation, emergency surgery