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Medical AI: are Osler's principles lost in translation?

Artificial intelligence (AI) is poised to transform clinical practice, but its adoption coincides with a longstanding decline in bedside medicine. Herein, we argue that reintroducing Oslerian principles into medical training may be essential to ensure that technological progress does not come at the expense of the physician-patient relationship. Throughout, we use the term AI to refer to tools built on algorithms, from risk calculators and predictive models to large language models, that produce predictions, cla…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-2181· cite & verify
Medical AI: are Osler's principles lost in translation?
Hospital Universitari Doctor Peset, València 10 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

In brief

This perspective article in Frontiers in Medicine argues that AI adoption in clinical practice coincides with a long decline in bedside medicine. The authors use William Osler's principles of direct patient observation and the doctor-patient relationship as a lens for judging how algorithm-based tools should be used in training and care.

The concern matters because the authors say AI outputs depend on what a physician has observed and recorded, so a hurried examination can yield precise-looking but wrong results. The piece is an opinion and does not measure outcomes of AI deployment, so how far the predicted erosion is occurring remains an open question.

Main points

  1. The authors define AI broadly as algorithm-based tools, from risk calculators to large language models, that have no agency and depend on how institutions deploy them.
  2. They argue AI cannot replace clinical observation, because algorithms treat anything missing from the record as nonexistent and can create an illusion of certainty.
  3. A time-and-motion study cited in the article found physicians spend nearly two hours on records and desk work for every hour of face time with patients.
  4. In hematologic malignancies, clinicians increasingly review algorithmic outputs at screens and may decide on treatment before examining the patient.
  5. The article says patients now arrive with interpretations shaped by AI tools, which increase knowledge but also expose them to misinformation.

The problem

Where AI tools are deployed carelessly in clinics, physicians may decide on treatment from algorithmic outputs before examining the patient, deepening an existing decline in bedside medicine.

The rundown

The article states that AI can synthesize literature, flag imaging and pathology patterns, draft notes, triage risk and suggest treatments, and says this is no longer in serious dispute. It nonetheless holds that AI will not replace the personal relationship between doctor and patient in the foreseeable future, because models are trained on the past.

The authors place the erosion of bedside medicine in a longer history: specialization around organ systems fragmented the view of the whole patient, and growing administrative and documentation burden reduced patient time. Studies of internal medicine trainees are cited as showing a decline in direct patient time for well over a decade.

Looking ahead, the authors say institutional incentives favor early, efficient decisions and that physicians may favor faster, less cognitively taxing workflows. They call for reintroducing Oslerian principles into medical training so that physicians pair AI with sharper listening, observation and physical examination skills.

Sources

  1. Peer-reviewedFrontiers in Medicine2026-10-22

The debate