The Role of Empathy in Critical Reasoning and the Limitations of Medical AI Systems
Bibliographic record
Abstract
The recent developments of medical AI systems (MAIS) open up questions as to whether and to what extent MAIS can be modeled to include empathetic understanding, as well as what impact MAIS' lack of empathetic understanding would have on its ability to perform the necessary critical analyses for reaching a diagnosis and recommending medical treatment. In this article, we argue that current medical AI systems' ability to empathize with patients is severely limited due to its lack of first-person experiences with human interests and that efforts to correct for this deficit-by having MAIS interpret patients' medical and non-medical interests-will encounter significant obstacles. Finally, we demonstrate how MAIS' lack of empathy is likely to hinder its performance in crucial aspects of the processes through which useful medical diagnoses are reached and through which appropriate treatment recommendations for patients are determined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".