Pith. sign in

REVIEW 2 major objections 6 minor 29 references

No current large language model has demonstrated safe autonomous triage of undifferentiated patients.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Passing medical exams does not make LLMs safe for autonomous triage: they fail to seek missing red flags, and current benchmarks do not test that.

T0 review reviewed 2026-08-03 challenge →

load-bearing objection A well-argued evidence-gap perspective with a useful new frame; the causal training-objective claim is the soft underbelly, but the safety case doesn't rest on it. the 2 major comments →

arxiv 2607.28677 v1 pith:E6R4RZBV submitted 2026-07-29 cs.AI

Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support

Shayndhan Sivanathan , Shravan Nageswaran , Mehdi Zadem , Ryaan Sultan , Nicolas von Mallinckrodt , Max Solovyev , Alexey Matyushkin , Sumon Sadhu
show 5 more authors
Gabriele C DeLuca Sanjeeva Jeyaretna James Hillis Manoj Ramachandran Prakash Jayakumar
This is my paper
classification cs.AI
keywords large language modelsclinical decision supportautonomous triagepatient safetyreasoning from silencebenchmark validityasymmetric costmedical reasoning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This Perspective argues that the evidence used to claim large language models are ready for autonomous triage measures the wrong thing. Passing medical exams and scoring high on curated cases shows knowledge, not the ability to act safely when a patient's story is incomplete. The paper's core claim is that models trained to continue the most probable text are not optimized to seek the missing red flag, broaden the differential, or escalate when a must-not-miss diagnosis remains unexcluded. Current benchmarks supply complete, pre-cleaned histories and therefore cannot see this failure. The paper concludes that autonomous triage should not be deployed until systems pass evaluations that withhold information, score asymmetric harm, and validate simulators against real outcomes.

Core claim

The paper's central claim is that the safety gap for autonomous triage is not medical knowledge but an objective mismatch: an LLM optimized to predict the most probable next token and to be helpful has no native representation of the cost of missing a rare, dangerous diagnosis. Under incomplete patient histories, models fail at the behaviors safe triage requires—widening the differential, seeking the missing red flag, lowering the threshold for escalation, and deferring when information is insufficient. Because existing evaluations use complete, expertly curated cases and confidence-gated populations that exclude the ambiguous patient, they cannot detect this failure. The paper proposes that

What carries the argument

The central mechanism is 'reasoning from silence': making safe inferences from diagnostically informative missing information—recognizing that what a patient has not said may reflect incomplete elicitation rather than true absence of disease. The paper uses this concept to explain why next-token-prediction training fails triage: the model conditions on what is present, not on what is absent, so absence exerts no force on confidence. The other load-bearing piece is the proposed evaluation design: withhold information, score the right objective with asymmetric cost, and validate the simulator in three layers (face, distributional, predictive).

Load-bearing premise

The argument depends on the premise that the training objective—predicting the most probable next token and being helpfully agreeable—is what causes models to ignore missing red flags, so that no amount of prompting or wrapping can fully fix the deficit.

What would settle it

A prospective randomized trial in a real emergency or primary-care setting where an LLM triages undifferentiated, incompletely disclosing patients, with the model required to ask for missing details and escalate high-harm cases; if the model's under-triage rate of must-not-miss diagnoses is comparable to or better than clinicians' on the same encounters, the central 'evidence does not exist' claim would be overturned for that system. A cheaper check: measure whether a model's differential width widens and its confidence falls as a fixed history is systematically reduced from complete to 20% co

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If correct, exam-passing and curated-benchmark accuracy cannot be used as evidence of readiness for autonomous triage, no matter how high the scores.
  • Deploying LLM symptom checkers or autonomous primary-care systems without evidence from incomplete-information, harm-weighted evaluations risks confident, plausible answers that close the case before a must-not-miss diagnosis is excluded.
  • Assistant-like behaviors—credulity, agreeableness, miscalibration—compound the core deficit, so evaluations must test resistance to minimized and adversarial patient histories, not just accuracy.
  • The paper's proposed standard—withhold information, score asymmetric cost, validate simulators—offers a concrete precondition that any autonomous triage claim must meet before it could justify deployment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the objective-mismatch account is right, then scale alone will not close the gap; the field may need a different training signal that rewards information seeking and harm-weighted deferral, not just helpfulness.
  • The same reasoning-from-silence logic likely applies beyond triage—to any high-stakes LLM decision under incomplete inputs, where the unstated detail is the one that matters.
  • A testable extension: build a benchmark that systematically varies information completeness and measures whether a model's differential width and escalation threshold respond to missingness; the paper predicts they will not.
  • The paper leaves open whether structured elicitation policies layered on top of current models can compensate; that is testable and is not settled by the paper's argument.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. This Perspective argues that no current evidence demonstrates that LLMs can safely perform autonomous triage of self-presenting, undifferentiated patients without a clinician in the loop. The paper distinguishes knowledge-oriented benchmarks (licensing exams, curated vignettes) from the real task of sequential decision-making under incomplete histories and asymmetric error costs, and argues that existing evaluations—including those of AMIE and autonomous primary-care systems—pre-filter patients, supply complete histories, or confidence-gate outputs, so they cannot detect failures of 'reasoning from silence.' It also identifies a cluster of assistant-like behaviors (credulity, agreeableness, miscalibration) that compound the deficit, and proposes three evaluation requirements: withhold information by design, score harm-weighted behaviors rather than top-k accuracy, and validate synthetic simulators through face, distributional, and predictive validity.

Significance. The evidence-gap claim is well supported: the paper marshals a randomized study showing a drop from 94.9% to <34.5% condition identification when real users provide histories [2], and two systematic reviews showing that only a small fraction of LLM evaluations use real-world prospective data [15,16]. If accepted, the paper provides a timely caution against premature deployment and a concrete, operationalizable evaluation agenda. Although it is a perspective rather than a new empirical study, its strengths include a falsifiable central claim, explicit positive proposals, and a clear breakdown of how specific design choices (pre-screening, confidence gating, complete histories) hide tail risks. The mechanistic explanation of the deficit is the least supported part, but it is not required for the central claim.

major comments (2)
  1. [Sections 1 and 2] The paper presents a causal claim as established fact: 'a model trained to continue the most probable text has no native representation of the danger it was never told about' (§2), and 'the deeper problem is what the substrate was never built to do' (§1). The cited evidence ([11], [12]) demonstrates that current models behave this way, but it does not show that the training objective itself prevents mitigation; §5 acknowledges that structured elicitation policies exist (ref. 24). Since the central evidence-gap claim does not depend on this mechanistic explanation, please frame it as a hypothesis ('we hypothesize that...') rather than as a settled property of LLMs.
  2. [Declarations] 'All authors declare no conflict of interest' appears inconsistent with the author affiliations listing Atman Labs, a commercial AI company (author block, lines 3-4). Employment, equity, or funding from Atman Labs is a financial interest highly relevant to a Perspective arguing for stricter evidence before autonomous triage deployment. This must be disclosed or explicitly rebutted before publication.
minor comments (6)
  1. [Section 1] The sentence reporting '94.9% ... but fewer than 34.5%' could clarify that both percentages refer to condition-identification rates, and that the first is on curated inputs while the second is on real-user dialogue; the current phrasing is slightly ambiguous.
  2. [Section 2] Reference [11] is a medRxiv preprint; the 1,000-consultation stress-test and the 97.5% accuracy figure rest on it. Please mark it as a preprint and temper 'a large stress-test confirms' to reflect the provisional status.
  3. [Section 5] 'This journal's editors have put it more plainly still' is ambiguous because the manuscript does not identify the target journal; specify that ref. [29] is a Nature Medicine editorial.
  4. [Section 5] 'Even the most advanced dynamic simulators still furnish the clinical facts' is unclear; specify that the simulator provides the clinical facts to the model rather than requiring the model to elicit them.
  5. [Declarations] The data availability statement claims a dataset 'used for this review' that will be shared; as this is a perspective with no original dataset, remove or clarify this statement.
  6. [Section 1] Consider defining 'autonomous triage' explicitly (degree of clinician backstop, setting, and population) at first use, since the paper discusses consumer symptom checkers, autonomous primary care systems, and ED agents, which differ in autonomy and risk.

Circularity Check

0 steps flagged

No significant circularity: the safety-evidence-gap claim rests on external studies and is not derived from self-referential definitions, fitted parameters, or load-bearing self-citations.

full rationale

This is a Perspective/argumentative review, not a formal derivation. The central claim is that evidence of safety for autonomous LLM triage does not yet exist, and that claim is supported by external empirical studies and systematic reviews (e.g., refs 2, 6–10, 15, 16), not by equations or fitted parameters. The mechanistic assertion about next-token prediction is explanatory, not a logical consequence of the evidence-gap claim, and the paper explicitly acknowledges that elicitation policies exist (refs 23–24) while arguing that they require proper evaluation. The only self-citation is ref [5] (Hillis & Payne), which is used for the peripheral point that human involvement should be deliberately specified along an autonomy spectrum; it does not support the central safety-evidence claim. No step in the paper reduces a prediction to an input by construction. Therefore no circularity is present.

Axiom & Free-Parameter Ledger

0 free parameters · 4 axioms · 0 invented entities

The paper introduces no fitted parameters or invented physical/conceptual entities in the sense of new postulates. Its central argument rests on domain assumptions about LLM training objectives, the information structure of triage, and the representativeness of cited studies. The 'asymmetric cost' of triage is invoked conceptually but never assigned a numeric value, so it is not a fitted parameter.

axioms (4)
  • domain assumption An LLM trained for next-token continuation and helpfulness does not, by default, optimize for safe action under asymmetric cost or actively seek missing information.
    Central mechanistic claim in §1–2; argued from training objectives, not empirically or formally demonstrated in this paper.
  • domain assumption In autonomous triage, the decisive red flag is often not volunteered by the patient and must be actively elicited.
    Assumed throughout §2, e.g., the thunderclap-headache case; no new empirical evidence is presented here.
  • domain assumption Existing evaluation studies are representative enough to conclude that curated benchmark performance does not transfer to incomplete-history triage.
    Inferred from cited studies [2,6–8,15,16]; the paper does not independently reanalyze those datasets.
  • ad hoc to paper Simulator validity is established by face, distributional, and predictive validity.
    This three-layer validation hierarchy is the paper's own proposal, not an established evaluation standard.

reviewed 2026-08-03 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support." pith.science (2026). https://pith.science/paper/E6R4RZBV

@misc{pith2026260728677,
  author       = {Pith},
  title        = {Pith review of: Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E6R4RZBV}},
  note         = {Machine review of arXiv:2607.28677}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning. These developments have accelerated the use of LLMs for symptom assessment and clinical decision support in diagnostic and treatment guidance, administrative documentation, and rules-based alert enhancement. This Perspective concerns the most consequential of these applications: the autonomous triage of self-presenting, undifferentiated patients, with little or no clinician in the loop. For that task, the evidence of safety does not yet exist. The gap is not in medical knowledge but in the fidelity of clinical evaluation: a model optimized to continue the most probable text is not optimized to act safely when the safe answer is the improbable must-not-miss diagnosis. Safe triage is not the selection of the most likely diagnosis; it is a sequential decision under asymmetric cost, in which the single catastrophic miss outweighs many false alarms, and the decisive signal may be one the patient has not volunteered - and that the model has not been trained to seek. The core deficit is therefore one of information gathering under uncertainty. Under incomplete histories, LLM systems may fail to show the behaviors safe triage requires: broadening the differential; seeking the missing red flag; lowering the threshold for escalation; deferring judgement until sufficient information is obtained; and escalating concern where high-harm diagnoses remain unexcluded. These modes of failure for LLMs can be difficult to detect considering that evaluations to date often use complete, well-curated, confidence-gated simulations. The application of LLMs under these conditions may be amplified by assistant-like behaviors and positive bias, including credulity, agreeableness, and miscalibration - when these are not constrained by clinical triage logic.

Figures

Figures reproduced from arXiv: 2607.28677 by Alexey Matyushkin, Gabriele C DeLuca, James Hillis, Manoj Ramachandran, Max Solovyev, Mehdi Zadem, Nicolas von Mallinckrodt, Prakash Jayakumar, Ryaan Sultan, Sanjeeva Jeyaretna, Shayndhan Sivanathan, Shravan Nageswaran, Sumon Sadhu.

Figure 1
Figure 1. Figure 1: Benchmark performance does not test the capabilities on which safe triage depends. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: What benchmarks supply versus what safe triage requires. Across six dimensions (informa [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Three requirements for an evidence base that could justify autonomous triage. [ [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

29 extracted references · 2 linked inside Pith

  1. [1]

    Performance of a large language model on the reasoning tasks of a physician

    Brodeur PG, Buckley TA, Kanjee Z, Goh E, Ling EB, Jain P, et al. Performance of a large language model on the reasoning tasks of a physician. Science. 2026;392(6797):524-7

    2026

  2. [2]

    Reliability of LLMs as medical assistants for the general public: a randomized preregistered study

    Bean AM, Payne RE, Parsons G, Kirk HR, Ciro J, Mosquera-Gómez R, et al. Reliability of LLMs as medical assistants for the general public: a randomized preregistered study. Nat Med. 2026;32(2):609-15

    2026

  3. [3]

    Measuring what Matters: Construct Validity in Large Language Model Benchmarks2025 November 3, 2025

    Bean AM, Kearns RO, Romanou A, Hafner FS, Mayne H, Batzner J, et al. Measuring what Matters: Construct Validity in Large Language Model Benchmarks2025 November 3, 2025

    2025

  4. [4]

    On the robustness of medical term representations in locally deployable language models

    Auger SD, Graham NSN, Scott G. On the robustness of medical term representations in locally deployable language models. medRxiv. 2026

    2026

  5. [5]

    Health AI needs meaningful human involvement: lessons from war

    Hillis JM, Payne K. Health AI needs meaningful human involvement: lessons from war. Nat Med. 2024;30(12):3397-8

    2024

  6. [6]

    From Concept to Clinic: Real World Evidence for Autonomous AI Deployment in Primary Care Telemedicine

    Saenz A, Schumacher E, Naik D, Khosla N, Kannan A. From Concept to Clinic: Real World Evidence for Autonomous AI Deployment in Primary Care Telemedicine. medRxiv. 2026:2026.03.18.26348749

    2026

  7. [7]

    Towards autonomous medical artificial intelligence agents

    Ferber D, Hilgers L, Höper C, Kinny-Köster B, Eckardt JN, Egger-Heidrich K, et al. Towards autonomous medical artificial intelligence agents. Nature. 2026. 12

    2026

  8. [8]

    A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

    Brodeur P, Koshy JM, Palepu A, Saab K, Homiar A, Ruparel R, et al. A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic. arXiv. 2026;2603.08448v3

    arXiv 2026

  9. [9]

    Safety of a large language model-based clinical decision support system in African primary healthcare

    Agweyu A, Mwaniki P, Musau W, Korom R, Isaaka L, Wanyama C, et al. Safety of a large language model-based clinical decision support system in African primary healthcare. Nat Health. 2026;1(6):607-18

    2026

  10. [10]

    AI-based Clinical Decision Support for Primary Care: A Real-World Study2025 22 Jul 2025; (arXiv:2507.16947v1 [cs.CL])

    Korom R, Kiptinness S, Adan N, Said K, Ithuli C, Rotich O, et al. AI-based Clinical Decision Support for Primary Care: A Real-World Study2025 22 Jul 2025; (arXiv:2507.16947v1 [cs.CL]). Available from: https://arxiv.org/abs/2507.16947

    Pith/arXiv arXiv 2025

  11. [11]

    Medical errors in large language models revealed using 1,000 synthetic clinical transcripts

    Auger SD, Scott G. Medical errors in large language models revealed using 1,000 synthetic clinical transcripts. medRxiv. 2026:2026.03.23.26349082

    2026

  12. [12]

    Large Language Models lack essential metacognition for reliable medical reasoning

    Griot M, Hemptinne C, Vanderdonckt J, Yuksel D. Large Language Models lack essential metacognition for reliable medical reasoning. Nat Commun. 2025;16(1):642

    2025

  13. [13]

    Towards conversational diagnostic artificial intelligence

    Tu T, Schaekermann M, Palepu A, Saab K, Freyberg J, Tanno R, et al. Towards conversational diagnostic artificial intelligence. Nature. 2025;642(8067):442-50

    2025

  14. [14]

    Towards Conversational AI for Disease Management

    Liévin V, Palepu A, Weng WH, Saab K, Stutz D, Cheng Y, et al. Towards Conversational AI for Disease Management. Nature. 2026

    2026

  15. [15]

    Testing and Evalua- tion of Health Care Applications of Large Language Models: A Systematic Review

    Bedi S, Liu Y, Orr-Ewing L, Dash D, Koyejo S, Callahan A, et al. Testing and Evalua- tion of Health Care Applications of Large Language Models: A Systematic Review. Jama. 2025;333(4):319-28

    2025

  16. [16]

    LLM-assisted systematic review of large language models in clinical medicine

    Chen SF, Alyakin A, Seas A, Yang E, Choi JJ, Lee JV, et al. LLM-assisted systematic review of large language models in clinical medicine. Nat Med. 2026;32(3):1152-9

    2026

  17. [17]

    Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support

    Omar M, Sorin V, Collins JD, Reich D, Freeman R, Gavin N, et al. Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support. Commun Med (Lond). 2025;5(1):330

    2025

  18. [18]

    MedMisBench: Measuring Epistemic Resilience of LLMs Under Misleading Medical Context

    Zhou H, Zou X, Wu J, Wu S, Wu J, Segal BM, et al. MedMisBench: Measuring Epistemic Resilience of LLMs Under Misleading Medical Context. bioRxiv. 2026:2026.05.25.727671

    2026

  19. [19]

    Training language models to be warm can reduce accuracy and increase sycophancy

    Ibrahim L, Hafner FS, Rocher L. Training language models to be warm can reduce accuracy and increase sycophancy. Nature. 2026;652(8112):1159-65

    2026

  20. [20]

    Competing Biases underlie Overconfidence and Underconfidence in LLMs

    Kumaran D, Fleming SM, Markeeva L, Heyward J, Banino A, Mathur M, et al. Competing Biases underlie Overconfidence and Underconfidence in LLMs. Nature Machine Intelligence. 2026;8(4):614-27

    2026

  21. [21]

    Assessment of Large Language Models in Clinical Reasoning: A Novel Benchmarking Study

    McCoy LG, Swamy R, Sagar N, Wang M, Bacchi S, Fong JMN, et al. Assessment of Large Language Models in Clinical Reasoning: A Novel Benchmarking Study. NEJM AI. 2025;2(10):AIdbp2500120

    2025

  22. [22]

    First, do NOHARM: towards clinically safe large language models

    Wu D, Haredasht FN, Maharaj SK, Jain P, Tran J, Gwiazdon M, et al. First, do NOHARM: towards clinically safe large language models. ArXiv. 2025

    2025

  23. [23]

    MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning

    Li SS, Balachandran V, Feng S, Ilgen JS, Pierson E, Koh PW, et al. MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning. 2024

    2024

  24. [24]

    SymptomAI: Toward a Con- versational AI Agent for Everyday Symptom Assessment

    Breda J, Yousif F, Hawkins B, Cotoi M, Liu M, Luo R, et al. SymptomAI: Toward a Con- versational AI Agent for Everyday Symptom Assessment. arXiv preprint arXiv:260504012. 2026

    2026

  25. [25]

    A POMDP Formulation of Preference Elicitation Problems

    Boutilier C. A POMDP Formulation of Preference Elicitation Problems. In: Dechter R, Kearns MJ, Sutton RS, editors. Proceedings of the Eighteenth National Conference on Artificial 13 Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence; July 28-August 1, 2002; Edmonton, Alberta, Canada: AAAI Press; 2002. p. 239-46

    2002

  26. [26]

    A clinical environment simulator for dynamic AI evaluation

    Luo L, Kim SE, Zhang X, Kernbach JM, Kenia R, Acosta JN, et al. A clinical environment simulator for dynamic AI evaluation. Nat Med. 2026;32(3):820-7

    2026

  27. [27]

    LLMs Don’t Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations2025 2025-09-11

    Mayne H, Kearns RO, Yang Y, Bean AM, Delaney E, Russell C, et al. LLMs Don’t Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations2025 2025-09-11. Available from: https://arxiv.org/abs/2509.09396

    Pith/arXiv arXiv 2025

  28. [28]

    AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence

    Angus DC, Khera R, Lieu T, Liu V, Ahmad FS, Anderson B, et al. AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence. JAMA. 2025;334(18):1650-64

    2025

  29. [29]

    Nature Medicine

    Show us the evidence for the value of medical AI. Nature Medicine. 2026;32(4):1163-. 14

    2026

This paper was first reviewed by deepseek-v4-flash on August 3, 2026.