Clinical Decision-Making

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Overview

Clinical decision-making is the process of choosing tests, diagnoses, treatments, and care plans from evidence that may be incomplete or uncertain, with consequences for patient outcomes and resource use. Approaches include clinical prediction models, causal and individualized treatment-effect methods, decision-focused optimization, and sequential policies that balance gathering more information against acting or deferring. Large language models and multi-agent systems are increasingly evaluated for interactive tasks such as history-taking, triage, longitudinal record review, and treatment planning, often with retrieval, uncertainty estimates, or clinician oversight. A central challenge is that strong diagnostic or benchmark scores do not ensure safe, stable, fair decisions in real workflows, especially when evidence is missing, patient context shifts, or the costs of errors differ.

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133 papers21 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

Mar 5, 2026

Bayesian Supervised Causal Clustering

Finding patient subgroups with similar characteristics is crucial for personalized decision-making in various disciplines such as healthcare and policy evaluation. While most existing approaches rely on unsupervised clustering methods, there is a growing trend toward using supervised clustering methods that identify operationalizable subgroups in the context of a specific outcome of interest. We propose Bayesian Supervised Causal Clustering (BSCC), with treatment effect as outcome to guide the clustering process. BSCC identifies homogenous subgroups of individuals who are similar in their covariate profiles as well as their treatment effects. We evaluate BSCC on simulated datasets as well as real-world dataset from the third International Stroke Trial to assess the practical usefulness of the framework.

Luwei Wang, Nazir Lone, Sohan Seth

School of Informatics, University of Edinburgh, Edinburgh, UK · Usher Institute, University of Edinburgh, Edinburgh, UK

Clinical Decision-Making
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Mar 2, 2026

PreSight: Preoperative Outcome Prediction for Parkinson's Disease via Region-Prior Morphometry and Patient-Specific Weighting

Preoperative improvement rate prediction for Parkinson's disease surgery is clinically important yet difficult because imaging signals are subtle and patients are heterogeneous. We address this setting, where only information available before surgery is used, and the goal is to predict patient-specific postoperative motor benefit. We present PreSight, a presurgical outcome model that fuses clinical priors with preoperative MRI and deformation-based morphometry (DBM) and adapts regional importance through a patient-specific weighting module. The model produces end-to-end, calibrated, decision-ready predictions with patient-level explanations. We evaluate PreSight on a real-world two-center cohort of 400 subjects with multimodal presurgical inputs and postoperative improvement labels. PreSight outperforms strong clinical, imaging-only, and multimodal baselines. It attains 88.89% accuracy on internal validation and 85.29% on an external-center test for responder classification and shows better probability calibration and higher decision-curve net benefit. Ablations and analyses confirm the contribution of DBM and the patient-specific weighting module and indicate that the model emphasizes disease-relevant regions in a patient-specific manner. These results demonstrate that integrating clinical prior knowledge with region-adaptive morphometry enables reliable presurgical decision support in routine practice.

Yand Wang, Chen Zhang, Lanyun Zhu +6

Beijing Jiaotong University · Nanyang Technological University · Institute of Medical Technology, Peking University +3

Clinical Outcome PredictionClinical Decision-MakingImportance Weighting
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Mar 1, 2026

TARSE: Test-Time Adaptation via Retrieval of Skills and Experience for Reasoning Agents

Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior example at the right reasoning step. We frame clinical question answering as an agent problem with two explicit, retrievable resources: skills, reusable clinical procedures such as guidelines, protocols, and pharmacologic mechanisms; and experience, verified reasoning trajectories from previously solved cases (e.g., chain-of-thought solutions and their step-level decompositions). At test time, the agent retrieves both relevant skills and experiences from curated libraries and performs lightweight test-time adaptation to align the language model's intermediate reasoning with clinically valid logic. Concretely, we build (i) a skills library from guideline-style documents organized as executable decision rules, (ii) an experience library of exemplar clinical reasoning chains indexed by step-level transitions, and (iii) a step-aware retriever that selects the most useful skill and experience items for the current case. We then adapt the model on the retrieved items to reduce instance-step misalignment and to prevent reasoning from drifting toward unsupported shortcuts. Experiments on medical question-answering benchmarks show consistent gains over strong medical RAG baselines and prompting-only reasoning methods. Our results suggest that explicitly separating and retrieving clinical skills and experience, and then aligning the model at test time, is a practical approach to more reliable medical agents.

Junda Wang, Zonghai Tao, Hansi Zeng +3

University of Massachusetts Amherst, MA, USA · University of Massachusetts Lowell, MA, USA

Clinical QALLM Agent Skill RetrievalTest-Time AdaptationClinical ReasoningClinical Decision-Making
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Feb 27, 2026

Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare

In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk estimates and its impact on treatment decisions. For overparameterized models, now standard in machine learning, a substantial source of variability often goes undetected. Even when the data and model architecture are held fixed, randomness introduced by optimization and initialization can lead to materially different risk estimates for the same patient. This problem is largely obscured by standard evaluation practices, which rely on aggregate performance metrics (e.g., log-loss, accuracy) that are agnostic to individual-level stability. As a result, models with indistinguishable aggregate performance can nonetheless exhibit substantial procedural arbitrariness, which can undermine clinical trust. We propose an evaluation framework that quantifies individual-level prediction instability by using two complementary diagnostics: empirical prediction interval width (ePIW), which captures variability in continuous risk estimates, and empirical decision flip rate (eDFR), which measures instability in threshold-based clinical decisions. We apply these diagnostics to simulated data and GUSTO-I clinical dataset. Across observed settings, we find that for flexible machine-learning models, randomness arising solely from optimization and initialization can induce individual-level variability comparable to that produced by resampling the entire training dataset. Neural networks exhibit substantially greater instability in individual risk predictions compared to logistic regression models. Risk estimate instability near clinically relevant decision thresholds can alter treatment recommendations. These findings that stability diagnostics should be incorporated into routine model validation for assessing clinical reliability.

Elizabeth W. Miller, Jeffrey D. Blume

School of Data Science University of Virginia Charlottesville

Clinical Risk PredictionClinical Decision-Making
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Feb 26, 2026

ClinDet-Bench: Beyond Abstention, Evaluating Judgment Determinability of LLMs in Clinical Decision-Making

Clinical decisions are often required under incomplete information. Clinical experts must identify whether available information is sufficient for judgment, as both premature conclusion and unnecessary abstention can compromise patient safety. To evaluate this capability of large language models (LLMs), we developed ClinDet-Bench, a benchmark based on clinical scoring systems that decomposes incomplete-information scenarios into determinable and undeterminable conditions. Identifying determinability requires considering all hypotheses about missing information, including unlikely ones, and verifying whether the conclusion holds across them. We find that recent LLMs fail to identify determinability under incomplete information, producing both premature judgments and excessive abstention, despite correctly explaining the underlying scoring knowledge and performing well under complete information. These findings suggest that existing benchmarks are insufficient to evaluate the safety of LLMs in clinical settings. ClinDet-Bench provides a framework for evaluating determinability recognition, leading to appropriate abstention, with potential applicability to medicine and other high-stakes domains, and is publicly available.

Yusuke Watanabe, Yohei Kobashi, Takeshi Kojima +3

Kyoto University, Department of Biomedical Data Intelligence · Kyoto University, Department of Cardiovascular Medicine · The University of Tokyo

Clinical Language Model EvaluationLLM Safety BenchmarksLLM AbstentionClinical Decision-Making
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Feb 24, 2026

A Governance and Evaluation Framework for Deterministic, Rule-Based Clinical Decision Support in Empiric Antibiotic Prescribing

Empiric antibiotic prescribing in high-risk clinical contexts often requires decision making under conditions of incomplete information, where inappropriate coverage or unjustified escalation may compromise safety and antimicrobial stewardship. While clinical decision-support systems have been proposed to assist in this process, many approaches lack explicit governance and evaluation mechanisms defining scope, abstention conditions, recommendation permissibility, and expected system behavior. This work specifies a governance and evaluation framework for deterministic clinical decision-support systems operating under explicitly constrained scope. Deterministic behavior is adopted to ensure that identical inputs yield identical outputs, supporting transparency, auditability, and conservative decision support in high-risk prescribing contexts. The framework treats governance as a first-class design component, separating clinical decision logic from rule-based mechanisms that determine whether a recommendation may be issued. Explicit abstention, deterministic stewardship constraints, and exclusion rules are formalized as core constructs. The framework defines an evaluation methodology utilizing a fixed set of synthetic, mechanism-driven clinical cases with predefined expected behavior. This validation process focuses on behavioral alignment with specified rules rather than clinical effectiveness, predictive accuracy, or outcome optimization. Within this protocol, abstention is treated as a correct and intended outcome when governance conditions are not satisfied. The proposed framework provides a reproducible approach for specifying, governing, and inspecting deterministic clinical decision-support systems in empiric antibiotic prescribing contexts where transparency, auditability, and conservative behavior are prioritized.

Francisco José Gárate, Paloma Chausa, Diego Moreno +3

Grupo de Bioingeniería y Telemedicina, ETSI Telecomunicación, Centro de Tecnología Biomedica, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain · Instituto de Investigación Hospital 12 de Octubre (imas12), Hospital Universitario 12 de Octubre, 28041 Madrid, Spain · Innovation Unit, Parc Sanitari Sant Joan de Deu, Sant Boi de Llobregat, Spain; Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain +2

Specification-Based TestingClinical Decision SupportClinical Decision-Making
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Feb 19, 2026

MedClarify: An information-seeking AI agent for medical diagnosis with case-specific follow-up questions

Large language models (LLMs) are increasingly used for diagnostic tasks in medicine. In clinical practice, the correct diagnosis can rarely be immediately inferred from the initial patient presentation alone. Rather, reaching a diagnosis often involves systematic history taking, during which clinicians reason over multiple potential conditions through iterative questioning to resolve uncertainty. This process requires considering differential diagnoses and actively excluding emergencies that demand immediate intervention. Yet, the ability of medical LLMs to generate informative follow-up questions and thus reason over differential diagnoses remains underexplored. Here, we introduce MedClarify, an AI agent for information-seeking that can generate follow-up questions for iterative reasoning to support diagnostic decision-making. Specifically, MedClarify computes a list of candidate diagnoses analogous to a differential diagnosis, and then proactively generates follow-up questions aimed at reducing diagnostic uncertainty. By selecting the question with the highest expected information gain, MedClarify enables targeted, uncertainty-aware reasoning to improve diagnostic performance. In our experiments, we first demonstrate the limitations of current LLMs in medical reasoning, which often yield multiple, similarly likely diagnoses, especially when patient cases are incomplete or relevant information for diagnosis is missing. We then show that our information-theoretic reasoning approach can generate effective follow-up questioning and thereby reduces diagnostic errors by ~27 percentage points (p.p.) compared to a standard single-shot LLM baseline. Altogether, MedClarify offers a path to improve medical LLMs through agentic information-seeking and to thus promote effective dialogues with medical LLMs that reflect the iterative and uncertain nature of real-world clinical reasoning.

Hui Min Wong, Philip Heesen, Pascal Janetzky +2

LMU Munich, Munich, Germany · Munich Center for Machine Learning, Germany · Department of Neuroradiology, Heidelberg University, Heidelberg, Germany

Information GainMedical LLMsClinical Decision-MakingClinical ReasoningLLM Agents
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Dec 8, 2025

CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space

Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform. While world models (WMs) offer a paradigm for generative prediction, existing medical applications remain limited. Existing methods often rely on stochastic diffusion models, focusing on visual reconstruction rather than causal, physiological transitions. Furthermore, in medical domain, models like MeWM typically ignore patient-specific temporal and clinical contexts and lack a feedback mechanism to link predictions to treatment decisions. To address these gaps, we introduce CLARITY, a medical world model that forecasts disease evolution directly within a structured latent space. It explicitly integrates time intervals (temporal context) and patient-specific data (clinical context) to model treatment-conditioned progression as a smooth, interpretable trajectory, and thus generate physiologically faithful, individualized treatment plans. Finally, CLARITY introduces a novel prediction-to-decision framework, translating latent rollouts into transparent, actionable recommendations. CLARITY demonstrates state-of-the-art performance in treatment planning. On the MU-Glioma-Post dataset, our approach outperforms recent MeWM by 12%, and significantly surpasses all other medical-specific large language models.

Tianxingjian Ding, Yuanhao Zou, Chen Chen +2

Institute of Artificial Intelligence, University of Central Florida

Disease Progression ModelingHealthcareClinical Decision SupportClinical Decision-MakingOncologyLatent World Models
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Nov 3, 2025

An Explanation-oriented Inquiry Dialogue Game for Expert Collaborative Recommendations

This work presents a requirement analysis for collaborative dialogues among medical experts and an inquiry dialogue game based on this analysis for incorporating explainability into multiagent system design. The game allows experts with different knowledge bases to collaboratively make recommendations while generating rich traces of the reasoning process through combining explanation-based illocutionary forces in an inquiry dialogue. The dialogue game was implemented as a prototype web-application and evaluated against the specification through a formative user study. The user study confirms that the dialogue game meets the needs for collaboration among medical experts. It also provides insights on the real-life value of dialogue-based communication tools for the medical community.

Qurat-ul-ain Shaheen, Katarzyna Budzynska, Carles Sierra

IIIA, CSIC, Barcelona, Spain · Software Competence Centre Hagenberg, Hagenberg, Austria · Laboratory of The New Ethos, Faculty of Administration and Social Sciences, Warsaw University of Technology, Warsaw, Poland

Explainable Artificial IntelligenceHealthcareMulti-Agent CollaborationClinical Decision-MakingMulti-Agent Communication
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Oct 21, 2025

Timely Clinical Diagnosis through Active Test Selection

There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions. We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framework that integrates Bayesian Experimental Design (BED) with large language models (LLMs) to better emulate real-world diagnostic reasoning. At each step, ACTMED selects the test expected to yield the greatest reduction in diagnostic uncertainty for a given patient. LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data. Clinicians can remain in the loop; reviewing test suggestions, interpreting intermediate outputs, and applying clinical judgment throughout. We evaluate ACTMED on real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use. This represents a step toward transparent, adaptive, and clinician-aligned diagnostic systems that generalize across settings with reduced reliance on domain-specific data.

Silas Ruhrberg Estévez, Nicolás Astorga, Mihaela van der Schaar

University of Cambridge Cambridge, UK

Clinical Decision SupportClinical Decision-MakingClinical Reasoning
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Sep 29, 2025

Evaluating utility of clinical prediction models: A comparison of decision curve analysis and cost curves

Classification models typically predict a binary outcome and use one or more decision thresholds to produce a classification or inform a clinical decision. Decision curves and cost curves are model evaluation approaches that assess net benefit and loss across decision thresholds. In this paper we compare decision curves and cost curves, demonstrating that decision curves are closely related to a specific type of cost curve called a Brier curve. Users of both approaches set the classification threshold assuming calibrated model scores, using the relative clinical importance of correct positive and negative classifications. A given decision curve has a corresponding Brier curve such that reference lines common in each space can be included in either (e.g. the 'treat none' decision curve can be translated into an equivalent 'treat none' Brier curve). We suggest the upper envelope decision curve as a useful comparison to a model decision curve showing the maximum achievable net benefit through recalibration. We show that net benefit (used for decision curves) and Brier loss (used for Brier curves) will always choose the same model as optimal at any given threshold, but cannot be compared or aggregated across thresholds except under specific assumptions; doing so otherwise requires custom weightings.

Louise AC Millard, Matthew Sperrin, Richard M Martin +1

MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom. · Population health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom. · Intelligent Systems Laboratory, University of Bristol, Bristol, United Kingdom.

Clinical PredictionClinical Decision-Making
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Mar 25, 2025

Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM

Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms are ambiguous or common across multiple conditions. In this work, we move beyond correlation to investigate the causal influence of key symptoms-specifically "chest pain" on diagnostic predictions. Leveraging the CausaLM framework, we generate counterfactual text representations in which target concepts are effectively "forgotten" enabling a principled estimation of the causal effect of that concept on a model's predicted disease distribution. By employing Textual Representation-based Average Treatment Effect (TReATE), we quantify how the presence or absence of a symptom shapes the model's diagnostic outcomes, and contrast these findings against correlation-based baselines such as CONEXP. Our results offer deeper insight into the decision-making behavior of clinical NLP models and have the potential to inform more trustworthy, interpretable, and causally-grounded decision support tools in medical practice.

Alexander Romanus, Mehul Shetty, Connor Jordan

Clinical Decision-MakingCausal InterpretabilityCausal InferenceCausal Interventions in Language Models
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Feb 23, 2025

Tool and Trainer? Experimental Evidence on Human Learning from Predictive AI in Cancer Diagnosis

Artificial intelligence (AI) is increasingly deployed to aid professional judgment, yet its implications for the development of human expertise remain unclear. We examine how the availability of AI assistance during training and task execution shapes novices' individual and collective judgment in the context of cancer diagnosis. Across two field experiments at a large teaching hospital, 576 medical novices learned to diagnose lung cancer from CT scans. Study 1 used a 2 * 2 factorial design that randomized AI assistance during training, task execution, both, or neither. Novices who received AI assistance during training achieved higher diagnostic accuracy in both AI-assisted and unaided task execution, with a larger improvement in the former. However, training-phase assistance was associated with more common errors than task-execution-phase assistance, suggesting that while the trainer effect improves individual accuracy, it limits the benefits of aggregating judgments. Study 2 shows that AI assistance during earlier task execution improved subsequent unaided performance and reduced common errors in the unaided phase of task execution, thus revealing a surprisingly beneficial "tool-as-trainer" effect. These findings suggest that, contingent on the right deployment strategy, AI assistance can support individual expertise development and improve collective judgment by reducing common errors across individuals.

Vivianna Fang He, Sihan Li, Phanish Puranam +1

UCL School of Management, University College London; London, United Kingdom. · INSEAD; Singapore. · West China Hospital, Sichuan University; Chengdu, China.

Human-AI CollaborationClinical Decision-Making
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