Clinical Decision Support

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14 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space
Dec 8, 2025Tianxingjian Ding, Yuanhao Zou, Chen Chen +2Healthcare Clinical Decision Support
Institute of Artificial Intelligence, University of Central Florida
JEEVHITAA -- An HCAI Ecosystem to Support Collective Care
Dec 6, 2025Shyama Sastha Krishnamoorthy Srinivasan, Harsh Pala, Mohan Kumar +1Healthcare Clinical Decision Support
IIIT-Delhi New Delhi, India · RIT, NY Rochester, New York, US
First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations
Dec 1, 2025David Wu, Fateme Nateghi Haredasht, Saloni Kumar Maharaj +54Healthcare Clinical Decision Support
MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support
Nov 20, 2025Elias Hossain, Md Mehedi Hasan Nipu, Maleeha Sheikh +5Selective Prediction Healthcare
College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA · Department of Computer Science and Engineering, North South University, Dhaka 1229, Bangladesh · Department of Electrical and Computer Engineering, Purdue University Fort Wayne, Fort Wayne, IN 46805, USA +4
Timely Clinical Diagnosis through Active Test Selection
Date pendingSilas Ruhrberg Estévez, Nicolás Astorga, Mihaela van der SchaarClinical Decision Support Clinical Decision-Making
University of Cambridge Cambridge, UK
NSIDDx: A Design Framework for Neuro-Symbolic, Practitioner-First Differential Diagnosis in Low-Resource Settings
Date pendingAarav SinghRare Disease Diagnosis Healthcare
IIIT Naya Raipur

Dec 8, 2025, cs.LGJ/K move · Enter open · S save

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

Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah+1

Institute of Artificial Intelligence, University of Central Florida

Healthcare Clinical Decision Support Clinical Decision-Making Oncology Latent World Models

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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.

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Latest papers 156

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

JEEVHITAA -- An HCAI Ecosystem to Support Collective Care

First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations

MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support

Timely Clinical Diagnosis through Active Test Selection

NSIDDx: A Design Framework for Neuro-Symbolic, Practitioner-First Differential Diagnosis in Low-Resource Settings