arXiv Machine Learning

Learning Under Treatment-Induced Label Indeterminacy with Expert Annotations of Counterfactual Outcomes: A Case Study in Neurological Prognostication

arXiv:2608. 12477v1 Announce Type: new Abstract: Clinical prediction models are often developed as if the outcome of interest were cleanly observed for every patient.

arXiv AI

Jul 21

Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare

arXiv:2607. 17508v1 Announce Type: cross Abstract: We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific predictors.

By Sazan Mahbub, Caleb Ellington, Zhiyuan Li, Yixin Yang, Souvik Kundu, Ben Lengerich, Eric P. Xing

rag safety

arXiv AI

Sep 15

Uncertainty-Aware Calibrated Clinical Text Classification with Large Language Models

arXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect pati...

By Mridul Sharma, Adeetya Patel, Zaneta D' Souza, Samira Abbasgholizadeh Rahimi, Siva Reddy, Sreenath Madathil

llms rag nlp benchmarks

arXiv Machine Learning

Jun 2

Model Multiplicity and Predictive Arbitrariness in Recidivism Risk Assessment

arXiv:2606. 02198v1 Announce Type: new Abstract: Prediction tasks over individual futures, which are inherently noisy, often admit multiple similarly accurate models.

By Ashwin Singh, Carlos Castillo

arXiv Machine Learning

Jul 27

Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness

arXiv:2607. 21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice.

By Jonathan Zhang, Erik Skalnes, Jacob Chen, Michael Oberst

arXiv AI

Jul 16

Ask Before You Diagnose: Safe-Psych, a Sequential Evaluation Benchmark for LLMs in Psychiatry

arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.

By Oriana Presacan, Andreea Grama, Larisa Irimin\u{a}, Alireza Nik, Jaya Ojha, Vajira Thambawita, Ciprian I. B\u{a}cil\u{a}, Bogdan Ionescu, Michael A. Riegler

llms benchmarks safety

arXiv AI

Aug 3

EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses

arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.

By Jiahui Li, Ruili Fang, Zishuai Liu, Yutong Guo, Nan Yang, Wenzhan Song, Jin Lu, Fei Dou

llms benchmarks

arXiv AI

Aug 11

When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information

arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.

By Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam, Quan Z. Sheng

llms nlp

arXiv AI

Aug 3

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

arXiv:2607. 28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning.

By Shayndhan Sivanathan, Shravan Nageswaran, Mehdi Zadem, Ryaan Sultan, Nicolas von Mallinckrodt, Max Solovyev, Alexey Matyushkin, Sumon Sadhu, Gabriele C DeLuca, Sanjeeva Jeyaretna, James Hillis, Manoj Ramachandran, Prakash Jayakumar

llms agents safety

arXiv Machine Learning

2d ago

Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting

arXiv:2512.19735v4 Announce Type: replace Abstract: Accurately predicting mortality risk in intensive care unit (ICU) patients is critical for clinical decision-making. Large language models (LLMs) a...

By Gangxiong Zhang, Yongchao Long, Yuxi Zhou, Yong Zhang, Shenda Hong

llms safety

arXiv AI

Aug 6

Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction

arXiv:2605. 24212v2 Announce Type: replace-cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain.

By Siqi Li, Chuan Hong, Ziye Tian, Benjamin Sieu-Hon Leong, Koshi Nakagawa, Hideharu Tanaka, Sang Do Shin, Khuong Quoc Dai, Do Ngoc Son, Marcus Eng Hock Ong, Nan Liu, Molei Liu

fine-tuning safety

arXiv AI

2d ago

TRIAGE: Dialectical LLM Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series

arXiv:2606.09030v2 Announce Type: replace-cross Abstract: Clinical early warning systems built on irregularly sampled medical time series (ISMTS) from electronic health records must deliver continuou...

By Hyeongwon Jang, Gyouk Chu, Changhun Kim, Hangyul Yoon, Jeonguk Lee, Eunho Yang, Joonhyung Park

llms benchmarks

arXiv AI

Jun 3

Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis

arXiv:2606. 02671v1 Announce Type: cross Abstract: Machine learning predictors have become essential tools for guiding automated decision making.

By Itai Zilberstein, Ioannis Anagnostides, Tuomas Sandholm

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