v333: Proceedings of CHIL 2026

2026-06-30 · Source: Proceedings of Machine Learning Research · Field: Health & Wellbeing — Health & Medical Research, Clinical Care & Medical Practice, Medical Devices & Health Technology · Depth: Expert, medium

Summary

Volume 333 of the Conference on Health, Inference, and Learning (CHIL) 2026 proceedings, held on June 29-30, 2026, at UC Berkeley, presents 33 papers advancing machine learning applications in healthcare. Key research areas include the extensive use of Large Language Models (LLMs) for tasks such as reconstructing sepsis trajectories, multimodal clinical prediction with AgentRx, enhancing diagnostic reasoning, generating SOAP notes, and identifying breast cancer treatment side effects. Other significant contributions cover medical imaging, including 3D neuroimage classification, brachytherapy planning with ALMo, and chest X-ray interpretation via LUNGUAGE. Papers also address electronic health records (EHRs) through synthetic data generation, memory-efficient inference, and handling label imbalance. Further topics explore deep survival analysis, EEG foundation models, single-cell RNA-seq, reinforcement learning for Alzheimer's disease, and hospital workflow simulation with H-AdminSim.

Key takeaway

For AI Scientists and Machine Learning Engineers developing clinical applications, these proceedings highlight the critical need for robust evaluation and specialized benchmarks. You should prioritize developing models that demonstrate strong generalization across diverse datasets and clinical contexts, especially when integrating LLMs for diagnostic reasoning or treatment planning. Consider adopting methods for synthetic data generation and memory-efficient EHR inference to address data scarcity and computational constraints in real-world deployments.

Key insights

Machine learning, particularly LLMs, is rapidly expanding its utility across diverse clinical and administrative healthcare applications.

Principles

• LLMs are versatile for clinical text analysis.
• Benchmarking is crucial for model evaluation.
• Synthetic data aids robustness and privacy.

Method

Papers present methods like LLM-based textual time series reconstruction, hazard factorization for survival analysis, agent-based synthetic EHR generation, and reinforcement learning for treatment optimization.

In practice

• LLMs can reconstruct disease trajectories.
• AI agents predict multimodal clinical outcomes.
• ML optimizes brachytherapy treatment plans.

Topics

• Large Language Models
• Healthcare AI
• Clinical Prediction
• Medical Imaging
• Electronic Health Records
• Benchmarking

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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• LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs — LLMs can generate and evaluate synthetic medical interview data, enabling scalable clinical skills assessment.
• When eight popular large language models (LLMs) answer common parent questions about early maxillary expansion, how reliable are they—and how readable is what they say? — LLM performance in healthcare varies significantly across accuracy, comprehensiveness, and readability, with no single model excelling in all.
• Benchmarking GPT Models for Conversational AI Systems: Can AI Read a Doctor’s Notes? — LLMs struggle with medical abbreviation expansion, especially context-dependent ones, despite larger models performing better.
• MedArena: Comparing LLMs for Medicine-in-the-Wild Clinician Preferences — Real-world clinician preferences reveal that medical LLM utility extends beyond factual recall to include depth, clarity, and clinical nuance.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.