v298: Proceedings of MLHC 2025

2026-06-04 · Source: Proceedings of Machine Learning Research · Field: Health & Wellbeing — Artificial Intelligence & Machine Learning, Health & Medical Research, Clinical Care & Medical Practice · Depth: Expert, medium

Summary

Volume 298 presents the proceedings of the 10th Machine Learning for Healthcare Conference, held on August 15-16, 2025, at the Mayo Clinic in Rochester, MN, USA. This collection features diverse research spanning critical areas of healthcare AI. Key topics include enhancing interpretability and explainability in diagnostic models, such as with Neural Hawkes Process models and uncertainty-aware time series interpretability for critical care. Several papers explore the application of Large Language Models (LLMs) for tasks like rare disease differential diagnosis, clinical text summarization, and question answering, alongside evaluations of their factuality and performance in multidisciplinary team decision-making. Other research focuses on learning continuous latent trajectories from Electronic Health Records for survival prediction, optimizing medical image segmentation with partially annotated data, and developing novel methods for drug optimization and protein representation learning. The volume also addresses fairness in EHR predictions and automated newborn screening.

Key takeaway

For AI Scientists and ML Engineers developing healthcare solutions, you should prioritize model interpretability and fairness alongside predictive accuracy. Consider integrating multimodal data, such as EHRs, medical images, and time-series, to build robust diagnostic and prognostic tools. Explore the potential of Large Language Models and Retrieval-Augmented Generation for clinical text analysis and decision support, while rigorously evaluating their factuality and ethical implications in real-world settings.

Key insights

Advanced ML techniques are transforming healthcare diagnostics, prognostics, and treatment across diverse modalities.

Principles

• Interpretability is crucial for clinical ML adoption.
• Multimodal data integration enhances predictive power.
• Fairness must be engineered into healthcare AI systems.

In practice

• Apply RAG for clinical question answering.
• Use contrastive learning for biobehavioral time-series.
• Implement federated learning for CT segmentation.

Topics

• Machine Learning in Healthcare
• Large Language Models
• Medical Image Analysis
• Model Interpretability
• Electronic Health Records
• Clinical Decision Support

Best for: NLP Engineer, Computer Vision Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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