v248: Proceedings of CHIL 2024

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Health & Wellbeing — Healthcare Systems & Policy, Medical Devices & Health Technology, Clinical Care & Medical Practice · Depth: Expert, medium

Summary

Volume 248 compiles the proceedings of the fifth Conference on Health, Inference, and Learning (CHIL) 2024, held at Cornell Tech, featuring cutting-edge research in applying machine learning and AI to diverse healthcare challenges. The papers cover a wide array of topics, including the interpretation of intracardiac electrograms, drug synergy prediction using Graph Neural Networks (DDoS), and adaptive learning from multi-source motion sensor data for daily physical activity monitoring. Significant advancements are presented in medical imaging analysis, such as interpretable breast cancer classification from mammograms, glaucoma detection from OCT data, and view-specific chest X-ray generation using vision-language models. Furthermore, the proceedings highlight the integration of Large Language Models (LLMs) like ChatGPT for improving radiology report analysis and generating faithful patient summaries, alongside the development of Health-LLM for health prediction via wearable sensor data. Critical cross-cutting themes addressed include privacy-preserving machine learning (PriSHA), fairness auditing for medical early-warning systems (FAMEWS), and the regulatory landscape surrounding AI medical device updates, underscoring the complex interplay of technology, ethics, and policy in modern healthcare.

Key takeaway

The CHIL 2024 proceedings showcase over 30 papers on cutting-edge AI/ML applications across health, addressing challenges from diagnostics to patient monitoring and health equity. Key contributions include novel Graph Neural Networks for drug synergy, Vision Transformers for medical imaging, LLMs for radiology and EHR analysis, and advanced wearable data processing for fatigue and activity monitoring. This collection provides essential insights for researchers, clinicians, and policymakers seeking to leverage computational methods for improving patient outcomes and ethical AI deployment in healthcare.

Topics

• Health AI/ML
• Medical Imaging Analysis
• Wearable Sensor Data
• Large Language Models
• Drug Synergy Prediction

Code references

• ahli-org/chil-proceedings-2024
• willxxy/Text-EGM
• uzh-dqbm-cmi/graphnn
• Oceanjinghai/HealthTimeSerial
• MGH-LMIC/LLM_normal

Best for: AI Scientist, Research Scientist, Domain Expert

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• OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction — OOD-GraphLLM uses a GraphLLM to predict drug synergy under out-of-distribution conditions by integrating molecular graph and semantic data.
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• Bridging the interpretability gap for medical artificial intelligence models using class-association manifold learning — CAML enhances medical AI explainability by decoupling decision patterns and visualizing global class-associated knowledge.
• SPOKE: A massive biomedical knowledge graph for precision health and drug discovery — SPOKE integrates vast biomedical data into a knowledge graph for drug discovery and precision medicine.

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