Awesome Machine Learning for Healthcare

A curated list of machine learning papers for healthcare, with an emphasis on natural language processing, multimodal learning, electronic health records, medical agents, and clinical AI evaluation.

See CONTRIBUTING.md for the curation and year-grouping policy.

Table of Contents

• Large Language Models
• Medical Agents
• Synthetic Data Generation
• Data Representation and Predictive Modeling
• Toward a Natural Language Interface for EHRs
• Digital Scribing and Clinical Documentation
• Fact Checking
• Medical Imaging
• Multimodal Large Language Models

Large Language Models

Through 2023

• MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering | CHIL' 22
• Med-HALT: Medical Domain Hallucination Test for Large Language Models | CoNLL' 23
• MEDITRON-70B: Scaling Medical Pretraining for Large Language Models | arXiv, 2023/11

2024

• Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data | CHIL' 24
• K-QA: A Real-World Medical Q&A Benchmark | BioNLP' 24
• Small language models learn enhanced reasoning skills from medical textbooks | npj Digital Medicine, 2025
• Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare | NeurIPS GenAI4Health Workshop' 24
• MedReadMe: A Systematic Study for Fine-grained Sentence Readability in Medical Domain | EMNLP' 24

Medical Agents

Through 2023

• MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning | Findings of ACL' 24

2024

• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator | COLING' 25
• AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning | Nature Communications, 2025
• MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making | NeurIPS' 24
• Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents | arXiv, 2024/05
• AgentClinic: a multimodal benchmark for tool-using clinical AI agents | npj Digital Medicine, 2026
• DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge | IMWUT, 2024
• ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World | NeurIPS Datasets and Benchmarks' 25
• MMedAgent: Learning to Use Medical Tools with Multi-modal Agent | Findings of EMNLP' 24
• MEDCO: Medical Education Copilots Based on a Multi-agent Framework | ECCV Workshops' 24
• Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions | arXiv, 2024/08

Synthetic Data Generation

Through 2023

• Data-driven approach for creating synthetic electronic medical records | BMC Medical Informatics and Decision Making, 2010
• Generating Multi-label Discrete Patient Records using Generative Adversarial Networks | MLHC' 17
• EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models | TMLR, 2024
• Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model | Nature Communications, 2023
• EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records | npj Digital Medicine, 2023
• LLMSYN: Generating Synthetic Electronic Health Records Without Patient-Level Data | MLHC' 24

Data Representation and Predictive Modeling

Through 2023

• GenHPF: General Healthcare Predictive Framework for Multi-Task Multi-Source Learning | IEEE JBHI, 2024

2024

• REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models | arXiv, 2024/02
• EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation | CIKM' 24
• EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models | AMAI Workshop @ MICCAI' 24

Multimodal Representation Learning

Through 2023

• MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images | MLHC' 22
• Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attention | MLHC' 23

2024

• From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR | CHIL' 24
• FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction | KDD' 24
• MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models | CIKM' 24
• Multimodal Patient Representation Learning with Missing Modalities and Labels | ICLR' 24

Toward a Natural Language Interface for EHRs

Through 2023

• Toward a Natural Language Interface for EHR Questions | AMIA' 15
• Annotating Logical Forms for EHR Questions | LREC' 16
• A Semantic Parsing Method for Mapping Clinical Questions to Logical Forms | AMIA' 17
• emrQA: A Large Corpus for Question Answering on Electronic Medical Records | EMNLP' 18
• Text-to-SQL Generation for Question Answering on Electronic Medical Records | WWW' 20
• Using FHIR to Construct a Corpus of Clinical Questions Annotated with Logical Forms and Answers | AMIA' 19
• Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing | LREC' 20
• Paraphrasing to improve the performance of Electronic Health Records Question Answering | AMIA' 20
• Knowledge Graph-based Question Answering with Electronic Health Records | MLHC' 21
• emrKBQA: A Clinical Knowledge-Base Question Answering Dataset | BioNLP' 21
• Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture | ML4H' 21
• Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records | CHIL' 22
• DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries | LREC' 22
• Learning to Ask Like a Physician | ClinicalNLP' 22
• RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports | LREC' 22
• EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records | NeurIPS Datasets and Benchmarks' 22 | Code
• LeafAI: query generator for clinical cohort discovery rivaling a human programmer | JAMIA, 2023
• Toward a Neural Semantic Parsing System for EHR Question Answering | AMIA' 23
• quEHRy: a question answering system to query electronic health records | JAMIA, 2023
• ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram | NeurIPS Datasets and Benchmarks' 23
• MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records | AAAI' 24
• Adapted large language models can outperform medical experts in clinical text summarization | Nature Medicine, 2024
• Question Answering for Electronic Health Records: Scoping Review of Datasets and Models | JMIR, 2024
• EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images | NeurIPS Datasets and Benchmarks' 23 | Code | PhysioNet

2024

• EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records | EMNLP' 24 | Code
• EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries | NeurIPS Datasets and Benchmarks' 24 | Code | PhysioNet
• A dataset and benchmark for hospital course summarization with adapted large language models | JAMIA, 2025

Digital Scribing and Clinical Documentation

Reviews and Perspectives

Through 2023

• The digital scribe | npj Digital Medicine, 2018
• Challenges of developing a digital scribe to reduce clinical documentation burden | npj Digital Medicine, 2019
• The digital scribe in clinical practice: a scoping review and research agenda | npj Digital Medicine, 2021

2024

• The Utility and Implications of Ambient Scribes in Primary Care | JMIR AI, 2024

2025

• Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review | JMIR AI, 2025
• Beyond human ears: navigating the uncharted risks of AI scribes in clinical practice | npj Digital Medicine, 2025
• Policy brief: ambient AI scribes and the coding arms race | npj Digital Medicine, 2025

2026

• Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings | npj Digital Medicine, 2026

Dialogue-to-Note Generation

Through 2023

• Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations | MLHC' 20
• Towards Automating Medical Scribing: Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization | NLP-MC Workshop' 21
• Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques | ACL-IJCNLP' 21
• User-Driven Research of Medical Note Generation Software | NAACL' 22
• An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters | EACL' 23

2024

• Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes | ClinicalNLP' 24

2025

• Evaluation and practical application of prompt-driven ChatGPTs for EMR generation | npj Digital Medicine, 2025
• Enhancing clinical documentation with voice processing and large language models: a study on the LAOS system | npj Digital Medicine, 2025

2026

• Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization | arXiv, 2026/04

Datasets and Benchmarks

Through 2023

• PriMock57: A Dataset Of Primary Care Mock Consultations | ACL' 22
• Aci-bench: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation | Scientific Data, 2023
• Overview of the MEDIQA-Chat 2023 Shared Tasks on the Summarization & Generation of Doctor-Patient Conversations | ClinicalNLP' 23

2025

• MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs | arXiv, 2025/08

Ambient Scribe Deployment and Evaluation

2024

• The association between use of ambient voice technology documentation during primary care patient encounters, documentation burden, and provider burnout | Family Practice, 2024
• The impact of nuance DAX ambient listening AI documentation: a cohort study | JAMIA, 2024
• Use of an ambient artificial intelligence tool to improve quality of clinical documentation | Future Healthcare Journal, 2024
• Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study | JMIR AI, 2024
• Impact of an Artificial Intelligence-Based Solution on Clinicians' Clinical Documentation Experience: Initial Findings Using Ambient Listening Technology | Journal of General Internal Medicine, 2024
• AI-Powered Clinical Documentation and Clinicians' Electronic Health Record Experience: A Nonrandomized Clinical Trial | JAMA Network Open, 2024

2025

• An evaluation framework for ambient digital scribing tools in clinical applications | npj Digital Medicine, 2025
• A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation | npj Digital Medicine, 2025
• Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden | JAMIA, 2025
• Ambient artificial intelligence scribes: utilization and impact on documentation time | JAMIA, 2025
• Clinician Experiences With Ambient Scribe Technology to Assist With Documentation Burden and Efficiency | JAMA Network Open, 2025
• Physician Perspectives on Ambient AI Scribes | JAMA Network Open, 2025
• Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout | JAMA Network Open, 2025
• Ambient Documentation Technology in Clinician Experience of Documentation Burden and Burnout | JAMA Network Open, 2025
• Use of an AI Scribe and Electronic Health Record Efficiency | JAMA Network Open, 2025
• Ambient AI Scribes in Clinical Practice: A Randomized Trial | NEJM AI, 2025
• A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being | NEJM AI, 2025
• Evaluating clinical AI summaries with large language models as judges | npj Digital Medicine, 2025
• Ambient AI-assisted clinical documentation in surgical outpatient care: a preliminary study of usability, workflow, and patient experience | World Journal of Pediatric Surgery, 2025
• Ambient Artificial Intelligence Scribes: Learnings after 1 Year and over 2.5 Million Uses | NEJM Catalyst, 2025

2026

• Ambient Scribe Technology in Simulated Patient Encounters Across Specialties | JAMA Network Open, 2026
• Vision-Enabled AI scribes reduce omissions in clinical conversations: evidence from simulated medication histories | npj Digital Medicine, 2026
• Ambient Artificial Intelligence Scribes and Physician Financial Productivity | JAMA Network Open, 2026
• Patient attitudes toward ambient artificial intelligence scribes in clinical care: insights from a cross-sectional study | JAMIA, 2026
• Listening to the note: clinician perspectives on ambient artificial intelligence scribes in medical documentation | JAMIA, 2026
• Comparing ambient scribes: a randomized crossover clinical trial addressing ambient scribe technologies' impact on physician burnout | JAMIA, 2026
• Impact of an Ambient AI Scribe Among Clinicians and Patients: Real-World Prospective Observational Time-Motion Study | JMIR Medical Informatics, 2026
• Ambient scribe in general practice: a multi-perspective before-after longitudinal mixed-methods study | npj Digital Medicine, 2026
• Use of AI scribes in UK primary care: a survey of general practitioners | npj Digital Medicine, 2026
• Rapid Evaluation of Artificial Intelligence Technology Used for Ambient Dictation in Primary Care: Comparing the Quality of Documentation of Artificial Intelligence-Generated and Human-Produced Clinical Notes | Annals of Internal Medicine, 2026
• Changes in Clinician Time Expenditure and Visit Quantity With Adoption of Artificial Intelligence-Powered Scribes: A Multisite Study | JAMA, 2026

Fact Checking

Through 2023

• Explainable Automated Fact-Checking for Public Health Claims | EMNLP' 20 | Code
• Evidence-based Fact-Checking of Health-related Claims | Findings of EMNLP' 21 | Code

2024

• HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking | LREC-COLING' 24 | Code
• DOSSIER: Fact Checking in Electronic Health Records while Preserving Patient Privacy | MLHC' 24 | Code
• EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records | NeurIPS Datasets and Benchmarks' 24 | Code | PhysioNet
• FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models | CVPR' 25 | Code

2025

• VeriFact: Verifying Facts in LLM-Generated Clinical Text with Electronic Health Records | arXiv, 2025/01 | Code | PhysioNet

Medical Imaging

Medical Imaging Datasets

Through 2023

• MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs | arXiv, 2019/01
• MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports | Scientific Data, 2019
• Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge | Medical Image Analysis, 2024

2024

• Generalist foundation models from a multimodal dataset for 3D computed tomography | Nature Biomedical Engineering, 2026
• Development of a large-scale grounded vision language dataset for chest CT analysis | Scientific Data, 2025
• Shadow and Light: Digitally Reconstructed Radiographs for Disease Classification | arXiv, 2024/06
• MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine | ICLR' 25

Radiology Report Generation

Through 2023

• Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT | EMNLP' 20
• RadGraph: Extracting Clinical Entities and Relations from Radiology Reports | NeurIPS Datasets and Benchmarks' 21
• RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction | MLHC' 23
• Evaluating progress in automatic chest X-ray radiology report generation | Patterns, 2023
• Radiology-Aware Model-Based Evaluation Metric for Report Generation | arXiv, 2023/11

2024

• CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation | AAAI SSS Clinical FMs' 24
• Evaluating GPT-4V (GPT-4 with Vision) on Detection of Radiologic Findings on Chest Radiographs | Radiology, 2024
• LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation | arXiv, 2024/04
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding | NeurIPS MAR Workshop' 24
• GREEN: Generative Radiology Report Evaluation and Error Notation | Findings of EMNLP' 24

Multimodal Large Language Models

2024

• MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models | ICLR' 25
• MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models | ICLR' 25

2025

• GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning | arXiv, 2025/04
• Systematic Evaluation of Large Vision-Language Models for Surgical Artificial Intelligence | arXiv, 2025/04