IEEE MedAI 2026 (IEEE International Conference on Medical Artificial Intelligence) is an academic conference held in Zhengzhou, China on 2026-11-20. The paper submission deadline is 2026-08-15. Acceptance notifications are sent on 2026-09-15.
OPICS
Areas in Medicine & Healthcare Benefited from AI (MH)
Area 1: Infrastructure of Medicine
Medical record database and data linkage
Software, hardware, robotics and languages for medicine
Drug design, development and clinical use
IoT infrastructure, software and methods for patients' communication
Ethics, privacy and security of managing and using patients' data
Ethics, privacy and security of medical decision making
Area 2: Telemedicine
Remote diagnosis
Remote consultation
Remote operation
Remote treatment planning
Remote monitoring, recommendation and intervention
Area 3: Digital and Precise Medicine
Digital hospital and digital health care
Distributed digital medicine
Virtual reality in medicine
Augmented reality in medicine
Automated control of medical facilities and devices
Multi-modal medical image processing and interpretation
3D image reconstruction
Area 4: Automated Medicine
Automated interpretation of medical records
Automated synthesis of patients' data
Automated decision making of diagnosis
Automated recommendation for medical services
Automated generation of prescription
Automated recommendation of treatment plan
Assistive living
Computerized clinical consultation, discussion and argumentation
Disease predisposition, diagnosis, progression and treatment
Area 5: Precise Medicine and Biomedical Informatics
Medical data, including blood chemistry, biomarkers, analyses and interpretation
Precise surgery and plan-guided plastic medicine
Individualized medicine
Targeted medicine
Particular patients adapted immunotherapy
Cellular/molecular data analyses and interpretation
Detection, qualification and annotation of genomic variants
Disease-omic data relationship knowledge base construction
Epigenetics and chromatin structure
Pharmacogenomics
Cancer genomics
Area 6: Computational Systems Biology
Immune system modeling
Single-cell and spatial omics
Biomolecular structure and function prediction
Interpretation of patient genomic, transcriptomic and omic data
Disease onset, development modeling
Microbe-human interactions
Metabolic reprogramming in diseases
Complex multi-component interactions within biological systems
Gene regulation and circuit design
Network biology and medicine
Areas in AI for Medicine and Healthcare (AI)
Area 1: Infrastructure and Knowledge-based AI
Algorithms, software and system architecture
Hardware and performance
Programming Languages
Knowledge representation and reasoning
Knowledge graphs, ontologies and platform and tools
Knowledge-based natural language understanding
Knowledge-based systems in general
Infrastructure supporting mobile and distributed AI
Area 2: Bionic AI
Swarm AI
Neural Network based AI
Deep learning supported AI
Neural-symbolic integrated AI
Non-Euclidean geometry and deep learning
Geometric flow learning
Brain-like Intelligence
Area 3: Collective AI
Federated AI
Crowd AI
Digital Twins
Distributed AI
Game theory-based AI
Consultation in decision making
Negotiation in decision making
Argumentation in decision making
Computer vision and image processing
Area 4: Automated AI
Automated decision making
Automated process design
Mathematics inspired AI
Nature inspired AI
Situation inspired AI
Metaverse based AI
AI-inspired algorithms
Automated monitoring, recommendation and intervention
Area 5: Generative AI
Generative adversarial network
Automated crowd intelligence generation
Automated language, image, voice and video generation
Automated scientific theory generation
Automated algorithms generation
ChatGPT-like AI
Generative AI with on-chip synthesis
Quantum AI
Area 6: Trustworthy AI
Explainable AI
Causality preserving AI
Ethical AI considerations
Deep reasoning and big data processing
Privacy preservation in medical data
Data security of AI
Cybersecurity of AI
Trust and transparency in AI
Social implications of AI technologies
AI responsibility, bias and user needs
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