The Second IEEE International Conference on Medical Artificial Intelligence (IEEE MedAI 2024)

Medical Artificial Intelligence is a highly specialized interdisciplinary research field that lies at the intersection of artificial intelligence (AI) and medicine. Building upon the success of the inaugural The First IEEE International conference on Medical Artificial Intelligence (MedAI) held in Beijing last year, The Second MedAI is scheduled in Chongqing from November 15th to 17th, 2024.

This prestigious event is expected to gather leading experts, researchers, and professionals in the field to exchange knowledge, present cutting-edge advancements, and foster collaborations in the realm of MedAI. Specifically, the aim of this conference is two-fold: (1) to showcase successful applications of AI techniques in solving challenging biomedical problems, and (2) to foster more extensive and deeper interactions and collaborations between medical researchers and practitioners with challenging medical issues and AI researchers who are eager to identify complex real-life problems for their techniques or to start developing novel algorithms to solve challenging medical issues. Such issues may range from molecular-level challenges, such as diagnostic issues, information extraction, analyses, and modeling from massive medical data, to mining electronic medical records, knowledge-based treatment plan recommendation, and further to robotics to conduct complex surgeries.

We invite original contributions on innovative AI theories and methodologies for medicine, AI-inspired biomedical technologies, and complex biomedical issues that can benefit from AI techniques or may pose new challenges to AI. The interested topics include any crossing between a medicine & healthcare area and an AI area below, but are not limited to the table of related areas.


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Call For Papers

We invite original contributions on innovative AI theories and methodologies for medicine, AI-inspired biomedical technologies, and complex biomedical issues that can benefit from AI techniques or may pose new challenges to AI. The interested topics include, but are not limited to:

Areas in Medicine & Healthcare benefited from AI

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, diagnose, 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

    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-Euclidian 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

  • Download Call For Papers


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