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