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IEEE MedAI 2026: IEEE International Conference on Medical Artificial Intelligence

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IEEE MedAI
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투고 마감일:
2026-08-15 10일 남음
통보일:
2026-09-15
개최일:
2026-11-20
개최지:
Zhengzhou, China
개최 횟수:
조회: 23244   팔로우: 10   참가: 3

논문 모집

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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