Conference Information

AIME 2027: International Conference on Artificial Intelligence in Medicine

Submission Date:
2027-01-31 Due in 77 days
Notification Date:
2027-04-19
Conference Date:
2027-07-05
Location:
Amsterdam, the Netherlands
Years:
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ICORE: B   Viewed: 17902   Tracked: 1   Attend: 0
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Conference Partner Index (CP-I)

62.9 / 100
Ranked #524 of 5,777 conferences · Top 10%

#12 of 184 in Bioinformatics & Health Informatics #41 of 755 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
72
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
88
Community attention (10%)
19
Public record completeness (15%)
55

Inputs used: Listed as ICORE B · Editions on record: 25 · Researchers following it here: 1 · Researchers who opened this page in the past 24 months: 4

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-11-15

Call For Papers

AIME 2027 (International Conference on Artificial Intelligence in Medicine) is a ICORE B conference held in Amsterdam, the Netherlands on 2027-07-05. The paper submission deadline is 2027-01-31. Acceptance notifications are sent on 2027-04-19.

Scientific Areas & Topic Categories AIME 2027 invites submissions that introduce or examine innovative AI solutions for problems in medicine and healthcare. These submissions may include new methods or enhancements to existing ones. Regardless, submissions should demonstrate their usefulness in addressing challenges in medicine and healthcare and highlight their contribution to the foundational theoretical knowledge. They should also discuss assumptions, limitations, and innovations relative to the current state of the art. Contributions focused on systems and applications should describe the development, implementation, or evaluation of innovative AI-based tools within medicine and healthcare. They should connect the work to the underlying theories and either assess the potential benefits for addressing healthcare problems or provide empirical evidence of benefits in clinical practice. The scope of the conference includes, but is not limited to, the following two core areas: 🟣 AI Foundations and Methodological Innovations Learning Paradigms and Representational Advances: Multimodal learning, generative AI, foundation models Causal inference, reinforcement and transfer learning Federated and privacy-preserving methods Knowledge, Reasoning, and Semantic Integration: Knowledge graphs, ontologies, terminologies Symbolic reasoning, Bayesian networks, fuzzy logic Data harmonization and integration of structured/unstructured data Language, Imaging, and Signal Intelligence: Clinical NLP, summarization, and extraction Biomedical imaging and signal processing Multimodal fusion of text, vision, time series 🔵 Applications, Systems, and Health Impact Clinical AI and Decision Support Systems: CDSS, triage tools, precision medicine Practice guidelines and adaptive workflows Agentic systems and innovative applications of large language models Deployment, Human-AI interaction and training: Smart hospitals, telemedicine, ambient intelligence Human-AI collaboration, usability and workflow-integration Medical education and clinical training with AI Patient-Centred and Population-Level Applications: mHealth, wearables, digital interventions and therapeutics Public health, epidemiology, equity Personalized patient engagement and remote monitoring Omics and Computational Biology for Precision Health: Multi-omics integration (genomics, transcriptomics, proteomics, metabolomics, microbiome) and single-cell/spatial omics Biomarker discovery, molecular subtyping, and patient stratification Polygenic risk, variant interpretation, pharmacogenomics, and integration of omics with EHR, imaging, and clinical phenotypes AI-Driven Drug Discovery and Development: Target identification, generative chemistry, and de novo drug design Protein structure prediction, virtual screening, and ADMET/toxicity prediction Drug repurposing, AI-enabled clinical trials, and pharmacovigilance 🔄 Cross-Cutting Themes (Applicable Across All Areas) Trustworthy AI: Explainability, fairness, bias mitigation, trust Ethics & Regulation: Legal frameworks, compliance, responsible innovation Safety & Evaluation: Deployment readiness, robustness, validation
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