The Global Summit on Artificial Intelligence in Healthcare & Life Sciences (GAIAH 2027) brings together the world’s leading researchers, clinicians, data scientists, and industry innovators to advance the transformative role of artificial intelligence in modern medicine and biological sciences.
As AI continues to reshape diagnostics, drug discovery, clinical decision-making, and patient care, GAIAH 2027 offers an unparalleled platform for the exchange of cutting-edge research, emerging technologies, and translational breakthroughs. Held in the historic and scientifically vibrant city of Prague, Czech Republic, the summit fosters high-impact dialogue between academia, healthcare institutions, biotechnology companies, and regulatory bodies from across the globe.
GAIAH 2027 is designed to accelerate the responsible integration of AI into healthcare systems worldwide — connecting discovery with delivery, and innovation with impact.
World-leading researchers, clinicians, and innovators in artificial intelligence and healthcare will deliver keynote lectures that define the current frontiers of the field. Each keynote is designed to inspire cross-disciplinary thinking, challenge existing assumptions, and present the most consequential scientific and clinical advances shaping the future of AI in medicine and life sciences.
1Researchers, clinicians, and data scientists from institutions worldwide will present their latest findings through curated oral presentations and interactive poster sessions. These forums are structured to maximize scientific exchange — encouraging critical feedback, stimulating dialogue, and fostering collaborations that accelerate the translation of AI research into clinical and biological impact.
2Five thematic scientific sessions cover the complete landscape of AI in healthcare and life sciences — from medical imaging, drug discovery, and clinical decision support to digital health, public health AI, and responsible AI governance. Each session is designed to ensure that every professional working in this field finds their research domain represented and engaged.
3Thought-provoking panel discussions will bring together researchers, clinicians, technologists, ethicists, and regulators to address the most critical and contested questions in healthcare AI — from algorithmic fairness and clinical validation standards to the governance of large language models in clinical settings. These discussions are designed to generate insight that no single discipline can produce alone.
5Connect with leading organizations, technology developers, and research institutions presenting the latest AI-powered tools, platforms, and services transforming healthcare and life sciences. The exhibition provides a dedicated environment for discovering solutions, evaluating technologies, and building partnerships relevant to your research and clinical practice.
6GAIAH 2027 is designed from the ground up as a collaborative environment. Structured networking sessions, cross-disciplinary breakout opportunities, and social events create meaningful connections between AI researchers, clinicians, industry professionals, and policy experts — relationships that extend well beyond the conference itself.
6By attending the Global Summit on Artificial Intelligence in Healthcare & Life Sciences 2027, you will gain access to the most current advances in healthcare AI, engage with a global community of researchers and practitioners, and contribute to the responsible and impactful integration of artificial intelligence into medicine. We look forward to welcoming you to Prague in April 2027.
Focus Areas Include:
Deep learning in radiology & CT/MRI interpretation, AI in digital pathology & histology, retinal imaging & ophthalmology AI, dermatology & skin lesion classification, AI in endoscopy, bronchoscopy & colonoscopy, echocardiography & cardiac imaging AI, mammography & breast cancer screening, point-of-care & portable diagnostics, multi-modal image fusion, radiomics & imaging biomarkers, self-supervised learning in medical imaging, foundation models for medical vision, image segmentation & tumor delineation, edge AI for imaging devices, diagnostic validation & clinical deployment.
Focus Areas Include:
Focus Areas: AI in drug target identification & molecular docking, generative models for compound design, in silico drug screening, genomics, transcriptomics & multi-omics integration, single-cell omics analysis, spatial transcriptomics, proteomics & metabolomics, biomarker discovery & validation, pharmacogenomics & individualized therapy, systems biology modeling, network biology & pathway analysis, digital twins for molecular modeling, CRISPR & gene editing informatics, rare disease research, vaccine design & immunoinformatic, AI in computational clinical trial modeling & in silico patient stratification.
Focus Areas Include:
Clinical decision support systems, NLP for electronic health records, real-world evidence (RWE) analytics, predictive modeling using structured EHR, clinical risk scoring systems, health informatics & clinical data warehousing, clinical workflow automation, risk stratification & early warning systems, sepsis, ICU & critical care AI, AI in surgical planning & robotic surgery, readmission & length-of-stay prediction, emergency medicine & triage AI, hospital resource & workflow optimization, health data interoperability (HL7, FHIR), AI in nursing & allied health practice
Focus Areas Include:
Wearable sensors & continuous health monitoring, smartphone-based diagnostics & mHealth, IoT & smart medical devices (IoMT), remote patient monitoring & telehealth, digital phenotyping & behavioral analytics, mental health AI & digital therapeutics, AI in rehabilitation & remote physiotherapy, AI in chronic disease management, AI in epidemiology & disease surveillance, population health modeling & social determinants, pandemic preparedness & outbreak prediction, global health equity & AI accessibility, health economics & cost-effectiveness modeling, AI in elderly care & aging populations, health policy & AI-driven healthcare systems.
Focus Areas Include:
Algorithmic bias, fairness & health equity, explainability & interpretability of clinical AI, EU AI Act, FDA & global regulatory frameworks, AI governance frameworks, model auditing & validation, clinical AI benchmarking, post-deployment monitoring, AI liability & medico-legal implications, trustworthy AI frameworks, human-in-the-loop systems, GDPR, federated learning & patient data privacy, large language models (LLMs) in clinical settings, generative AI in healthcare, AI in medical education & simulation, clinician-AI collaboration & trust
GAIAH 2027 — Target Audience
Date: May 31, 2026
Date: October 31, 2026
Date: November 1-30, 2026
Date: January 31, 2027
