IA pour le Diagnostic Médical et les Flux Cliniques

Ce qu'elle couvre

Ce programme avancé couvre l'ensemble du cycle de vie de l'adoption de l'IA clinique — de l'IA en imagerie médicale et à l'aide à la décision clinique jusqu'à la transcription ambiante et l'intégration aux DSI. Les participants apprennent à évaluer les outils d'IA selon les standards de validation clinique, à naviguer dans les cadres réglementaires (marquage CE, autorisation FDA) et à concevoir des structures de gouvernance pour un déploiement responsable. Les sessions combinent études de cas tirées de déploiements hospitaliers réels et exercices pratiques de cartographie des flux de travail.

À l'issue, vous saurez

• Design a structured clinical validation protocol for an AI imaging or CDSS tool, including bias and subgroup analysis
• Map the regulatory approval pathway (EU MDR/CE or FDA SaMD) for a specific clinical AI product your institution is evaluating
• Build a governance framework with defined roles, escalation paths, and audit procedures for deployed clinical AI
• Identify integration requirements for embedding an AI tool into an existing EHR using HL7 FHIR standards
• Develop a clinician change-management and adoption plan that addresses alert fatigue and workflow disruption

Sujets abordés

• AI in medical imaging: radiology, pathology, and ophthalmology use cases
• Clinical decision support systems (CDSS): design, integration, and alert fatigue management
• Ambient scribing and voice-to-EHR documentation workflows
• Clinical AI validation: study design, bias assessment, and performance metrics
• Regulatory pathways: EU MDR, CE marking for SaMD, FDA 510(k) and De Novo
• EHR integration standards: HL7 FHIR, SMART on FHIR, and API governance
• Governance, accountability, and incident response for deployed AI
• Change management and clinician adoption strategies

Modalité

Delivered as a blended programme over four to six weeks: two in-person days (or intensive virtual equivalents) bookend the programme, with weekly live online sessions of two to three hours in between. Approximately 60% of time is hands-on — workflow mapping, regulatory simulation exercises, and tool evaluation workshops. Participants receive a clinical AI evaluation toolkit, regulatory checklist templates, and access to a curated case library. A small cohort model (8–20) ensures peer learning between institutions. Can be tailored for purely in-person intensive delivery over three to five days for executive cohorts.

Ce qui fait que ça marche

• Establishing a multidisciplinary AI review committee that includes clinical, legal, ethics, and IT representation before any procurement
• Running a time-boxed clinical pilot with pre-defined success metrics and a clear go/no-go decision gate
• Embedding AI governance into existing clinical quality and risk management frameworks rather than creating parallel structures
• Investing in clinician champions who receive dedicated training and are visible advocates within their departments

Erreurs fréquentes

• Procuring AI tools based on vendor demos alone without independent clinical validation or real-world performance data
• Treating EHR integration as a purely technical task while underestimating workflow redesign and clinician buy-in required
• Overlooking regulatory obligations for Software as a Medical Device (SaMD), leading to compliance exposure post-deployment
• Deploying AI without a post-market surveillance or incident escalation process, creating patient safety blind spots

Fournisseurs à considérer

• ETIM Academy (European Society of Radiology)www.myesr.org/education/etim →
• NHS AI Lab / NHSX Digital Academiestransform.england.nhs.uk/ai-lab/ →
• Stanford Center for AI in Medicine and Imaging (AIMI) – online coursesaimi.stanford.edu/education →
• Coursera – AI for Medicine Specialization (DeepLearning.AI)www.coursera.org/specializations/ai-for-medicine →

Sources

• WHO Guidance on Ethics and Governance of AI for Health →
• European Commission – AI in Healthcare and the Medical Device Regulation →