About this training
Hospital operations, clinical decision support, medical imaging triage and clinical knowledge base RAG — an end-to-end hands-on program tailored to Türkiye's healthcare sector, framed within KVKK, EU AI Act and TİTCK compliance.
This training is designed for: Hospital CIO, CMIO, IT and digital transformation leaders Clinical decision support and clinical quality teams (sepsis committee, medication safety) Radiology, pathology and laboratory management leads HealthTech start-ups and teams building AI healthcare products Medical, digital and pharmacovigilance departments of pharmaceutical companies Digital transformation leads at the Ministry of Health, SGK, TİTCK and public health institutions
Why this course matters: A comprehensive AI training program tailored to Türkiye's healthcare sector — without a comparable peer in content Concrete preparation for KVKK Health Data Regulation, EU AI Act and TİTCK regulatory expectations A holistic approach unifying hospital operations, clinical decision support, imaging and RAG in a single program Content aligned with the HIMSS EMRAM maturity model and digital transformation roadmap Includes foundation models (MedSAM, BiomedCLIP, RAD-DINO) and a clinical LLM strategy Depth on clinical validation, doctor-in-the-loop and override rate — operationally critical AI topics in healthcare
Learning outcomes by the end of the programme: Ability to prioritize AI use-cases in hospital operations (bed management, ED triage, OR scheduling) Capability to select, validate and design doctor-in-the-loop integration for CDSS models (sepsis, AKI, early warning) Ability to design DICOM/PACS-compatible AI workflows for radiology and pathology triage Capability to build RAG architecture for clinical guidelines, SUT and hospital protocols (including HL7 FHIR integration) Ability to design AI architecture and documentation framework compliant with KVKK Health Data Regulation, EU AI Act and TİTCK Capability to prepare a 90-day healthcare AI pilot roadmap and clinical validation protocol
Prerequisites and recommended background: Familiarity with basic healthcare concepts (hospital operations, clinical processes, reimbursement) Use of Excel or basic data analysis tools Computer for the training (lab work runs in the cloud) Basic awareness of healthcare standards such as DICOM, HL7, FHIR (not mandatory — introduced during the training) Pre-training short survey to assess your institution's digital maturity (HIMSS EMRAM)
- Content framed within Türkiye's healthcare ecosystem (Ministry of Health, e-Nabız, MHRS, SGK MEDULA, TİTCK)
- AI architecture guide compliant with KVKK Health Data Regulation and EU AI Act high-risk healthcare classification
- Concrete use-cases for hospital operations, clinical decision support, imaging triage and clinical RAG
- Hands-on labs on DICOM, HL7 FHIR, PACS and HBYS data
- Foundation models (MedSAM, BiomedCLIP, RAD-DINO) and clinical LLM strategy
- Clinician-AI interaction patterns, override rate and clinical validation methodology (pre/post-deployment)
Key Takeaways
- Ability to prioritize AI use-cases in hospital operations (bed management, ED triage, OR scheduling)
- Capability to select, validate and design doctor-in-the-loop integration for CDSS models (sepsis, AKI, early warning)
- Ability to design DICOM/PACS-compatible AI workflows for radiology and pathology triage
- Capability to build RAG architecture for clinical guidelines, SUT and hospital protocols (including HL7 FHIR integration)
- Ability to design AI architecture and documentation framework compliant with KVKK Health Data Regulation, EU AI Act and TİTCK
- Capability to prepare a 90-day healthcare AI pilot roadmap and clinical validation protocol
Healthcare AI Training: Hospital Operations, Clinical Decision Support, Imaging Triage and Clinical RAG
Hospital operations, clinical decision support, medical imaging triage and clinical knowledge base RAG — an end-to-end hands-on program tailored to Türkiye's healthcare sector, framed within KVKK, EU AI Act and TİTCK compliance.
About This Course
Training Methodology
Content framed within Türkiye's healthcare ecosystem (Ministry of Health, e-Nabız, MHRS, SGK MEDULA, TİTCK)
AI architecture guide compliant with KVKK Health Data Regulation and EU AI Act high-risk healthcare classification
Concrete use-cases for hospital operations, clinical decision support, imaging triage and clinical RAG
Hands-on labs on DICOM, HL7 FHIR, PACS and HBYS data
Foundation models (MedSAM, BiomedCLIP, RAD-DINO) and clinical LLM strategy
Clinician-AI interaction patterns, override rate and clinical validation methodology (pre/post-deployment)
Who Is This For?
Why This Course?
A comprehensive AI training program tailored to Türkiye's healthcare sector — without a comparable peer in content
Concrete preparation for KVKK Health Data Regulation, EU AI Act and TİTCK regulatory expectations
A holistic approach unifying hospital operations, clinical decision support, imaging and RAG in a single program
Content aligned with the HIMSS EMRAM maturity model and digital transformation roadmap
Includes foundation models (MedSAM, BiomedCLIP, RAD-DINO) and a clinical LLM strategy
Depth on clinical validation, doctor-in-the-loop and override rate — operationally critical AI topics in healthcare
Learning Outcomes
Requirements
Course Curriculum
36 LessonsDuration
2 Hours
Section Description
This section maps Türkiye's healthcare digital value chain, regulatory framework and the actual position of AI in the sector. The goal is to draw a clear line between where AI use makes sense and where it carries unacceptable risk.
Learning Objectives
- Understand Türkiye's healthcare digital infrastructure (e-Nabız, MHRS, AHBS, HSYS, SGK MEDULA)
- Grasp the intersection of KVKK Health Data Regulation and EU AI Act high-risk classification
- Recognize that the HIMSS EMRAM maturity model defines prerequisites before healthcare AI
- Evaluate AI investment ROI vs clinical impact from a sector perspective
1.1 - Türkiye's Healthcare Digital Infrastructure
- 1.1.1 - Ministry of Health Digital Strategy: e-Nabız, MHRS, AHBS, HSYS, ESYS
- 1.1.2 - SGK MEDULA, SUT and reimbursement process data structure
- 1.1.3 - Hospital Information Management System (HBYS) common architectures and data quality
1.2 - Regulatory Framework and Healthcare AI
- 1.2.1 - KVKK special-category health data: explicit consent, anonymization, pseudonymization
- 1.2.2 - EU AI Act high-risk classification and impact on the healthcare sector
- 1.2.3 - Software as a Medical Device (SaMD) classification under TİTCK and CE-MDR
1.3 - Digital Maturity and Healthcare AI Cases
- 1.3.1 - HIMSS EMRAM maturity levels and the distribution of Türkiye's hospitals
- 1.3.2 - Global cases: Mayo Clinic, NHS, Kaiser Permanente AI transformation
- 1.3.3 - Türkiye AI healthcare cases: Acıbadem, Memorial, Medipol, MLP Care, Liv scale
Instructor
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