Which healthcare institutions is this training suitable for?

The training is designed for private and public hospitals (the scale of Acıbadem, Memorial, Medipol, MLP Care, Liv, Florence Nightingale, plus Education & Research Hospitals), family health centers, dental chains, imaging centers, laboratory chains, pharmaceutical and medical device companies, and HealthTech start-ups. Digital transformation teams at the Ministry of Health, SGK and TİTCK are also in the target audience.

Do clinical decision support (CDSS) models need to be certified as medical devices?

Healthcare medical device AI is on the EU AI Act high-risk list; under TİTCK and CE-MDR, software that directly influences diagnostic or therapeutic decisions is classified as Software as a Medical Device (SaMD). The training explains which CDSS scenarios require SaMD certification and which can remain as 'clinical decision support'; typical clinical validation and technical file requirements for CE-MDR Class IIa/IIb are summarized.

How does the imaging triage lab proceed without hospital data?

In public programs, anonymized open datasets (NIH ChestX-ray14, RSNA Pneumonia, CheXpert, BraTS, Camelyon) are used. In corporate/private programs, an NDA is signed and the institution's anonymized DICOM samples are used; the data never leaves the training environment, and all temporary data is deleted after the lab.

What kind of knowledge bases does the clinical RAG system run on?

Clinical guidelines (NICE, Ministry of Health guidelines, TKD, TND, TPD and other specialty societies), in-hospital protocols, SGK SUT, institutional procedures (infection control, drug preparation) and reference bases such as UpToDate and MSD Manual are used to build the RAG architecture. Hybrid search (BM25 + vector + reranking) and the combination with HL7 FHIR-structured patient data are also covered.

How is compliance with KVKK Health Data Regulation and EU AI Act ensured?

Section 6 is dedicated to this: for KVKK — special-category health data processing requirements, explicit consent, anonymization/pseudonymization techniques and data residency; for the EU AI Act — high-risk classification, FRIA (Fundamental Rights Impact Assessment), post-market monitoring and audit trail; for TİTCK — clinical validation files and a post-market surveillance approach.

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

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Healthcare AI Training: Hospital Operations, Clinical Decision Support, Imaging Triage and Clinical RAG

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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?

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?

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

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

Requirements

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)

Course Curriculum

36 Lessons

Duration

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

Şükrü Yusuf KAYA

Artificial Intelligence Architect | Corporate AI & LLM Trainings | Stanford University | Software & Technology Advisor

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Key Takeaways

Healthcare AI Training: Hospital Operations, Clinical Decision Support, Imaging Triage and Clinical RAG