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Professional Diploma in AI in Healthcare & Health Informatics

 

Field and Subfield: Nutrition Sciences / Healthcare and Medical Informatics

1. Program Overview:

An accredited professional and applied program extending over 3 months (80 hours of approved study load), aimed at preparing and qualifying healthcare practitioners, nutrition specialists, and medical informatics analysts to employ artificial intelligence and digital health technologies in improving the quality of care, diagnosis, and therapeutic services. The program covers the fundamentals of health informatics and medical data management systems, machine learning, deep learning, generative artificial intelligence, and Prompt Engineering technologies, diagnostic and predictive applications in medical imaging, laboratories, cardiology, and public health, addressing algorithmic bias, privacy, accountability, and cybersecurity issues, through to planning, building, and testing applied intelligent models and solutions.

2. Key Objectives:

  • Understanding informatics systems and digital health: understanding the fundamentals of Health Information Systems (HIS), Interoperability, and ensuring the quality, preparation, and visualization of medical data.
  • Mastering artificial intelligence and machine learning algorithms: understanding the Machine Learning Workflow, Generative AI models, and applications of intelligent Prompt Engineering in healthcare environments.
  • Applying intelligent solutions in medical specialties: employing AI tools in predictive diagnosis, medical imaging interpretation, laboratory medicine, cardiology, nutrition, and public health.
  • Governance, ethics, and patient safety: managing AI Bias risks, ensuring Explainability & Accountability, protecting the confidentiality of patient data, and applying cybersecurity standards.
  • Developing project prototypes: planning, designing, and testing AI-based solutions, evaluating their ethical impact and clinical safety, and presenting them professionally.

3. Target Audience:

  • Physicians, healthcare practitioners, clinical nutrition specialists, and nursing professionals.
  • Health informatics specialists and officers and electronic medical records management personnel.
  • Medical data analysts, biomedical engineers, and digital health technology developers.
  • Graduates and students of Medicine, Applied Medical Sciences, Pharmacy, Nutrition, Bioinformatics, and Computer Science.

4. Study Plan and Modules (12 Modules — 24 Interactive Lectures):

Module

Topics and Training Units

Number of Lectures

Total Hours

Module 1

Introduction to Artificial Intelligence and Its Technologies (ML, DL, NN, Generative AI)

2

4 hours

Module 2

Artificial Intelligence in Healthcare, and the Fundamentals of Informatics and Health Information Systems

2

4 hours

Module 3

Digital Health and Interoperability, and the Quality, Preparation, Analysis, and Visualization of Health Data

2

4 hours

Module 4

Machine Learning Workflow (ML Workflow), Its Algorithms, and Healthcare Applications

2

4 hours

Module 5

Model Evaluation, Generative Artificial Intelligence, and Prompt Engineering

2

4 hours

Module 6

AI Risks and Applications in Medical Imaging, Laboratory Medicine, Cardiology, and Public Health

2

4 hours

Module 7

Key Applications for Diagnosis and Prediction, and Standards of Privacy and Health Data Confidentiality

2

4 hours

Module 8

Addressing AI Bias, and Principles of Transparency, Explainability, and Accountability

2

4 hours

Module 9

Patient Safety and Cybersecurity, and Methodology for Project Selection and Problem Identification

2

4 hours

Module 10

Planning Data and AI Solutions, Selecting Tools, and Building Prototypes

2

4 hours

Module 11

Testing and Evaluation of Intelligent Solutions, Ethics Assessment, and Patient-Safety Compliance

2

4 hours

Module 12

Preparation for the Presentation, and Delivery and Discussion of the Final Healthcare Solution Project Presentation

2

4 hours

Total

12 Comprehensive Study Modules

24 Lectures

48 Training Hours

5. Organizational Structure and Assessment System:

  • Duration and sessions: 3 months (evening period), at a rate of two lectures per week, two hours per lecture.
  • Language of study and delivery method: Arabic and English | fully remote interactive training (Online).
  • Distribution of credit hours (80 hours):
  • 50 hours: live and interactive online lectures.
  • 20 hours: required and independent readings.
  • 7 hours: chapter questions and self-assessment.
  • 3 hours: final review and program completion examination.
  • Attendance and assessment requirements: attendance of no less than 75% of the lectures, and completion of periodic assignments and assessment projects.
  • Certificate and final project: the graduate is awarded a Diploma in Clinical Nutrition (Artificial Intelligence in Healthcare and Health Informatics) after submitting and presenting the project and passing the final online examination consisting of 25 multiple-choice questions + 5 questions based on practical and applied cases.

 

 

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