Training on AI for Radiology and Medical Imaging Interpretation in Low-Resource Settings

Access to quality radiology services is a persistent challenge in low-resource healthcare settings due to shortages of specialists, limited imaging equipment, and high operational costs. Delayed or inaccurate diagnoses can lead to poor patient outcomes. Artificial Intelligence (AI) offers transformative potential by supporting rapid, accurate interpretation of medical images and assisting clinicians in decision-making.

This 10-day course equips participants with practical knowledge and skills to apply AI in radiology and medical imaging, with a focus on low-resource environments. Training covers AI-assisted analysis of X-rays, CT scans, ultrasounds, and other imaging modalities, workflow integration, validation of AI outputs, and responsible deployment. Participants will explore regulatory, ethical, and safety considerations for AI in healthcare, including patient privacy and clinical effectiveness.

Through hands-on exercises, case studies, and scenario-based learning, participants will learn how to implement AI tools responsibly, optimize imaging workflows, and improve diagnostic efficiency and accuracy in settings with limited resources.

By the end of the program, participants will be able to leverage AI to enhance radiology services, expand access to diagnostics, and support timely clinical decision-making in resource-constrained healthcare systems.

Duration

10 Days

Who Should Attend

• Radiologists and imaging specialists

• Healthcare administrators

• Digital health professionals

• IT and AI practitioners in healthcare

• Policymakers and regulators

• Hospital managers

Individual Impact

• Understand AI applications in medical imaging
• Improve diagnostic support skills
• Gain knowledge on AI implementation
• Strengthen clinical decision-making
• Build digital health leadership capacity

Organizational Impact

• Improve diagnostic accuracy
• Reduce patient wait times
• Expand imaging services
• Optimize resource use
• Enhance healthcare access

By the end of this course, participants will be able to:

• Explain AI concepts in radiology
• Apply AI tools for imaging interpretation
• Integrate AI into clinical workflows
• Address ethical and regulatory concerns
• Design AI-supported imaging programs

Module 1: Introduction to AI in Radiology

• Basics of AI and machine learning

• Medical imaging types and workflows

• Benefits and limitations of AI tools

• Current trends in AI diagnostics

• Practical Exercise: Identify imaging challenges AI can solve

Module 2: AI Tools for X-Ray and CT Scan Interpretation

• AI-assisted detection of diseases
• Image enhancement technologies
• Automated reporting systems
• Accuracy and reliability considerations
• Case Study: AI improving tuberculosis screening

Module 3: AI in Ultrasound and MRI Analysis

• Real-time image support
• Pattern recognition in soft tissue imaging
• Workflow efficiency improvements
• Remote imaging interpretation
• Practical Exercise: Simulated AI-supported image review

Module 4: Implementing AI in Low-Resource Settings

• Infrastructure requirements
• Cost-effective solutions
• Cloud-based platforms
• Training healthcare staff
• Case Study: AI deployment in rural hospitals

Module 5: Data Management and Interoperability

• Medical image storage systems
• Data labeling and quality
• Integration with health records
• Data sharing frameworks
• Practical Exercise: Design a basic imaging data workflow

Module 6: Ethics, Bias, and Patient Safety

• Algorithm bias risks
• Transparency and explainability
• Clinical accountability
• Patient consent
• Case Study: Addressing AI bias in diagnostics

Module 7: Regulatory and Legal Considerations

• Approval processes for AI tools
• Clinical validation standards
• Data protection laws
• Liability frameworks
• Practical Exercise: Review an AI regulatory checklist

Module 8: Monitoring AI Performance and Quality

• Accuracy tracking
• Human oversight models
• Continuous improvement
• Error management
• Case Study: Improving AI system reliability

Module 9: Financing and Sustainability

• Cost-benefit analysis
• Funding models
• Public-private partnerships
• Long-term maintenance planning
• Practical Exercise: Develop an AI imaging investment plan

Module 10: Future of AI in Medical Imaging

• Emerging technologies
• AI-driven diagnostics expansion
• Personalized imaging analysis
• Global health innovations
• Practical Exercise: Create an AI adoption roadmap