Assignment Instructions on AI-Based Medical Imaging for Disease Diagnosis
Assignment 12
General Assessment Guidance
This assessment represents the core evaluation for the module, focusing on the application of artificial intelligence in medical imaging to improve disease diagnosis. Students are expected to critically examine how AI models, including deep learning and image analysis algorithms, influence diagnostic accuracy, clinical workflow, and patient outcomes.
All submissions must be made through Turnitin online access. Submissions via email, USB, or hard copy are invalid. Late submissions will receive zero marks. Only include your Student Reference Number (SRN); do not include personal identifiers.
The Harvard referencing system must be applied consistently. AI tools may be used only for proofreading, language correction, or formatting checks. Analytical content, reasoning, and interpretation must be entirely original.
A completed Assignment Cover Sheet is required for submission validation.
Assessment Brief
Context of AI in Medical Imaging
Produce a consultancy-style report examining the use of AI-based imaging technologies for diagnosing diseases such as cancer, neurological disorders, or cardiovascular conditions. Focus on evaluating the accuracy, reliability, ethical considerations, and clinical integration of these technologies.
The report should incorporate real-world examples, peer-reviewed studies, regulatory guidelines, and case data where available. Emphasize the balance between innovation, patient safety, and healthcare policy implications.
Learning Objectives
LO1 – Critically evaluate AI algorithms in medical imaging and their clinical impact.
LO2 – Examine ethical, regulatory, and operational considerations in deploying AI for disease diagnosis.
LO3 – Apply evidence-based reasoning to analyze data from imaging studies and AI models.
LO4 – Formulate recommendations for implementing AI tools in clinical workflows effectively.
Core Report Sections
- Emerging Technologies and Diagnostic Potential
- Clinical and Operational Barriers to AI Adoption
- Accuracy, Reliability, and Validation Metrics
- Ethical, Legal, and Societal Considerations
- Integrating Evidence from Case Studies and Literature
- Strategic Guidance for Implementation
Each section should provide critical insight, supported with data and literature, avoiding generic description.
Suggested Report Structure
- Declaration Page (PP)
- Title Page
- Table of Contents
- Emerging Technologies and Diagnostic Potential
- Clinical and Operational Barriers to AI Adoption
- Accuracy, Reliability, and Validation Metrics
- Ethical, Legal, and Societal Considerations
- Integrating Evidence from Case Studies and Literature
- Strategic Guidance for Implementation
- Harvard References
- Appendices (if required)
Word Count Breakdown (Approximate)
Emerging Technologies and Diagnostic Potential – 500
Clinical and Operational Barriers to AI Adoption – 400
Accuracy, Reliability, and Validation Metrics – 500
Ethical, Legal, and Societal Considerations – 400
Integrating Evidence from Case Studies and Literature – 400
Strategic Guidance for Implementation – 300
Total – approximately 2,500 words
Word allocation is flexible; emphasis is on analytical depth and evidence-based evaluation.
Emerging Technologies and Diagnostic Potential
Examine AI techniques used in medical imaging, such as convolutional neural networks, reinforcement learning, and radiomics. Discuss their capabilities for detecting disease, predicting progression, and assisting radiologists.
Highlight examples such as AI detection of tumors in MRI or CT scans, automated segmentation, and integration with electronic health records. Consider the role of multi-modal imaging, predictive analytics, and real-time decision support systems.
Clinical and Operational Barriers to AI Adoption
Explore the challenges in implementing AI technologies in healthcare settings:
- Integration with hospital IT and PACS systems
- Training and acceptance among healthcare professionals
- Variability in patient demographics affecting AI performance
- Financial and resource constraints for smaller clinics or hospitals
Use recent case studies or reports to illustrate successes and limitations in clinical deployment.
Accuracy, Reliability, and Validation Metrics
Critically assess how AI models are validated for diagnostic accuracy:
- Sensitivity, specificity, precision, and recall
- ROC curves, confusion matrices, and cross-validation techniques
- Comparison with human expert performance
- Handling of edge cases and rare conditions
Highlight strengths, limitations, and potential biases in AI-driven imaging studies.
Ethical, Legal, and Societal Considerations
Discuss ethical and regulatory concerns associated with AI in medical imaging:
- Patient privacy and data security
- Transparency and explainability of AI algorithms
- Accountability in case of misdiagnosis
- Impact on radiology workforce and professional roles
Consider regulatory frameworks such as FDA guidelines, HIPAA compliance, and international standards.
Integrating Evidence from Case Studies and Literature
Use real-world case studies and peer-reviewed literature to illustrate how AI has been applied effectively and where it has fallen short. Include quantitative data on diagnostic improvements, workflow efficiencies, or error reduction. Discuss limitations of available evidence and implications for wider adoption.
Strategic Guidance for Implementation
Provide evidence-based recommendations for adopting AI in medical imaging:
- Steps for integrating AI with clinical workflows
- Training and upskilling staff
- Ongoing evaluation and quality assurance
- Balancing automation with human oversight
- Patient communication strategies regarding AI-assisted diagnosis
Conclude by summarizing the strategic, ethical, and operational value of AI technologies for improving disease diagnosis.
References and Presentation
- Apply Harvard referencing consistently.
- Maintain professional formatting, numbered pages, and clear labeling of figures and tables.
- Demonstrate analytical depth, critical reasoning, and integration of diverse evidence sources.