AI Diagnostics Case Studies

Supplementary Resources
• Research Article: “Deep Learning for Pneumonia Detection in Chest X-Rays” (Radiology, 2020)
https://pubs.rsna.org/doi/10.1148/radiol.2020191781
• Infographic: “How AI Interprets Medical Images”
https://www.healthit.gov/sites/default/files/ai-medical-imaging-infographic.png
• Political Cartoon Activity: Sketch or locate a cartoon showing a robot doctor under pressure to diagnose quickly—consider the trade-offs between speed and accuracy.

Case Study 1: AI-Assisted Radiology Imaging for Pneumonia Detection

A major children’s hospital implemented an AI algorithm that analyzes pediatric chest X-rays to identify early signs of pneumonia. The system automatically flags high-risk images for radiologist review, reducing interpretation time and supporting faster treatment decisions.

Further Reading: “Pediatric Pneumonia: AI Gains Ground” (JAMA Pediatrics, 2021)
https://jamanetwork.com/journals/jamapediatrics/fullarticle/2784321

Infographic: “AI Workflow in Radiology”
https://example.com/radiology-ai-workflow.png

Political Cartoon Prompt: Draw a scene where a radiologist and a robot debate over a suspicious X-ray, highlighting human–AI collaboration.

1. What are the key benefits of the AI tool in this scenario?
   
   
   
   
2. What technical or clinical challenges did the development team encounter?
   
   
   
   
   
   
   
3. What measurable outcomes or improvements were reported after deploying the AI system?
   
   
   
   
   
   
   

Case Study 2: Predictive Analytics for Sepsis Risk in ICU Patients

A tertiary care hospital integrated an AI-driven predictive model into its electronic health record system to identify ICU patients at high risk of developing sepsis. The tool continuously monitors vital signs and lab results, issuing alerts when early warning signs appear.

Further Reading: “Machine Learning to Predict Sepsis in the ICU” (Nature Medicine, 2019)
https://www.nature.com/articles/s41591-019-0533-6

Infographic: “Sepsis Alert System Overview”
https://example.com/sepsis-alert-infographic.png

Political Cartoon Prompt: Create a cartoon showing nurses responding to an AI alert, exploring both trust and skepticism.

1. What benefits did this predictive analytics solution offer to clinicians and patient care?
   
   
   
   
2. Which data-related or workflow challenges arose during model implementation?
   
   
   
   
   
   
   
3. How was the model’s accuracy and impact on sepsis outcomes evaluated?
   
   
   
   
   
   
   

Case Study 3: Pathology Image Analysis for Cancer Biopsy Classification

A pathology lab adopted an AI system that classifies digitized biopsy slides to distinguish between benign and malignant tissue samples. The tool provides probability scores and heat maps to guide pathologists’ review and reduce diagnostic variability.

Further Reading: “AI-Driven Cancer Biopsy Analysis” (The Lancet Oncology, 2020)
https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(20)30215-8/fulltext

Infographic: “Heat Map Visualizations in Pathology”
https://example.com/pathology-heatmap-infographic.png

Political Cartoon Prompt: Sketch a microscope debating with a computer chip over who sees the “true” cancer cell.

1. What key advantages does this AI solution bring to pathology workflows?
   
   
   
   
2. What challenges did developers face in training the model on diverse tissue samples?
   
   
   
   
   
   
   
3. What improvements in diagnostic consistency or turnaround time were observed post-deployment?
   
   
   
   
   
   
   

AI Ethics Discussion Worksheet

Instructions: Reflect on the following ethical issues related to AI in medicine. Provide thoughtful responses to each prompt. Use the space below each question to write your answers.

1. Data Privacy and Patient Consent

• What types of sensitive patient data might an AI medical tool require, and why is this data important?

• How can developers ensure informed consent when collecting patient data for AI training?

• What measures can be implemented to protect patient privacy throughout the AI development life cycle?

2. Algorithmic Bias and Fairness

• Describe a scenario where AI in healthcare could produce biased outcomes. What factors contribute to this bias?

• How can bias in training data be identified and minimized?

• What steps can be taken to ensure that AI-driven medical tools provide equitable care across diverse patient populations?

3. Accountability and Transparency

• Who should be held responsible if an AI medical diagnosis is incorrect? Explain your reasoning.

• What level of transparency about AI decision-making processes is necessary for healthcare providers and patients?

• Propose ways that AI developers and medical institutions can maintain accountability for AI-driven healthcare solutions.

Reflection

In a brief paragraph, discuss how balancing innovation with ethical safeguards can impact the future of AI in medicine.

Medical AI Solution Design Template

Use this template to plan your AI-driven medical solution. Provide detailed responses in each section.

1. Problem Statement

• Describe the specific medical challenge or clinical need your AI tool will address.

• Why is this problem significant for patients, clinicians, or healthcare systems?

2. Target Users and Impact

• Who will use your AI tool? (e.g., radiologists, ICU nurses, pathologists, patients)

• What benefits will users and patients experience?

3. AI Methodology

• Which AI approach will you employ? (e.g., machine learning classification, natural language processing, computer vision)

• Briefly outline the algorithm or model architecture you plan to use.

4. Data Requirements and Sources

• What types of data are needed? (e.g., medical images, lab results, patient histories)

• Identify potential data sources and how you will access or collect the data.

5. Ethical Safeguards

• How will you ensure patient privacy and secure data handling?

• What steps will you take to detect and mitigate algorithmic bias?

• How will you maintain transparency and accountability in your AI tool’s decisions?

6. Implementation Plan

• Describe key development phases and a rough timeline (e.g., data collection, model training, validation, deployment).

• What resources or collaborations will you need? (e.g., software, hardware, domain experts)

7. Evaluation and Metrics

• Which performance metrics will you use to assess your AI tool? (e.g., accuracy, sensitivity, specificity, AUC)

• How will you measure real-world impact on patient outcomes or clinical workflows?

8. Reflection

• What challenges do you anticipate during development and deployment?

• How will your solution contribute to responsible innovation in healthcare?

AI Medicine Debate Guide

Purpose: Enable students to critically evaluate the promise and perils of AI in medicine by debating from multiple perspectives.

Debate Format & Roles (30 minutes)

1. Divide into three teams (3–4 students each):
   • Pro-AI Team: Argues that AI’s benefits in healthcare outweigh the risks.
   • Critical-AI Team: Argues that AI’s risks and ethical concerns outweigh the benefits.
   • Moderator Team: Crafts the motion, enforces time limits, and leads Q&A.

2. Assign roles within teams:
   • Lead Speaker: Presents opening arguments (2 min)
   • Rebuttal Speaker: Responds to the other side’s points (2 min)
   • Closing Speaker: Offers summary and final reflection (1 min)

Preparation (10 minutes)

• Use insights from AI Diagnostics Case Studies and the AI Ethics Discussion Worksheet.
• Research additional examples or news articles to support your position.

Proposed Motions (choose one)

• “AI should be fully integrated in all diagnostic workflows by 2030.”
• “The potential harms of medical AI outweigh its benefits.”
• “Stricter regulations and human oversight are essential before deploying AI in patient care.”

Debate Procedure

1. Opening Statements (3 min per side): Pro-AI goes first, then Critical-AI.
2. Rebuttals (2 min per side): Respond directly to the opposing opening.
3. Audience Questions (5 min): Moderator fields 2–3 questions from classmates.
4. Closing Statements (1 min per side).

Potential Argument Themes

• Data Privacy & Security
– Pro: AI can anonymize datasets and detect breaches preemptively.
– Con: Large-scale data collection heightens risk of leaks and misuse.

• Diagnostic Accuracy & Bias
– Pro: AI can reduce human error and detect patterns invisible to clinicians.
– Con: Biased training data can propagate disparities in care.

• Accountability & Regulation
– Pro: Clear protocols and FDA-style approval can ensure safe AI tools.
– Con: Ambiguity around liability if AI errs puts patients at risk.

• Human Connection & Trust
– Pro: AI frees clinicians to spend more time with patients.
– Con: Overreliance on algorithms can erode the doctor–patient relationship.

Moderator Guidelines

• Keep strict time.
• Ensure fair turn-taking.
• Encourage evidence-based arguments.
• Summarize key points before opening audience Q&A.

Reflection & Takeaways (5 minutes)

After the debate, each student writes a brief response:

1. Which arguments were most convincing and why?
   
   
   
   
   
   
   
2. How has your perspective on AI in medicine changed?
   
   
   
   
   
   
   
3. What ethical or practical safeguards would you recommend for real-world AI deployment?