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In this episode of the Digital Pathology Podcast, I explore the ethical and bias considerations in AI and machine learning through the lens of pathology. This is part six of our special seven-part series based on the landmark Modern Pathology review co-authored by the UPMC group, including Matthew Hanna, Liam Pantanowitz, and Hooman Rashidi.

From data bias and algorithmic bias to labeling, sampling, and representation issues, I break down where biases in AI can arise—and what we, as medical data stewards, must do to recognize, mitigate, and avoid them.

🔬 Key Topics Covered:

• [00:00:00] Introduction and post-USCAP 2025 reflections
• [00:03:00] Overview of AI and ethics paper from Modern Pathology
• [00:06:00] What it means to be a “data steward” in pathology
• [00:08:00] Core ethical principles: autonomy, beneficence, justice & more
• [00:13:00] Types of bias in AI systems: data, sampling, algorithmic, labeling
• [00:22:00] Temporal and feedback loop bias examples in pathology
• [00:29:00] FDA involvement and global guidelines for ethical AI
• [00:34:00] Bias mitigation: from diverse datasets to ongoing monitoring
• [00:43:00] The FAIR principles for responsible data use
• [00:49:00] AI development & reporting frameworks: QUADAS, CONSORT, STARD

🩺 Why This Episode Matters:
If we want to deploy AI ethically and reliably in pathology, we must check our bias—not just once, but at every stage of AI development. This episode gives you practical tools, frameworks, and principles for building responsible AI workflows from the ground up.

🎧 Listen now and become a more conscious and capable digital pathology data steward.

👉 Get the Paper here: Ethical and Bias Considerations in Artificial Intelligence/Machine Learning

 📘 Explore more on this topic: https://digitalpathologyplace.com

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