Dr. Kelly Cohen is a Professor of Aerospace Engineering at the University of Cincinnati and a leading authority in explainable, certifiable AI systems. With more than 31 years of experience in artificial intelligence, his research focuses on fuzzy logic, safety-critical systems, and responsible AI deployment in aerospace and autonomous environments. His lab’s work has received international recognition, with students earning top global research awards and building real-world AI products used in industry.

In this episode 190 of the Disruption Now Podcast, 👩‍🔬 Dr. Cohen explains:

What explainable AI really means for clinicians
How transparent models improve patient safety
Strategies to reduce algorithmic bias in healthcare systems
Real examples of XAI in diagnostics & treatment

This video is essential for tech leaders, AI researchers, data scientists, clinicians, and anyone interested in ethical, trustworthy AI in medicine.

📅 CHAPTERS / TIMESTAMPS

00:00 Introduction — Why XAI in Healthcare
02:15 Kelly Cohen Bio & Expertise
05:40 What Explainable AI Actually Is
11:20 Challenges in Medical AI Adoption
16:50 Case Study: XAI in Diagnostics
22:10 Reducing Bias in ML Models
28:35 Regulatory & Ethical Standards
33:50 Future of Explainability in Medicine
39:25 Audience Q&A Highlights
44:55 Final Thoughts & Next Steps

💡 Q&A SNIPPET

Q: What is explainable AI?
A: Explainable AI refers to systems where decisions can be understood, traced, and validated — critical for safety-critical applications like aerospace, healthcare, and autonomous vehicles.

Q: Why is black-box AI dangerous?
A: Without transparency, errors cannot be audited, responsibility is unclear, and humans become unknowing test subjects.

Q: What is insurable AI?
A: Insurable AI is AI that has been tested, quantified for risk, and certified to the point where insurers are willing to underwrite it — creating real accountability.

🔗 RESOURCES & HANDLES

Dr. Kelly Cohen LinkedIn:
🔗 https://www.linkedin.com/in/kelly-cohen-phd

Mentioned Concepts:
✔ Explainable AI (XAI)
✔ Model interpretability
✔ Algorithmic bias & fairness

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