In this episode of the Responsible AI Report, Patrick speaks with Caroline Brzezinski and Dr. Amber Jolley-Paige from mpathic about the intersection of AI and healthcare. They discuss the importance of measuring AI accuracy, the need for standardized testing, acceptable error rates in medical AI, and current trends in AI adoption within the healthcare sector. The conversation emphasizes the critical role of human oversight and expert involvement in ensuring the safety and efficacy of AI tools in medical applications.

Takeaways

• AI in healthcare requires domain-specific validation.
• Human oversight is essential for AI accuracy.
• Standardized testing for medical AI is crucial.
• Acceptable error rates depend on potential harm.
• Different healthcare sectors adopt AI at varying rates.
• Generative AI is just one aspect of healthcare AI.
• AI tools must be tailored to specific medical needs.
• Experts should guide AI development and deployment.
• The healthcare industry is still figuring out best practices.
• AI advancements necessitate ongoing regulatory discussions.

Learn more by visiting:
https://mpathic.ai/
https://www.linkedin.com/in/amber-jolley-paige-ph-d-72041b46/
https://www.linkedin.com/in/caraline-7b22588b/

Dr. Jolley is a licensed professional counselor, researcher, and educator with over a decade of experience in the mental health field. As the Vice President of Clinical Product and a founding team member at mpathic, she leads a team that utilizes an evidence-based labeling system to advance natural language processing technologies. Dr. Jolley leverages her extensive clinical, research, and teaching background to develop a conversation and insights engine, providing individuals and organizations with actionable insights for enhanced understanding.

Caraline Bruzinski is a Senior Machine Learning Engineer at mpathic, where she models clinical trial data from therapist-client sessions with a focus on measuring empathy and therapist-patient conversational outcomes. Caraline specializes in refining models to achieve higher accuracy and reliability, developing custom ML models tailored to address specific clinical setting challenges, and conducting statistical analysis to enhance the accuracy and fairness of machine learning outcomes. With a Master’s degree in Computer Science, specifically focusing on AI/ML, from New York University and a background in data engineering, she brings extensive experience from her previous roles, including as Tech Lead at Glossier Inc. There, she developed a recommendation system that boosted sales by over $2M annually.

The Responsible AI Report is produced by the Responsible AI Institute.

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