Artificial Intelligence in Clinical Practice: Opportunities and Safeguards (Live Webinar)

Artificial Intelligence in Clinical Practice: Opportunities and Safeguards (Live Webinar)

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Date: Thursday, October 23, 2025
Time: 3:00 PM – 4:00 PM ET
Duration: 60 minutes
Format: Live interactive webinar with Q&A

Description:
AI-driven tools are increasingly used to support diagnostics, clinical decision-making, and patient monitoring. This webinar reviews practical applications of AI in healthcare, including imaging, predictive analytics, and workflow automation. Faculty will also discuss risks such as algorithmic bias, data privacy, and regulatory oversight.

Learning Objectives:

  • Describe current AI applications in clinical practice.

  • Evaluate benefits and limitations of AI in diagnostics and treatment planning.

  • Identify ethical and regulatory considerations for safe adoption.

  • Incorporate AI tools into practice while maintaining patient-centered care.

Dr Evelyn Dodge

Professor of Biomedical Data Science

Johns Hopkins University School of Medicine

Dr. Evelyn Dodge is a biomedical data scientist with over 20 years of experience applying machine learning and big data analytics to population health. Her research emphasizes ensuring equity and transparency in AI algorithms used for diagnostics and treatment planning. Dr. Dodge has collaborated with the World Health Organization and national health systems on AI ethics frameworks, and she currently leads a multi-institutional consortium examining bias mitigation strategies in medical AI. A passionate educator, she mentors graduate students and clinicians in responsible innovation, bridging technology and patient-centered care.

  1. No Relevant Financial Relationships
Dr. Dodge has disclosed no relevant financial relationships with ineligible companies.

  1. Advisory/Consulting Disclosure

Dr. Dodge serves as an unpaid advisor on the World Health Organization’s Artificial Intelligence in Health Ethics panel. She has disclosed no relevant financial relationships with ineligible companies.

Dr Amy Smith

Associate Professor of Medicine

Department of Internal Medicine, University of California, San Francisco (UCSF)

Dr. Amy Smith is a physician and researcher specializing in clinical informatics and digital health. Her work focuses on integrating artificial intelligence and predictive analytics into electronic health records to support clinical decision-making and improve patient safety. Dr. Smith has led NIH-funded projects on machine learning models for early detection of sepsis and has published extensively on the ethical use of AI in clinical practice. She is a frequent speaker at international conferences on digital health innovation and serves on advisory panels shaping guidelines for responsible AI adoption in healthcare.

  1. No Relevant Financial Relationships
Dr. Smith has disclosed no relevant financial relationships with ineligible companies.

  1. Research Funding Disclosure

Dr. Smith receives research funding from the National Institutes of Health (NIH) for studies related to clinical decision support. She has disclosed no relevant financial relationships with ineligible companies.

Speaker Disclosures

Dr. Amy Smith

  • Financial Relationships: Dr. Smith has disclosed no relevant financial relationships with ineligible companies.

  • Non-Financial Relationships: Dr. Smith has disclosed no non-financial interests relevant to the content of this activity.

Dr. Evelyn Dodge

  • Financial Relationships: Dr. Dodge has disclosed no relevant financial relationships with ineligible companies.

  • Non-Financial Relationships: Dr. Dodge has disclosed no non-financial interests relevant to the content of this activity.

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Webinar
09/25/2025 at 4:45 PM (EDT)  |  60 minutes
09/25/2025 at 4:45 PM (EDT)  |  60 minutes
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Post Test Assessment
4 Questions  |  Unlimited attempts  |  0/175 points to pass
4 Questions  |  Unlimited attempts  |  0/175 points to pass Complete this short assessment to demonstrate your understanding of the course content. A passing score of 70% is required to earn CME credit. You may retake the test if necessary.
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CME Certificate of Completion
Up to 10.00 medical credits available  |  No certificate available
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