Introduction to Artificial Intelligence in Healthcare
In this presentation, Dr. Bradley Malin discusses the emerging role of artificial intelligence (AI) within healthcare. Dr. Malin reviews the types of AI and their practical applicability to workflows and tools in healthcare, and the challenges of responsible integration.
This Artificial Intelligence in Healthcare Seminar Series session took place on January 14, 2025 at 12:00 p.m.
Activity Information
How to Claim Credit
You may claim credit after watching this activity.
You will be redirected to the BCM DCPD credit management site when claiming credit and may be asked to register or log in.
Needs Statement
Faculty in clinical, research, and educational roles often face challenges in integrating artificial intelligence (AI) into workflows because of limited foundational knowledge, ethical concerns, and insufficient exposure to practical tools that enhance efficiency and innovation. This series aims to bridge the gap between theoretical knowledge and real-world clinical practice, promote the use of research tools such as literature review automation and data analytics, and advance the application of AI-driven educational methods synergistically with the Learning Health System. By focusing on practical applications, ethical challenges, and future innovations, the series seeks to break down barriers to the implementation of AI and to foster innovation and collaboration.
Educational Objectives
At the conclusion of the activity, the participants should be able to:
- Differentiate between the types of artificial intelligence (e.g., predictive vs. generative) that hold relevance for healthcare.
- Recognize how artificial intelligence has the potential to impact different aspects of health and healthcare, ranging from basic science to clinical diagnostics.
- Demonstrate understanding of the challenges to the responsible application of artificial intelligence in healthcare.
Target Audience
Professional Categories
- Physicians
- Fellows
- Residents
- Nurses
- Other Health Professionals
Specialties
Interest Groups
- Artificial Intelligence and Technology
Competencies
- Patient Care and Procedural Skills
- Practice-Based Learning and Improvement
- Provide Patient-Centered Care
- Quality Improvement
- Roles/Responsibilities
- Systems-Based Practice
- Teams and Teamwork
- Utilize Informatics
- Values/Ethics for Interprofessional Practice
- Work in Interdisciplinary Teams
Activity Evaluation
Evaluation by questionnaire will address program content, presentation, and possible bias.
Educational Methods
- Lectures
Accreditation/Credit Designation
Baylor College of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
Baylor College of Medicine designates this enduring material activity for a maximum of 1.0 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Activity Director
Term of Approval
January 1, 2025 through January 31, 2027. Original release date: January 1, 2025.
Disclosure Policy
Baylor College of Medicine (BCM) is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education (CME) for physicians. BCM is committed to sponsoring CE activities that are scientifically based, accurate, current, and objectively presented.
In accordance with the ACCME Standards for Commercial Support, BCM has implemented a mechanism requiring everyone in a position to control the content of an educational activity (i.e., directors, planning committee members, faculty) to disclose any relevant financial relationships with commercial interests (drug/device companies) and manage/resolve any conflicts of interest prior to the activity. Individuals must disclose to participants the existence or non-existence of financial relationships at the time of the activity or within 24 months prior.
In addition, BCM has requested activity faculty/presenters to disclose to participants any unlabeled use or investigational use of pharmaceutical/device products; to use scientific or generic names (not trade names) in referring to products; and, if necessary to use a trade name, to use the names of similar products or those within a class. Faculty/presenters have also been requested to adhere to the ACCME's validation of clinical content statements.
BCM does not view the existence of financial relationships with commercial interests as implying bias or decreasing the value of a presentation. It is up to participants to determine whether the relationships influence the activity faculty with regard to exposition or conclusions. If at any time during this activity you feel that there has been commercial/promotional bias, notify the Activity Director or Activity Coordinator. Please answer the questions about balance and objectivity in the activity evaluation candidly.
All of the relevant financial relationships listed for these individuals have been mitigated.
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020;92(4):807-812. doi:10.1016/j.gie.2020.06.040.
Shortliffe EH. Mycin: A Knowledge-Based Computer Program Applied to Infectious Diseases. Proc Annu Symp Comput Appl Med Care. 1977;66-69.
Parmar R. Training Deep Neural Networks. Medium. September 11, 2018. Accessed January 24, 2025.
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/science.aax2342.
Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H. Synthetic data augmentation using GAN for improved liver lesion classification. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Published online April 2018. doi:10.1109/isbi.2018.8363576.
Zhang Z, Yan C, Mesa DA, Sun J, Malin BA. Ensuring electronic medical record simulation through better training, modeling, and evaluation. J Am Med Inform Assoc. 2020;27(1):99-108. doi:10.1093/jamia/ocz161.
Yan C, Zhang Z, Nyemba S, Malin BA. Generating Electronic Health Records with Multiple Data Types and Constraints. AMIA Annu Symp Proc. 2021;2020:1335-1344. Published 2021 Jan 25.
Zhang Z, Yan C, Lasko TA, Sun J, Malin BA. SynTEG: a framework for temporal structured electronic health data simulation. J Am Med Inform Assoc. 2021;28(3):596-604. doi:10.1093/jamia/ocaa262.
Zhang Z, Yan C, Malin BA. Keeping synthetic patients on track: feedback mechanisms to mitigate performance drift in longitudinal health data simulation. J Am Med Inform Assoc. 2022;29(11):1890-1898. doi:10.1093/jamia/ocac131.
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583-589. doi:10.1038/s41586-021-03819-2.
Abramson J, Adler J, Dunger J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024;630(8016):493-500. doi:10.1038/s41586-024-07487-w.
Gao S, Fang A, Huang Y, et al. Empowering biomedical discovery with AI agents. Cell. 2024;187(22):6125-6151. doi:10.1016/j.cell.2024.09.022.
Yan C, Grabowska ME, Dickson AL, et al. Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer's disease with real-world clinical validation. NPJ Digit Med. 2024;7(1):46. Published 2024 Feb 26. doi:10.1038/s41746-024-01038-3.
Yan C, Ong HH, Grabowska ME, et al. Large language models facilitate the generation of electronic health record phenotyping algorithms. J Am Med Inform Assoc. 2024;31(9):1994-2001. doi:10.1093/jamia/ocae072.
Liu S, Wright AP, Patterson BL, et al. Using AI-generated suggestions from ChatGPT to optimize clinical decision support. J Am Med Inform Assoc. 2023;30(7):1237-1245. doi:10.1093/jamia/ocad072.
Choi E, Biswal S, Malin B, Duke J, Stewart WF, Sun J. Generating Multi-label Discrete Patient Records using Generative Adversarial Networks. arXiv. 2017. doi:10.48550/arXiv.1703.06490.
Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H. Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification. arXiv. 2018. doi:10.48550/arXiv.1801.02385.
Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller PA. Data augmentation using synthetic data for time series classification with deep residual networks. ResearchGate. 2018.
Zhang J, Li Z, Das K, Malin BA, Kumar S. SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency. arXiv. 2023. doi:10.48550/arXiv.2311.01740.
Chen L, Zaharia M, Zou J. How is ChatGPT's behavior changing over time? arXiv. 2023. doi:10.48550/arXiv.2307.09009.
Sun Y, Yang XN, Yang SS, et al. Antigen-induced chimeric antigen receptor multimerization amplifies on-tumor cytotoxicity. Signal Transduct Target Ther. 2023;8(1):445. Published 2023 Dec 8. doi:10.1038/s41392-023-01686-z.
Disclosures
The following individual(s) has/have reported financial or other relationship(s) with commercial entities whose products/services may relate to the educational content of this activity:
Presenter
-
Bradley Malin, Ph.D.
Accenture Professor, Department of Biomedical Informatics
Vanderbilt University Medical Center
Disclosure:
Nothing to disclose.
Activity Director
-
Ruchi Gaba, M.D, FACE, FEAA
Associate Professor of Endocrinology
Baylor College of Medicine
Disclosure:
Nothing to disclose.
Planning Committee Members
-
-
Angela Catic, M.D., M.Ed.
Assistant Professor
Baylor College of Medicine
Disclosure:
Nothing to disclose.
-
Jessica Davila, Ph.D., M.S.
Associate Professor of Medicine
Baylor College of Medicine
Disclosure:
Nothing to disclose.
-
Ruchi Gaba, M.D, FACE, FEAA
Associate Professor of Endocrinology
Baylor College of Medicine
Disclosure:
Nothing to disclose.
-
Katherine Griffith, M.B.A.
Principal, Business Strategy & Development
Baylor College of Medicine
Disclosure:
Nothing to disclose.
-
Sashank Kaushik, M.D., M.B.A.
Assistant Professor
Baylor College of Medicine
Disclosure:
Nothing to disclose.
-
Nabil Mansour, M.D.
Assistant Professor
Baylor College of Medicine
Disclosure:
- Consultancy: Medtronic; Iterative Health (ended)
-
-
Chirayu Shah, M.D., M.Ed.
Associate Professor
Baylor College of Medicine
Disclosure:
Nothing to disclose.
-
Andrew J. Zimolzak, M.D., M.M.Sc.
Assistant Professor, Medicine-Health Services Research
Baylor College of Medicine, Michael E. DeBakey VA Medical Center
Disclosure:
- Ownership Interests (stock, stock options, excluding diversified mutual funds): Stryker Corporation (individual stocks)
Health Topics
Presenter:
