InfoGuides: Health Informatics: Computable Biomedical Knowledge

Computable Biomedical Knowledge

Mobilizing Computable Biomedical Knowledge
Mobilizing Computable Biomedical Knowledge is an international community from academia, the sciences, and government working together to ensure that biomedical knowledge in computable form is findable, accessible, interoperable, and reusable.
How can Computable Biomedical Knowledge (CBK) Transform Health? Expert Perspectives

How Can Computable Biomedical Knowledge (CBK) Transform Health?
Experts from Informatics, Library, Clinical and Health System communities discuss CBK
• What impacts will CBK have on health and healthcare?
• How can Computable Biomedical Knowledge positively affect health and health care?

1. Eric Poon, MD, MPH, FACMI, Chief Health information Officer, Duke Medicine, Duke University
2. Tanner J. Caverly, MD, MPH, Assistant Professor, Departments of Learning Health Sciences and Internal Medicine
3. Gerald Perry, MLS, Health Science Library, Univ of AZ
4. Daniel Masys, MD, Biomedical and health informatics, University of Washington

Health decisions should be informed by the best available knowledge. Research at the intersections of health equity, health disparities, and computable biomedical knowledge (CBK) applications.

Allee NJ, Perry G, Rios GR, et al. Mobilizing health equity through Computable Biomedical Knowledge (CBK): a call to action to the library, information sciences, and health informatics communities. J Med Libr Assoc. 2024;112(2):158-163. doi:10.5195/jmla.2024.1836

"Recognizing that the future of health care will be informed by advanced information technologies including artificial intelligence (AI), machine learning, and algorithmic applications, the authors, all active in the Mobilizing Computable Biomedical Knowledge (MCBK) community argue that in order to advance towards states of improved health equity, knowledge workers, health and biomedical researchers, healthcare practitioners, government agencies, philanthropy, industry, consumer health advocacy and community-based organizations all need to be engaged in and encouraging the conduct of research at the intersections of health equity, health disparities, and computable biomedical knowledge (CBK) applications. '

Learning Health Systems

University of Michigan Medical School Department of Learning Health Sciences: Revolutionizing Learning, Transforming Health
The Department of Learning Health Sciences (DLHS) is a first-in-the-nation basic science department focused on the sciences related to learning across multiple levels of scale-individual, group, organization, region and nation.

Christophers, L., Torok, Z., Hudson, C., Rafter, T., Trayer, Á., Chao-Chi Hong, G., & Carroll, Á. (2025). Learning health systems and learning organisations in post-acute rehabilitation care: a scoping review. Disability and rehabilitation, 1–9. Advance online publication. https://doi.org/10.1080/09638288.2025.2458746

McNeilly, S. M., Wang, K. W., Jacobs, S. A., Yone, N. S., Williams, D. A., Rapkin, B. D., Joseph, C. D., & Gutnick, D. N. (2025). Towards a learning healthcare community in the Bronx: evaluating the Bridging Research, Accurate Information and Dialogue (BRAID) model. Health research policy and systems, 23(1), 20. https://doi.org/10.1186/s12961-025-01289-w

Closed Loop Clinical Decision Support Systems

Gracia Martínez, J. L., Pfang, B., Morales Coca, M. Á., Caramés Sánchez, C., Del Olmo Rodríguez, M., Villegas García, M. A., Short Apellaniz, J., Arcos Campillo, J., Álvaro de la Parra, J. A., Manzano Lorefice, F., & Muñoz Alameda, L. E. (2025). Implementing a closed loop clinical decision support system for sustainable preoperative care. NPJ digital medicine, 8(1), 6. https://doi.org/10.1038/s41746-024-01371-7

Reading List

Provided by Philip Walker, Director of Eskind Biomedical Library, Vanderbilt University

Askin, N., Ostapyk, T., & Epp, C. (2025). Filtering failure: the impact of automated indexing in Medline on retrieval of human studies for knowledge synthesis. Journal of the Medical Library Association : JMLA, 113(1), 58–64. https://doi.org/10.5195/jmla.2025.1972

•Use of the search filter 'exp animals/not humans.sh' is a well-established method in evidence synthesis to exclude non-human studies. However, the shift to automated indexing of Medline records has raised concerns about the use of subject-heading-based search techniques.

Blaizot, A., Veettil, S. K., Saidoung, P., Moreno-Garcia, C. F., Wiratunga, N., Aceves-Martins, M., Lai, N. M., & Chaiyakunapruk, N. (2022). Using artificial intelligence methods for systematic review in health sciences: A systematic review. Research synthesis methods, 13(3), 353–362. https://doi.org/10.1002/jrsm.1553

•This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the reported benefits and challenges in using such methods.

Blasingame, M. N., Koonce, T. Y., Williams, A. M., Giuse, D. A., Su, J., Krump, P. A., & Giuse, N. B. (2025). Evaluating a large language model’s ability to answer clinicians’ requests for evidence summaries. Journal of the Medical Library Association: JMLA, 113(1), 65–77. https://doi.org/10.5195/jmla.2025.1985

•This study investigated the performance of a generative artificial intelligence (AI) tool using GPT-4 in answering clinical questions in comparison with medical librarians’ gold-standard evidence syntheses.

Buetow, S., & Lovatt, J. (2024). From insight to innovation: Harnessing artificial intelligence for dynamic literature reviews. The Journal of Academic Librarianship, 50(4), 102901. https://doi.org/10.1016/j.acalib.2024.102901

•This paper examines how AI is reshaping reviews, potentially extending their meaning beyond text-based sources to accommodate multimedia content and predictive insights

•https://copyrightalliance.org/education/artificial-intelligence-copyright/

Crigger, E., Reinbold, K., Hanson, C., Kao, A., Blake, K., & Irons, M. (2022). Trustworthy Augmented Intelligence in Health Care. Journal of medical systems, 46(2), 12. https://doi.org/10.1007/s10916-021-01790-z

•To develop actionable guidance for trustworthy AI in health care, the AMA reviewed literature on the challenges health care AI poses and reflected on existing guidance as a starting point for addressing those challenges (including models for regulating the introduction of innovative technologies into clinical care).

Jin, Q., Leaman, R., & Lu, Z. (2024). PubMed and beyond: biomedical literature search in the age of artificial intelligence. EBioMedicine, 100, 104988. https://doi.org/10.1016/j.ebiom.2024.104988

•An overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs.

Novak, L. L., Russell, R. G., Garvey, K., Patel, M., Craig, K. J. T., Snowdon, J., & Miller, B. (2023). Clinical use of artificial intelligence requires AI-capable organizations. JAMIA Open, 6(2), ooad028. https://doi.org/10.1093/jamiaopen/ooad028

•Ideas are starting to emerge about the organizational routines, competencies, resources, and infrastructures that will be required for safe and effective deployment of AI in health care, but there has been little empirical research.

Research Libraries Guiding Principles for Artificial Intelligence

•https://www.arl.org/wp-content/uploads/2024/04/Research-Libraries-Guiding-Principles-for-Artificial-Intelligence.pdf

Sun, Grace H., and Stephanie H. Hoelscher. “The ChatGPT Storm and What Faculty Can Do.” Nurse Educator 48, no. 3 (2023). https://journals.lww.com/nurseeducatoronline/fulltext/2023/05000/the_chatgpt_storm_and_what_faculty_can_do.1.aspx.

•The exponential rise in the popularity of ChatGPT, and concerns of academic integrity with its use, has raised concerns among faculty for how to best address this issue.

George Mason University Libraries AI Salon Series 2024-2025

sa·lon: The word salon is French, originally meaning "reception room." In 1800's France, the meaning grew to include a "gathering of elegant people" occurring regularly in said room.

Event Description: The AI Salon series brings community members together to discuss current events and intersectional topics surrounding artificial intelligence. In the style of scientific salons, a Mason librarian will provide a brief presentation about an AI topic. Following the presentation, we’ll open the floor for a lively discussion where you can share your thoughts, ask questions, and connect with audience members. This is a great chance to engage with critical issues and learn from each other.

Audience:  This series is open to faculty, students, staff, alumni, and members of the campus community interested in exploring the many ways AI is shaping our lives and the future across disciplines. Each session will examine the evolving landscape of artificial intelligence and its significance in society. Whether you're an AI enthusiast or curious about the topic, we invite you to join us for these interactive sessions. We look forward to your participation and insights in this enriching salon series! 

You asked and we listened! All salons offer a virtual option and an in-person option.

Join us for one or both sessions for lively discussion (or mindful listening in) as our colleagues explore these timely issues. Note: To ensure open dialog and to build community, salons will not be recorded.

Location: Our in-person salons will be held in the Fenwick Library Main Reading Room (Level 2) on Fairfax campus. Our online salons will be held on Zoom.

Registration: Visit https://library.gmu.edu/workshops to register for online and in-person sessions and to receive a calendar invite. Zoom links will be provided through this registration.

March 2025 Salon - AI in Healthcare and Medicine

Moderated by Elaine Hicks, Health Sciences Librarian (George Mason University Libraries)

Date: Tuesday, March 4, 2025 from 1:00pm - 2:00pm Eastern Time

Campus: Online Session

Date: Wednesday, March 5, 2025 from 1:00pm - 2:00pm Eastern Time

Location: 2001 Fenwick Library, Main Reading Room

AI in Healthcare and Medicine
March 4 and 5, 2025