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Shi J, Bendig D, Vollmar HC, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. J Med Internet Res 2023; 25:e45815. [PMID: 38064255 PMCID: PMC10746970 DOI: 10.2196/45815] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 08/16/2023] [Accepted: 09/30/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. OBJECTIVE In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. METHODS Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. RESULTS From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. CONCLUSIONS The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.
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Affiliation(s)
- Jin Shi
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | - David Bendig
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | | | - Peter Rasche
- Department of Healthcare, University of Applied Science - Hochschule Niederrhein, Krefeld, Germany
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Sorkin BC, Kuszak AJ, Bloss G, Fukagawa NK, Hoffman FA, Jafari M, Barrett B, Brown PN, Bushman FD, Casper S, Chilton FH, Coffey CS, Ferruzzi MG, Hopp DC, Kiely M, Lakens D, MacMillan JB, Meltzer DO, Pahor M, Paul J, Pritchett-Corning K, Quinney SK, Rehermann B, Setchell KD, Sipes NS, Stephens JM, Taylor DL, Tiriac H, Walters MA, Xi D, Zappalá G, Pauli GF. Improving natural product research translation: From source to clinical trial. FASEB J 2020; 34:41-65. [PMID: 31914647 PMCID: PMC7470648 DOI: 10.1096/fj.201902143r] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 10/12/2019] [Accepted: 10/21/2019] [Indexed: 12/28/2022]
Abstract
While great interest in health effects of natural product (NP) including dietary supplements and foods persists, promising preclinical NP research is not consistently translating into actionable clinical trial (CT) outcomes. Generally considered the gold standard for assessing safety and efficacy, CTs, especially phase III CTs, are costly and require rigorous planning to optimize the value of the information obtained. More effective bridging from NP research to CT was the goal of a September, 2018 transdisciplinary workshop. Participants emphasized that replicability and likelihood of successful translation depend on rigor in experimental design, interpretation, and reporting across the continuum of NP research. Discussions spanned good practices for NP characterization and quality control; use and interpretation of models (computational through in vivo) with strong clinical predictive validity; controls for experimental artefacts, especially for in vitro interrogation of bioactivity and mechanisms of action; rigorous assessment and interpretation of prior research; transparency in all reporting; and prioritization of research questions. Natural product clinical trials prioritized based on rigorous, convergent supporting data and current public health needs are most likely to be informative and ultimately affect public health. Thoughtful, coordinated implementation of these practices should enhance the knowledge gained from future NP research.
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Affiliation(s)
- Barbara C. Sorkin
- Office of Dietary Supplements, National Institutes of Health (NIH), Bethesda, MD, US
| | - Adam J. Kuszak
- Office of Dietary Supplements, National Institutes of Health (NIH), Bethesda, MD, US
| | - Gregory Bloss
- National Institute on Alcohol Abuse and Alcoholism, NIH, Bethesda, MD, US
| | | | | | | | | | - Paula N. Brown
- British Columbia Institute of Technology, Burnaby, British Columbia, Canada
| | | | - Steven Casper
- Office of Dietary Supplement Programs, Center for Food Safety and Applied Nutrition, Food and Drug Administration (FDA), Hyattsville, MD, US
| | - Floyd H. Chilton
- Department of Nutritional Sciences and the BIO5 Institute, University of Arizona, Tucson, AZ, US
| | | | - Mario G. Ferruzzi
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC, US
| | - D. Craig Hopp
- National Center for Complementary and Integrative Health, NIH, Bethesda, MD, US
| | - Mairead Kiely
- Cork Centre for Vitamin D and Nutrition Research, School of Food and Nutritional Sciences, University College Cork, Ireland
| | - Daniel Lakens
- Eindhoven University of Technology, Eindhoven, Netherlands
| | | | | | | | - Jeffrey Paul
- Drexel Graduate College of Biomedical Sciences, College of Medicine, Evanston, IL, US
| | | | | | - Barbara Rehermann
- National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD, US
| | | | - Nisha S. Sipes
- National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, US
| | | | | | - Hervé Tiriac
- University of California, San Diego, La Jolla, CA, US]
| | - Michael A. Walters
- Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, MN, US
| | - Dan Xi
- Office of Cancer Complementary and Alternative Medicine, National Cancer Institute, NIH, Shady Grove, MD, US
| | | | - Guido F. Pauli
- CENAPT and PCRPS, University of Illinois at Chicago College of Pharmacy, Chicago, IL, US
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