1
|
No-boundary thinking: a viable solution to ethical data-driven AI in precision medicine. AI AND ETHICS 2021; 2:635-643. [PMID: 34870283 PMCID: PMC8628283 DOI: 10.1007/s43681-021-00118-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/02/2021] [Indexed: 10/28/2022]
Abstract
AbstractToday Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.
Collapse
|
2
|
Moore JH, Boland MR, Camara PG, Chervitz H, Gonzalez G, Himes BE, Kim D, Mowery DL, Ritchie MD, Shen L, Urbanowicz RJ, Holmes JH. Preparing next-generation scientists for biomedical big data: artificial intelligence approaches. Per Med 2019; 16:247-257. [PMID: 30760118 DOI: 10.2217/pme-2018-0145] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data are being stored in electronic health records yielding big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making. We end with some specific training recommendations for preparing next-generation scientists for biomedical big data.
Collapse
Affiliation(s)
- Jason H Moore
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mary Regina Boland
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Pablo G Camara
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hannah Chervitz
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Graciela Gonzalez
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Blanca E Himes
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dokyoon Kim
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle L Mowery
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marylyn D Ritchie
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Li Shen
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ryan J Urbanowicz
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John H Holmes
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| |
Collapse
|
3
|
Ciesielski TH, Aldrich MC, Marsit CJ, Hiatt RA, Williams SM. Transdisciplinary approaches enhance the production of translational knowledge. Transl Res 2017; 182:123-134. [PMID: 27893987 PMCID: PMC5362296 DOI: 10.1016/j.trsl.2016.11.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 10/12/2016] [Accepted: 11/02/2016] [Indexed: 12/28/2022]
Abstract
The primary goal of translational research is to generate and apply knowledge that can improve human health. Although research conducted within the confines of a single discipline has helped us to achieve this goal in many settings, this unidisciplinary approach may not be optimal when disease causation is complex and health decisions are pressing. To address these issues, we suggest that transdisciplinary approaches can facilitate the progress of translational research, and we review publications that demonstrate what these approaches can look like. These examples serve to (1) demonstrate why transdisciplinary research is useful, and (2) stimulate a conversation about how it can be further promoted. While we note that open-minded communication is a prerequisite for germinating any transdisciplinary work and that epidemiologists can play a key role in promoting it, we do not propose a rigid protocol for conducting transdisciplinary research, as one really does not exist. These achievements were developed in settings where typical disciplinary and institutional barriers were surmountable, but they were not accomplished with a single predetermined plan. The benefits of cross-disciplinary communication are hard to predict a priori and a detailed research protocol or process may impede the realization of novel and important insights. Overall, these examples demonstrate that enhanced cross-disciplinary information exchange can serve as a starting point that helps researchers frame better questions, integrate more relevant evidence, and advance translational knowledge more effectively. Specifically, we discuss examples where transdisciplinary approaches are helping us to better explore, assess, and intervene to improve human health.
Collapse
Affiliation(s)
- Timothy H Ciesielski
- Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH; Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH; Public Health Program, Regis College, Weston, Mass.
| | - Melinda C Aldrich
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tenn; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
| | - Carmen J Marsit
- Department of Pharmacology and Toxicology, Geisel School of Medicine at Dartmouth, Hanover, NH; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH
| | - Robert A Hiatt
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, Calif
| | - Scott M Williams
- Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH; Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH
| |
Collapse
|
4
|
Moore JH, Holmes JH. The golden era of biomedical informatics has begun. BioData Min 2016; 9:15. [PMID: 27069509 PMCID: PMC4827231 DOI: 10.1186/s13040-016-0092-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 03/30/2016] [Indexed: 11/10/2022] Open
Abstract
Biomedical informatics has become a central focus for many academic medical centers and universities as biomedical research because increasingly reliant on the processing, analysis, and interpretation of large volumes of data, information, and knowledge. We posit here that this is the beginning of the golden era of biomedical informatics with opportunity for this maturing discipline to have a substantial impact on the biggest questions and challenges facing efforts to improve human health and the healthcare system.
Collapse
Affiliation(s)
- Jason H Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-6116 USA
| | - John H Holmes
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-6116 USA
| |
Collapse
|
5
|
Huang X, Jennings SF, Bruce B, Buchan A, Cai L, Chen P, Cramer CL, Guan W, Hilgert UK, Jiang H, Li Z, McClure G, McMullen DF, Nanduri B, Perkins A, Rekepalli B, Salem S, Specker J, Walker K, Wunsch D, Xiong D, Zhang S, Zhang Y, Zhao Z, Moore JH. Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm. BioData Min 2015; 8:7. [PMID: 25670967 PMCID: PMC4323225 DOI: 10.1186/s13040-015-0037-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 01/05/2015] [Indexed: 11/10/2022] Open
Abstract
Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not "lots of data" as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges.
Collapse
Affiliation(s)
- Xiuzhen Huang
- Department of Computer Science, Arkansas State University, Jonesboro, AR 72467 USA
| | | | - Barry Bruce
- Sustainable Energy & Education Research Center, University of Tennessee at Knoxville, Knoxville, TN 37996 USA
| | - Alison Buchan
- Department of Microbiology, University of Tennessee, Knoxville, TN 37996 USA
| | - Liming Cai
- Department of Computer Science, University of Georgia, Athens, GA 30602 USA
| | - Pengyin Chen
- Crop, Soil, and Environmental Sciences, University of Arkansas at Fayetteville, Fayetteville, AR 72701 USA
| | - Carole L Cramer
- Arkansas Biosciences Institute, Department of Biological Sciences, Arkansas State University, Jonesboro, AR 72467 USA
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 USA
| | - Uwe Kk Hilgert
- BIO5 Institute & iPlant Collaborative, The University of Arizona, PO Box 210240, Tucson, AZ 85721 USA
| | - Hongmei Jiang
- Department of Statistics, Northwestern University, Evanston, IL 60208 USA
| | - Zenglu Li
- Center for Applied Genetic Technologies, The University of Georgia, Athens, GA 30602 USA
| | - Gail McClure
- Arkansas Science & Technology Authority, Arkansas NSF EPSCoR, Little Rock, AR 72201 USA
| | - Donald F McMullen
- Arkansas High Performance Computing Center, University of Arkansas at Fayetteville, Fayetteville, AR 72701 USA
| | - Bindu Nanduri
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Jackson, MS 39762 USA
| | - Andy Perkins
- Department of Computer Science and Engineering, Mississippi State University, Jackson, MS 39762 USA
| | - Bhanu Rekepalli
- National Institute for Computational Sciences, Department of Electrical Engineering and Computer Science, UTK and ORNL, Oak Ridge, TN 37832 USA
| | - Saeed Salem
- Department of Computer Science, North Dakota State University, Fargo, ND 58102 USA
| | - Jennifer Specker
- Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 02882 USA
| | - Karl Walker
- Department of Computer Science, University of Arkansas at Pine Bluff, Arkansas, 71601 USA
| | - Donald Wunsch
- Department of Electrical & Computer Engineering, Missouri University of Science & Technology, Rolla, MO 65409 USA
| | - Donghai Xiong
- Department of Pharmacology and Toxicology, Medical College of Wisconsin, Milwaukee, WI 53223 USA
| | - Shuzhong Zhang
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Yu Zhang
- Department of Computer Science, Trinity University, San Antonio, TX 78212 USA
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203 USA
| | - Jason H Moore
- Department of Genetics, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756 USA
| |
Collapse
|