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      Patient apprehensions about the use of artificial intelligence in healthcare

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          Abstract

          While there is significant enthusiasm in the medical community about the use of artificial intelligence (AI) technologies in healthcare, few research studies have sought to assess patient perspectives on these technologies. We conducted 15 focus groups examining patient views of diverse applications of AI in healthcare. Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in healthcare costs, data-source bias, and data security. We also found that patient acceptance of AI is contingent on mitigating these possible harms. Our results highlight an array of patient concerns that may limit enthusiasm for applications of AI in healthcare. Proactively addressing these concerns is critical for the flourishing of ethical innovation and ensuring the long-term success of AI applications in healthcare.

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          High-performance medicine: the convergence of human and artificial intelligence

          Eric Topol (2019)
          The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
          Article 0 comments Cited 1223 times – based on 0 reviews     Review now
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            Machine Learning in Medicine.

            Rahul Deo (2015)
            Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
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              The practical implementation of artificial intelligence technologies in medicine

              Jianxing He, Sally L. Baxter, Jie Xu … (2019)
              The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.
              Article 0 comments Cited 433 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Richard R. Sharp:
                ORCID: http://orcid.org/0000-0001-5441-2084
                Sharp.Richard@mayo.edu
                Journal
                Journal ID (nlm-ta): NPJ Digit Med
                Journal ID (iso-abbrev): NPJ Digit Med
                Title: NPJ Digital Medicine
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2398-6352
                Publication date (Electronic): 21 September 2021
                Publication date PMC-release: 21 September 2021
                Publication date Collection: 2021
                Volume: 4
                Electronic Location Identifier: 140
                Affiliations
                [1 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Biomedical Ethics Research Program, , Mayo Clinic Rochester, ; Rocheste, MN 55905-0002 USA
                [2 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Kern Center for the Science of Healthcare Delivery, , Mayo Clinic Rochester, ; Rocheste, MN 55905-0002 USA
                Author information
                http://orcid.org/0000-0001-9723-891X
                http://orcid.org/0000-0001-6317-1577
                http://orcid.org/0000-0001-9116-5940
                http://orcid.org/0000-0002-0755-6939
                http://orcid.org/0000-0001-5441-2084
                Article
                Publisher ID: 509
                DOI: 10.1038/s41746-021-00509-1
                PMC ID: 8455556
                PubMed ID: 34548621
                SO-VID: b2385d3e-e8c3-4c9d-8985-68dd2b5b553c
                Copyright © © The Author(s) 2021
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 18 February 2021
                Date accepted : 23 August 2021
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2021

                Keywords: medical ethics,health policy,translational research
                Data availability:
                Keywords: medical ethics, health policy, translational research

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