17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Ethical Considerations in the Use of Artificial Intelligence and Machine Learning in Health Care: A Comprehensive Review

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing health care by offering unprecedented opportunities to enhance patient care, optimize clinical workflows, and advance medical research. However, the integration of AI and ML into healthcare systems raises significant ethical considerations that must be carefully addressed to ensure responsible and equitable deployment. This comprehensive review explored the multifaceted ethical considerations surrounding the use of AI and ML in health care, including privacy and data security, algorithmic bias, transparency, clinical validation, and professional responsibility. By critically examining these ethical dimensions, stakeholders can navigate the ethical complexities of AI and ML integration in health care, while safeguarding patient welfare and upholding ethical principles. By embracing ethical best practices and fostering collaboration across interdisciplinary teams, the healthcare community can harness the full potential of AI and ML technologies to usher in a new era of personalized data-driven health care that prioritizes patient well-being and equity.

          Related collections

          Most cited references48

          • Record: found
          • Abstract: found
          • Article: not found

          The potential for artificial intelligence in healthcare

          The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

            Background Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. Methods Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. Results Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. Conclusions To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Precision Medicine, AI, and the Future of Personalized Health Care

              The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.
                Bookmark

                Author and article information

                Journal
                Cureus
                Cureus
                2168-8184
                Cureus
                Cureus (Palo Alto (CA) )
                2168-8184
                15 June 2024
                June 2024
                : 16
                : 6
                : e62443
                Affiliations
                [1 ] Software Engineering, Independent Researcher, Trenton, USA
                [2 ] Software Development, Independent Researcher, Seattle, USA
                [3 ] Data Architecture, Independent Researcher, McKinney, USA
                [4 ] Program Management, Independent Researcher, West Orange, USA
                [5 ] Data Analytics, Independent Researcher, Roswell, USA
                [6 ] Data Analytics, Independent Researcher, Dallas, USA
                [7 ] Data Architecture, Independent Researcher, Rancho Cordova, USA
                Author notes
                Mitul Harishbhai Tilala tilalamitul@ 123456gmail.com
                Article
                10.7759/cureus.62443
                11249277
                39011215
                79271c42-5af9-472f-a808-f54901cccf33
                Copyright © 2024, Harishbhai Tilala et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 May 2024
                : 15 June 2024
                Categories
                Healthcare Technology

                professional responsibility,clinical validation,transparency,algorithmic bias,data security,privacy,ethics,healthcare,machine learning,artificial intelligence

                Comments

                Comment on this article