Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review

Čartolovni, Anto; Tomičić, Ana; Mosler, Elvira Lazić

doi:10.1016/j.ijmedinf.2022.104738

Cited by 96 publications

(48 citation statements)

References 72 publications

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“…Previous reviews have tended to focus only on some aspects of the process of implementation of AI in health care; for example, regulation and legal issues [24,25], trust and ethics [24][25][26][27][28][29], clinical and patient outcomes [30][31][32], and economic impact [33]. Others have focused their studies on specific AI applications for health care, such as predictive medicine, diagnostics, and clinical decision-making [9,30,34,35].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

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Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Insights gained during these events have not translated in effective mechanisms for integrating societal perspectives (apart from the establishment of the nanotechnology observatory which mostly monitors technological developments). In an adjacent domain, Čartolovni et al ( 2022 ) have examined the difficulties around integrating ethical, legal and social considerations into data driven decision support tools. Their extensive literature review in the area of decision-making tool in medicine point to a number of problems areas that are common in multiple fields.…”

Section: Discussion and The Integration Of “Concern”mentioning

confidence: 99%

(Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies—the case of nanomaterials

et al. 2022

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The utility of decision-making tools for the risk governance of nanotechnology is at the core of this paper. Those working in nanotechnology risk management have been prolific in creating such tools, many derived from European FP7 and H2020-funded projects. What is less clear is how such tools might assist the overarching ambition of creating a fair system of risk governance. In this paper, we reflect upon the role that tools might and should play in any system of risk governance. With many tools designed for the risk governance of this emerging technology falling into disuse, this paper provides an overview of extant tools and addresses their potential shortcomings. We also posit the need for a data readiness tool. With the EUs NMP13 family of research consortia about to report to the Commission on ways forward in terms of risk governance of this domain, this is a timely intervention on an important element of any risk governance system.

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“…Neither top-down nor bottom-up approaches to governance can make machines think like humans and have ethical awareness, either on a technical or a moral level. Some studies have shown that people are not opposed to the implementation of new technologies and that the main reason for people’s fear of AI decision making is based on a distrust of government [ 21 ], so it is particularly important to strengthen the potential ethical review and legal implications of the AI decision-making process [ 22 ] and to govern the ethical risks of AI decision making [ 23 ].…”

Section: Literature Reviewsmentioning

confidence: 99%

Ethical Risk Factors and Mechanisms in Artificial Intelligence Decision Making

Guan

Liye

Zhao

2022

Behavioral Sciences

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While artificial intelligence (AI) technology can enhance social wellbeing and progress, it also generates ethical decision-making dilemmas such as algorithmic discrimination, data bias, and unclear accountability. In this paper, we identify the ethical risk factors of AI decision making from the perspective of qualitative research, construct a risk-factor model of AI decision making ethical risks using rooting theory, and explore the mechanisms of interaction between risks through system dynamics, based on which risk management strategies are proposed. We find that technological uncertainty, incomplete data, and management errors are the main sources of ethical risks in AI decision making and that the intervention of risk governance elements can effectively block the social risks arising from algorithmic, technological, and data risks. Accordingly, we propose strategies for the governance of ethical risks in AI decision making from the perspectives of management, research, and development.

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Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review

Cited by 96 publications

References 72 publications

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Artificial Intelligence Applications in Health Care Practice: Scoping Review

(Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies—the case of nanomaterials

Ethical Risk Factors and Mechanisms in Artificial Intelligence Decision Making

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