A Process Framework for Ethically Deploying Artificial Intelligence in Oncology

Hantel, Andrew; Clancy, Dillon D; Kehl, Kenneth L.; Marron, Jonathan M.; Allen, Eliezer M. Van; Abel, Gregory A.

doi:10.1200/jco.22.01113

Cited by 15 publications

(12 citation statements)

References 33 publications

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“…In particular, the avoidance of humanoid interfaces could help remind patient users that AI is not human, helping to preserve dignity. 20 PF-AI technologies may also risk violating nonmaleficence because of lack of regulatory oversight, risk for error, 21 and lack of transparency in training data sets and algorithms. Patients may mistakenly assume that these technologies have been thoroughly validated, potentially leading to overreliance.…”

Section: Ethical Implicationsmentioning

confidence: 99%

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Digital Health to Patient-Facing Artificial Intelligence: Ethical Implications and Threats to Dignity for Patients With Cancer

Kelkar,

Hantel,

Koranteng

et al. 2024

JCO Oncol Pract

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Section: Ethical Implicationsmentioning

confidence: 99%

“…In particular, the avoidance of humanoid interfaces could help remind patient users that AI is not human, helping to preserve dignity. 20…”

Section: Ethical Implicationsmentioning

confidence: 99%

Digital Health to Patient-Facing Artificial Intelligence: Ethical Implications and Threats to Dignity for Patients With Cancer

Kelkar,

Hantel,

Koranteng

et al. 2024

JCO Oncol Pract

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“…Concerns have been raised over AI bias, explainability (ie, the ability of an AI model to explain how it reached a result), responsibility for error or misuse, and humans' deference to its results. [3][4][5] As the ethical deployment of AI in cancer care requires solutions that meet the needs of stakeholders, this study sought to examine oncologists' familiarity with AI and perspectives on these issues. As familiarity with a technology changes stakeholder perceptions of it, 6 and because academic research in AI is burgeoning, we hypothesized that responses would vary for oncologists practicing in academic settings compared with those in other practice settings.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence models with applications for oncology have recently been approved by the US Food and Drug Administration (FDA), and the increasing complexity of personalized cancer care makes the field of oncology poised for an AI revolution. Concerns have been raised over AI bias, explainability (ie, the ability of an AI model to explain how it reached a result), responsibility for error or misuse, and humans’ deference to its results . As the ethical deployment of AI in cancer care requires solutions that meet the needs of stakeholders, this study sought to examine oncologists’ familiarity with AI and perspectives on these issues.…”

Section: Introductionmentioning

confidence: 99%

Perspectives of Oncologists on the Ethical Implications of Using Artificial Intelligence for Cancer Care

Hantel,

Walsh,

Marron

et al. 2024

JAMA Netw Open

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ImportanceArtificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment.ObjectiveTo evaluate oncologists’ views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities.Design, Setting, and ParticipantsThis cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan &amp; Provider Enumeration System.Main Outcomes and MeasuresThe primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions.ResultsOf 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models.Conclusions and RelevanceIn this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise.

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“…These include nuanced questions related to balancing goals of cure and symptom palliation, navigating therapeutic misconceptions and conflicts of interest, and the role of hope/hype in emerging technologies such as precision medicine and artificial intelligence in oncology. [1][2][3][4] Despite these ethical challenges, limited empirical work has been performed examining the role and practice of clinical ethics in oncology. The bioethics literature is sparse in this space, with but a handful of descriptive analyses of ethics consultation volume, ethicist training, and consult themes/content.…”

mentioning

confidence: 99%

Ethics Consultation in Oncology: The Search for Quality in Quantity

Marron

Hantel

Abel

et al. 2022

JCO Oncology Practice

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A Process Framework for Ethically Deploying Artificial Intelligence in Oncology

Abstract: Author affiliations and support information (if applicable) appear at the end of this article.

Cited by 15 publications

References 33 publications

Digital Health to Patient-Facing Artificial Intelligence: Ethical Implications and Threats to Dignity for Patients With Cancer

Digital Health to Patient-Facing Artificial Intelligence: Ethical Implications and Threats to Dignity for Patients With Cancer

Perspectives of Oncologists on the Ethical Implications of Using Artificial Intelligence for Cancer Care

Ethics Consultation in Oncology: The Search for Quality in Quantity

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