Academic machine learning researchers' ethical perspectives on algorithm development for health care: a qualitative study.

OBJECTIVES: We set out to describe academic machine learning (ML) researchers' ethical considerations regarding the development of ML tools intended for use in clinical care. MATERIALS AND METHODS: We conducted in-depth, semistructured interviews with a sample of ML researchers in medicine (N = 10) as part of a larger study investigating stakeholders' ethical considerations in the translation of ML tools in medicine. We used a qualitative descriptive design, applying conventional qualitative content analysis in order to allow participant perspectives to emerge directly from the data. RESULTS: Every participant viewed their algorithm development work as holding ethical significance. While participants shared positive attitudes toward continued ML innovation, they described concerns related to data sampling and labeling (eg, limitations to mitigating bias; ensuring the validity and integrity of data), and algorithm training and testing (eg, selecting quantitative targets; assessing reproducibility). Participants perceived a need to increase interdisciplinary training across stakeholders and to envision more coordinated and embedded approaches to addressing ethics issues. DISCUSSION AND CONCLUSION: Participants described key areas where increased support for ethics may be needed; technical challenges affecting clinical acceptability; and standards related to scientific integrity, beneficence, and justice that may be higher in medicine compared to other industries engaged in ML innovation. Our results help shed light on the perspectives of ML researchers in medicine regarding the range of ethical issues they encounter or anticipate in their work, including areas where more attention may be needed to support the successful development and integration of medical ML tools.

Physicians' and Machine Learning Researchers' Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study.

BACKGROUND: Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance. OBJECTIVE: This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine. METHODS: Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set. RESULTS: Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams. CONCLUSIONS: These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.

Factors Influencing Perceived Helpfulness and Participation in Innovative Research: A Pilot Study of Individuals with and without Mood Symptoms.

Little is known about how individuals with and without mood disorders perceive the inherent risks and helpfulness of participating in innovative psychiatric research, or about the factors that influence their willingness to participate. We conducted an online survey with 80 individuals (self-reported mood disorder [n = 25], self-reported good health [n = 55]) recruited via MTurk. We assessed respondents' perceptions of risk and helpfulness in study vignettes associated with two innovative research projects (intravenous ketamine therapy and wearable devices), as well as their willingness to participate in these projects. Respondents with and without mood disorders perceived risk similarly across projects. Respondents with no mood disorders viewed both projects as more helpful to society than to research volunteers, while respondents with mood disorders viewed the projects as equally helpful to volunteers and society. Individuals with mood disorders perceived ketamine research, and the two projects on average, as more helpful to research volunteers than did individuals without mood disorders. Our findings add to a limited empirical literature on the perspectives of volunteers in innovative psychiatric research.

Self-reported influences on willingness to receive COVID-19 vaccines among physically ill, mentally ill, and healthy individuals.

OBJECTIVE: Individuals with mental and physical disorders have been disproportionately affected by adverse health outcomes due to the COVID-19 pandemic, and yet vaccine hesitancy persists despite clear evidence of health benefits. Therefore, our study explored factors influencing willingness to receive a COVID-19 vaccine. METHODS: Individuals with mental illness (n = 332), physical illness (n = 331), and no health issues (n = 328) were recruited via Amazon Mechanical Turk. Participants rated willingness to obtain a fully approved COVID-19 vaccine or a vaccine approved only for experimental/emergency use and influences in six domains upon their views. We examined differences by health status. RESULTS: Participants across groups were moderately willing to receive a COVID-19 vaccine. Perceived risk was negatively associated with willingness. Participants differentiated between vaccine risk by approval stage and were less willing to receive an experimental vaccine. Individuals with mental illness rated risk of both vaccines similarly to healthy individuals. Individuals with physical illness expressed less willingness to receive an experimental vaccine. Domain influences differently affected willingness by health status as well as by vaccine approval status. CONCLUSIONS: Our findings are reassuring regarding the ability of people with mental disorders to appreciate risk in medical decision-making and the ability of people of varied health backgrounds to distinguish between the benefits and risks of clinical care and research, refuting the prevailing notions of psychiatric exceptionalism and therapeutic misconception. Our findings shine a light on potential paths forward to support vaccine acceptance.

Perceived protectiveness of research safeguards and influences on willingness to participate in research: A novel MTurk pilot study.

Little is known about how individuals with mood disorders view the protectiveness of research safeguards, and whether their views affect their willingness to participate in psychiatric research. We conducted an online survey with 80 individuals (self-reported mood disorder [n = 25], self-reported good health [n = 55]) recruited via MTurk. We assessed respondents' perceptions of the protectiveness of five common research safeguards, as well as their willingness to participate in research that incorporates each safeguard. Perceived protectiveness was strongly related to willingness to participate in research for four of the safeguards. Our findings add to a limited literature on the motivations and perspectives of key stakeholders in psychiatric research.