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.

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.

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.

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.

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.