Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research

Abstract

ObjectivesGiven widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data in AI research.DesignA qualitative study involving six focus groups with members of the public. Participants discussed their views about AI in general, then were asked to share their thoughts about three realistic health AI research scenarios. Data were analysed using qualitative description thematic analysis.SettingsTwo cities in Ontario, Canada: Sudbury (400 km north of Toronto) and Mississauga (part of the Greater Toronto Area).ParticipantsForty-one purposively sampled members of the public (21M:20F, 25–65 years, median age 40).ResultsParticipants had low levels of prior knowledge of AI and mixed, mostly negative, perceptions of AI in general. Most endorsed using data for health AI research when there is strong potential for public benefit, providing that concerns about privacy, commercial motives and other risks were addressed. Inductive thematic analysis identified AI-specific hopes (eg, potential for faster and more accurate analyses, ability to use more data), fears (eg, loss of human touch, skill depreciation from over-reliance on machines) and conditions (eg, human verification of computer-aided decisions, transparency). There were mixed views about whether data subject consent is required for health AI research, with most participants wanting to know if, how and by whom their data were used. Though it was not an objective of the study, realistic health AI scenarios were found to have an educational effect.ConclusionsNotwithstanding concerns and limited knowledge about AI in general, most members of the general public in six focus groups in Ontario, Canada perceived benefits from health AI and conditionally supported the use of health data for AI research.

Women’s attitudes to the use of AI image readers: a case study from a national breast screening programme

“…This bears out the findings of recent smaller scale studies. 16–18 Women of all ages see human interaction in diagnosis as critical to their experience of high-quality care.…”

“… 15 A few qualitative studies, although with small sample sizes, have explored public perception of the use of AI in medicine. 16–18 A recent survey conducted in the Netherlands involving 922 participants examined the perception of the use of AI to read mammograms. It found that the women surveyed did not support the use of AI without a human reader.…”

Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey

“…However, patient and public perceptions regarding health data sharing for AI research are not sufficiently characterized [ 9 ]. Data sharing for AI research purposes is a controversial subject, and therefore, conditional public support for data sharing cannot be assumed to extend to this field of research [ 10 ]. Reasons for this include knowledge and understanding of AI in general [ 10 ], ethical concerns [ 11 ], and fears around the potential reidentification of anonymized personal health data [ 12 ].…”

“…Data sharing for AI research purposes is a controversial subject, and therefore, conditional public support for data sharing cannot be assumed to extend to this field of research [ 10 ]. Reasons for this include knowledge and understanding of AI in general [ 10 ], ethical concerns [ 11 ], and fears around the potential reidentification of anonymized personal health data [ 12 ]. Furthermore, recent negative media reports about large technology companies using health data for AI research [ 13 ] and several important data breaches and cyberattacks [ 14 ] may undermine public trust in this technology.…”

“…Despite these additional issues, there is limited research exploring patient perceptions on data sharing for AI research purposes [ 10 , 15 - 18 ]. If the promises of AI are to be truly realized in health care, strategic public debates are important to ensure that the public maintains trust in the technology and use of confidential health data [ 19 ].…”