Dataset Information

A survey of pregnant patients' perspectives on the implementation of artificial intelligence in clinical care.

ABSTRACT:

Objective

To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery.

Materials and methods

We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019-June 2020. We investigated the role and interplay of patient demographic factors, healthcare literacy, understanding of AI, comfort levels with various AI scenarios, and preferences for AI use in clinical care.

Results

Of the 349 parturients, 57.6% were between the ages of 25-34 years, 90.1% reported college or graduate education and 69.2% believed the benefits of AI use in clinical care outweighed the risks. Cluster analysis revealed 2 distinct groups: patients more comfortable with clinical AI use (Pro-AI) and those who preferred physician presence (AI-Cautious). Pro-AI patients had a higher degree of education, were more knowledgeable about AI use in their daily lives and saw AI use as a significant advancement in medicine. AI-Cautious patients reported a lack of human qualities and low trust in the technology as detriments to AI use.

Discussion

Patient trust and the preservation of the human physician-patient relationship are critical in moving forward with AI implementation in healthcare. Pregnant individuals are cautiously optimistic about AI use in their care.

Conclusion

Our findings provide insights into the status of AI use in perinatal care and provide a platform for driving patient-centered innovations.

SUBMITTER: Armero W

PROVIDER: S-EPMC9748543 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

A survey of pregnant patients' perspectives on the implementation of artificial intelligence in clinical care.

Armero William W Gray Kathryn J KJ Fields Kara G KG Cole Naida M NM Bates David W DW Kovacheva Vesela P VP

Journal of the American Medical Informatics Association : JAMIA 20221201 1

<h4>Objective</h4>To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery.<h4>Materials and methods</h4>We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019-June 2020. We investigated the role and interplay of patient demographic factors, healthcare li ...[more]

PMID: 36250788

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Project description:ImportanceUnderstanding the views and values of patients is of substantial importance to developing the ethical parameters of artificial intelligence (AI) use in medicine. Thus far, there is limited study on the views of children and youths. Their perspectives contribute meaningfully to the integration of AI in medicine.ObjectiveTo explore the moral attitudes and views of children and youths regarding research and clinical care involving health AI at the point of care.Design, setting, and participantsThis qualitative study recruited participants younger than 18 years during a 1-year period (October 2021 to March 2022) at a large urban pediatric hospital. A total of 44 individuals who were receiving or had previously received care at a hospital or rehabilitation clinic contacted the research team, but 15 were found to be ineligible. Of the 29 who consented to participate, 1 was lost to follow-up, resulting in 28 participants who completed the interview.ExposuresParticipants were interviewed using vignettes on 3 main themes: (1) health data research, (2) clinical AI trials, and (3) clinical use of AI.Main outcomes and measuresThematic description of values surrounding health data research, interventional AI research, and clinical use of AI.ResultsThe 28 participants included 6 children (ages, 10-12 years) and 22 youths (ages, 13-17 years) (16 female, 10 male, and 3 trans/nonbinary/gender diverse). Mean (SD) age was 15 (2) years. Participants were highly engaged and quite knowledgeable about AI. They expressed a positive view of research intended to help others and had strong feelings about the uses of their health data for AI. Participants expressed appreciation for the vulnerability of potential participants in interventional AI trials and reinforced the importance of respect for their preferences regardless of their decisional capacity. A strong theme for the prospective use of clinical AI was the desire to maintain bedside interaction between the patient and their physician.Conclusions and relevanceIn this study, children and youths reported generally positive views of AI, expressing strong interest and advocacy for their involvement in AI research and inclusion of their voices for shared decision-making with AI in clinical care. These findings suggest the need for more engagement of children and youths in health care AI research and integration.

Project description:BackgroundResources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers.ObjectiveThis study's objective was to survey AI specialists in health care to investigate developers' perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures.MethodsA web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed.ResultsA total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively.ConclusionsThis study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications.

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Dataset Information

A survey of pregnant patients' perspectives on the implementation of artificial intelligence in clinical care.

Objective

Materials and methods

Results

Discussion

Conclusion

Publications

A survey of pregnant patients' perspectives on the implementation of artificial intelligence in clinical care.

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