Abstract of Qualitative Research Article

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Abstract of Qualitative Research Article Sushant Anil Patil Department of Information Technology DSRT-837 M22 Professional Writing Dr. Amanda Tanner February 10, 2024

Amann, J., Vayena, E., Ormond, K. E., Frey, D., Madai, V. I., & Blasimme, A. (2023). Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. Plos one , 18 (1), e0279088. https://doi.org/10.1371/journal.pone.0279088 Author Qualifications: Dr. Julia Amann is a postdoctoral researcher at the Health Ethics and Policy Lab, Swiss Federal Institute of Technology (ETH Zurich), Switzerland. Her expertise lies in the fields of digital health technologies, bioethics, and health communication. She has a keen research interest in exploring the ethical implications of medical AI for both research and clinical practice. Dr. Amann's work critically evaluates the potential and challenges of medical AI and provides significant insights into ethical governance for healthcare innovation. Dr. Alessandro Blasimme is an accomplished bioethicist with a PhD from the University of Milan and a Master's degree from La Sapienza University of Rome. He has held research positions in France and Switzerland before joining ETH Zurich in 2017. His work focuses on ethical and policy issues in biomedical innovation. Dr. Vince Istvan Madai holds the position of principal investigator and team lead at the QUEST Centre for Responsible Research, which is a part of the Berlin Institute of Health (BIH) of Charité Berlin. Additionally, he is a visiting Professor of Medical Informatics at Birmingham City University. Dr. Madai's area of expertise focuses on trustworthy AI in healthcare and AI

ethics. He is particularly interested in practical healthcare AI research, explainable AI (xAI), bias, reproducibility, and the translation of AI into healthcare solutions and product development. Research Concern The study addresses a gap in understanding the subjective perspectives of healthcare professionals and patients regarding integrating medical AI into stroke care. This gap is significant because while there is considerable research on the technical capabilities and clinical applications of AI and machine learning in healthcare, there is less focus on the expectations, apprehensions, and ethical considerations from the viewpoint of end-users and providers. The study aims to contribute to a more human-centered approach to AI integration in healthcare by exploring the attitudes and expectations surrounding medical AI. It acknowledges that the success of AI technologies depends on their technical proficiency and acceptance by healthcare providers and patients, their fit within existing clinical workflows, and their alignment with the ethical standards of medical practice. This research is pivotal in weaving the broader narrative of AI within healthcare by underscoring the necessity of robust ethical frameworks that underpin the development and implementation of AI technologies. Additionally, the research will contribute to understanding how AI can shape these critical interactions and implicit trust required for effective healthcare delivery. The investigation also brings to light practical concerns, such as the redefinition of professional roles, responsibilities, and patient rights, thus identifying key areas where AI integration needs to be

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managed with acute sensitivity. Furthermore, the study advocates for the active engagement of diverse stakeholders in creating and overseeing AI solutions, ensuring that these innovations are attuned to the real-world intricacies of clinical care and patient welfare. By bridging these gaps, the research contributes valuable insights on how machine learning and AI can be thoughtfully tailored and adopted, promoting a responsible and beneficial evolution of healthcare services. Research Purpose Statement and Research Questions The study aims to elucidate the perspectives of various stakeholders on the use of medical AI in the context of stroke, with an emphasis on discerning the ethical, practical, and communicational expectations of such technologies. The primary research questions are: 1. What are the anticipated benefits and challenges of medical AI as perceived by healthcare professionals in stroke care? 2. How do patients view the role of AI in managing their health outcomes post-stroke? Precedent Literature Key literature underpinning this research includes studies on the efficacy of AI in diagnosing stroke, ethical considerations in the use of AI in medical settings, patient autonomy and AI, and the psychology of trust in AI systems (Lee et al., 2017). Noteworthy contributions in these areas have provided a foundation for understanding the complexities of AI applications in healthcare and have underscored the necessity for comprehensive stakeholder engagement (Jiang et al., 2017).

Research Methodology The population targeted by this study includes healthcare professionals with experience in stroke care and patients who have undergone stroke treatment. A purposive sampling strategy was employed to select participants who could provide rich, relevant, and diverse insights into the research questions. The researchers conducted a study in which they conducted 34 interviews with 35 participants. The participants were likely diverse, consisting of stroke survivors and possibly their partners, which can provide multiple perspectives on the subject matter. The interviews varied in length but averaged 40 minutes each, resulting in over 22 hours of audio material. The data was collected through these interviews and transcribed to facilitate detailed analysis. The presence of a participant with mild to moderate aphasia required an adaptive approach. The researchers supplemented the challenging transcription with field notes to capture the essence of that participant's input. Instrumentation The primary method used for collecting data was through an interview guide. This guide consisted of open-ended questions designed to investigate the expectations and attitudes of participants toward medical artificial intelligence in stroke care. For the participant with aphasia, the researchers relied on field notes. During the interview, the interviewer took these notes to

capture non-verbal cues, context, and other relevant information that may not have been easily discernible from the audio recording alone due to the participant's speech difficulties. The audio recordings of the interviews were also vital as they allowed the researchers to capture the complete details and nuances of participants' spoken responses. These recordings were then transcribed verbatim to aid in thorough analysis. The researchers gathered comprehensive qualitative data by combining interview guides, audio recordings, transcriptions, and field notes. Findings The study found many expectations and concerns regarding using medical AI in stroke care. Healthcare professionals are cautiously optimistic about AI's ability to improve diagnostic accuracy but also recognize the need for ethical guidelines. Patients have varying attitudes towards AI, ranging from excitement about potentially better outcomes to concerns about losing personal care. These qualitative data were analyzed thematically, presenting findings through narrative accounts and thematic matrices to highlight the multifaceted nature of stakeholder perspectives.

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References: Amann, J., Vayena, E., Ormond, K. E., Frey, D., Madai, V. I., & Blasimme, A. (2023). Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. PLOS ONE, 18(1), e0279088. https://doi.org/10.1371/journal.pone.0279088 Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology , 2 (4), 230–243. https://doi.org/10.1136/svn-2017-000101 Lee, E., Kim, Y., Kim, N., & Kang, D. (2017). Deep into the Brain: Artificial Intelligence in Stroke Imaging. Journal of Stroke , 19 (3), 277–285. https://doi.org/10.5853/jos.2017.02054

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