Artificial intelligence (AI) is rapidly transforming the healthcare industry, offering numerous benefits such as improved diagnostics, personalised treatments, and streamlined administrative processes. What do the public, patients, and healthcare professionals think of the use of AI in healthcare?
Analysing 105 publications spanning 2001 to 2021, we explored the perspectives of the general public, patients, and healthcare professionals on the use of AI in healthcare. More specifically, we extracted information on how knowledgeable and familiar with AI they saw themselves, what they viewed as AI’s benefits, risks, and challenges, and how acceptable AI was to them. We also examined what they thought of AI’s development, implementation, regulations, and the Human – AI relationship.
Despite widespread positive attitudes toward AI integration, stakeholders voiced concerns, including data privacy, algorithmic bias, and professional autonomy. They doubted that care involving AI could be empathic. They called for increased education and training, highlighting the importance of transparency, validation, and patient-clinician involvement in AI development.
Our research emphasises the need to address the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethical AI implementation. As healthcare embraces AI, striking a delicate balance between technological advancement and ethical considerations becomes paramount for patient well-being and the sustainable progression of healthcare systems.
Find the original academic paper here: Vo, V., Chen, G., Aquino, Y. S. J., Carter, S. M., Do, Q. N., & Woode, M. E. (2023). Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Social Science & Medicine, 338, 116357. https://doi.org/10.1016/j.socscimed.2023.116357
This research is part of larger project funded by the National Health and Medical Research Council grant “The algorithm will see you now: ethical, legal and social implications of adopting machine learning systems for diagnosis and screening”.
Find out more about the Economic behaviour, incentives and preferences in health research theme.