We're implementing AI now, so why not ask us what to do? - How AI providers perceive and navigate the spread of diagnostic AI in complex healthcare systems

Soc Sci Med. 2024 Jan:340:116442. doi: 10.1016/j.socscimed.2023.116442. Epub 2023 Nov 22.

Abstract

Despite high expectations of artificial intelligence (AI) in medical diagnostics, predictions of its extensive and rapid adoption have so far not been matched by reality. AI providers seeking to promote and perpetuate the use of this technology are faced with the complex reality of embedding AI-enabled diagnostics across variable implementation contexts. In this study, we draw upon a complexity science approach and qualitative methodology to understand how AI providers perceive and navigate the spread of AI in complex healthcare systems. Using semi-structured, one-to-one interviews, we collected qualitative data from 14 providers of AI-enabled diagnostics. We triangulated the data by complementing the interviews with multiple sources, including a focus group of physicians with experience using these technologies. The notion of embedding allowed us to connect local implementation efforts with systemic diffusion. Our study reveals that AI providers self-organise to increase their adaptability when navigating the variable conditions and unpredictability of complex healthcare contexts. In addition to the tensions perceived by AI providers within the sociocultural, technological, and institutional subsystems of healthcare, we illustrate the practices emerging among them to mitigate these tensions: stealth science, agility, and digital ambidexterity. Our study contributes to the growing body of literature on the spread of AI in healthcare by capturing the view of technology providers and adding a new theoretical perspective through the lens of complexity science.

MeSH terms

  • Artificial Intelligence*
  • Data Accuracy
  • Focus Groups
  • Health Facilities
  • Humans
  • Physicians*