The Value of Artificial Intelligence in Laboratory Medicine

TLDR

Laboratory medicine is rapidly digitalizing, yet the benefits, evaluation, limitations, and implementation of artificial intelligence remain poorly understood. The study aimed to assess laboratory medicine stakeholders’ perceptions of AI’s value, anticipated challenges, and potential solutions. A web‑based survey of 128 stakeholders from Roche’s Strategic Advisory Network was conducted to gather these insights. Survey respondents—primarily medical practitioners and laboratory managers—reported that only 15.6 % of their organizations currently use AI, 66.4 % anticipate future use, but many are uncertain about adoption requirements, citing high costs, limited clinical evidence, governance, and privacy concerns, while calling for education, streamlined integration, and evidence generation to mainstream AI.

Abstract

As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evaluate it, what are its limitations, and how it can be implemented are not well understood. With a survey, we aimed to evaluate the thoughts of stakeholders in laboratory medicine on the value of AI in the diagnostics space and identify anticipated challenges and solutions to introducing AI.We conducted a web-based survey on the use of AI with participants from Roche's Strategic Advisory Network that included key stakeholders in laboratory medicine.In total, 128 of 302 stakeholders responded to the survey. Most of the participants were medical practitioners (26%) or laboratory managers (22%). AI is currently used in the organizations of 15.6%, while 66.4% felt they might use it in the future. Most had an unsure attitude on what they would need to adopt AI in the diagnostics space. High investment costs, lack of proven clinical benefits, number of decision makers, and privacy concerns were identified as barriers to adoption. Education in the value of AI, streamlined implementation and integration into existing workflows, and research to prove clinical utility were identified as solutions needed to mainstream AI in laboratory medicine.This survey demonstrates that specific knowledge of AI in the medical community is poor and that AI education is much needed. One strategy could be to implement new AI tools alongside existing tools.