The Value of Artificial Intelligence in Laboratory Medicine

Current Opinions and Barriers to Implementation

Ketan Paranjape, MS, MBA; Michiel Schinkel, MD; Richard D. Hammer, MD; Bo Schouten, MSc; R. S. Nannan Panday, MD, PhD; Paul W. G. Elbers, MD, PhD; Mark H. H. Kramer, MD; Prabath Nanayakkara, MD, PhD

Disclosures

Am J Clin Pathol. 2021;155(6):823-831. 

In This Article

Abstract and Introduction

Abstract

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Introduction

Advances in our understanding of biology, disease, and molecular medicine have created a central role for laboratory medicine in the diagnostic workup of many, if not most, diseases. It is estimated that 70% of decisions regarding a patient's diagnosis, treatment, and discharge are in part based on results of laboratory tests.[1] Unfortunately, the main cause of medical errors in the United States is inaccurate diagnosis.[2–5] The ever-increasing workload, high health care costs, and need for improved precision call for continuous optimization of the laboratory processes.[6] With both health care and laboratory medicine[7] transitioning into an era of big data and artificial intelligence (AI), the ability to provide accurate, readily available, and contextualized data is crucial. AI in health care is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data generated from diagnostics, medical records, claims, clinical trials, and so on. AI algorithms can only properly function with reliable and accurate laboratory data.[8] Automation and AI can fundamentally change the way medicine is practiced, as demonstrated by the recent applications in ophthalmology[9] and dermatology.[10] Some possible applications of AI specific to laboratory medicine are presented in Table 1. With the increasing role of laboratory medicine in many diseases, AI has the potential to improve diagnostics through more accurate detection of pathology, better laboratory workflows, improved decision support, and reduced costs, leading to higher efficiencies.[8,11,12]

As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with evaluating, implementing, and validating AI algorithms, both inside and outside their laboratories. Understanding what AI is good for and where it can be applied, along with the state-of-the-art and limitations, will be useful to practicing laboratory professionals and clinicians. On the other hand, the introduction of new technologies requires willingness to change the current structure and mindset toward these technologies, which are not always well understood. Historically, there has been resistance to the adoption of new technologies in the medical community.[13]

With a web-based survey among stakeholders in laboratory medicine in the United States, we aimed to evaluate their current perspectives on the value of AI in the diagnostics space and identify anticipated challenges with the introduction of AI in this field, as well as resistance to introduction of this new technology in today's practice.

Today, AI is occasionally used in laboratory medicine for enabling the effective use of resources, avoiding unnecessary tests, improving patient safety, and alerting for abnormal results.[14–18] AI is also being used in limited clinical usage for molecular/genomic testing[19–21] by accurately identifying variants and matching it to possible treatments.

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