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The value of AI in laboratory medicine

Here we summarise a recent article, published in American Journal of Clinical Pathology, that evaluated the thoughts of stakeholders in laboratory medicine on the value of AI.

Laboratory medicine

Estimates show that 70% of decisions regarding a patient’s diagnosis, treatment and discharge are in part based on the results from laboratory tests. With increasing costs and the ever-increasing workload, there has been a call for optimisation of laboratory processes. As the field is transitioning into an era of big data and AI, the need for accurate, readily available and contextualised data is critical.

AI is showing promise in the analysis of complex medical data generated from diagnostics, medical records, claims and clinical trials. Automation and AI can fundamentally change the way laboratories work. Additionally, they have the potential to improve diagnostics through more accurate detection, better laboratory workflows, improved decision support and reduced costs. Nonetheless, as laboratory medicine become more digitised and automated, several challenges associated with evaluating, implementing and validating AI algorithms will arise. AI algorithms can only properly function with reliable and accurate data. Introducing new technologies will also require willingness to change the current structure and mindset throughout organisations.

The value of AI

In this study, using a web-based survey, researchers evaluated current perspectives on the value of AI in the diagnostics space. They also identified anticipated challenges with the introduction of AI into the field, as well as resistance to emerging technologies.

In total, 128 of 302 stakeholders responded to the survey. They found that 15.6% of the organisations are currently using AI, while 66.4% felt they would use it in the future. Majority of individuals were unsure about what they would need to adopt AI in the diagnostics space. The team identified high investment costs, lack of proven clinical benefits, number of decision makers and privacy concerns as barriers to adoption. Moreover, individuals suggested education in the value of AI and research to prove clinical utility as the solutions needed to mainstream AI.

AI in laboratory medicine can help reduce healthcare costs, improve access to generate better insights and enhance the quality of care delivered to patients. Nonetheless, this study raises the concern that specific knowledge on AI in the medical community is generally still poor. This emphasises the need to educate the medical community. Additionally, the authors noted that an initial strategy could be to implement new AI tools alongside existing tools.

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