Abstract
Motivation: This study deals with the introduction of artificial intelligence (AI) in digital pathology (DP). The study starts from the highlights of a companion paper. Objective: The aim was to investigate the consensus and acceptance of the insiders on this issue. Procedure: An electronic survey based on the standardized package Microsoft Forms (Microsoft, Redmond, WA, USA) was proposed to a sample of biomedical laboratory technicians (149 admitted in the study, 76 males, 73 females, mean age 44.2 years). Results: The survey showed no criticality. It highlighted (a) the good perception of the basic training on both groups, and (b) a uniformly low perceived knowledge of AI (as arisen from the graded questions). Expectations, perceived general impact, perceived changes in the work-flow, and worries clearly emerged in the study. Conclusions: The of AI in DP is an unstoppable process, as well as the increase of the digitalization in the health domain. Stakeholders must not look with suspicion towards AI, which can represent an important resource, but should invest in monitoring and consensus training initiatives based also on electronic surveys.
Highlights
In a complementary study [1] we dealt with the introduction of artificial intelligence (AI) in digital pathology (DP)
SaInlusb.limneitwitiethlecthtreoanimcalolyf thoeasftiursdtys,awmepdleecoifdiendsidtoerpsr.opose a survey to investigate the accepAStunabnamlcyeiztaeinttdheleethcoteurotccononimcsaeelnl.ysutos aoffitrhset sinamsidpelerso.fPirnesliidmeirns.arily, we addressed the aspects of privaAcynaalnydzedtahtea osuectcuormitye..The questionnaire was checked for the compliance to the European GDPR 679/2016 and the Italian Decree 101/2018, as required by the Data Protection
The responses related to AI, Q8–10, showed a value below the current theoretical mean value (TMV) threshold in the two groups (p-value < 0.01)
Summary
In a complementary study [1] we dealt with the introduction of artificial intelligence (AI) in digital pathology (DP). This could lead to a second revolution in pathological diagnostics (starting from the first revolution determined by the introduction of DP techniques both in eHealth and mHealth [2,3]). There are many important implications related to the introduction of AI. These implications involve other disciplines ( connected to imaging) and other activities, from the work-flow to the training. Some important development guidelines have been identified. AI shows in DP (A) the potentiality to access and correlate large amount of data, and (B) direct prospective in the world of diagnostics
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