Artificial intelligence in primary care

Abstract

Artificial intelligence (AI) in medicine captures the imagination with concepts such as the digital doctor, but also raises concerns such as replacing healthcare professionals, undermining trust in clinicians, and exacerbating inequalities. There is also continued uncertainty as to exactly what AI is. Given this confusion about definition, purpose, and potential, the RCGP has published an introductory report aimed at clarifying the position,1 in which a broad definition of AI as software with decision making capacity is used, ranging from software sequentially going through a series of yes/no questions (decision trees and algorithms) at the simplest, to complex software that learns from data (machine learning) where millions of datasets are used, for example, to find a pattern linking a set of symptoms and a disease. Often, development of AI tools for place-based care and self-care is predominately driven by a focus on the technology and a commercial need to find a market, rather than the challenges and needs of healthcare professionals, practice and community providers, and patients. Unlocking the potential for AI means we must have healthcare professionals, patients, and technology experts working together and engaging with policy makers and commissioners. The development and provision of AI continues to progress rapidly. The Academic Health Science Network (AHSN), established by NHS England to spread innovation, released the results of a recent AI mapping survey,2 providing an overview of AI healthcare activity. While not exhaustive (organisations including Babylon and Livi are missing), the survey provides some useful insights. Primary and community care AI clusters around two areas. First, clinical decision making and care management, for example, symptom assessment, automating clinical coding, image recognition for dermatological conditions, triaging, and personalised self-management. Second, proactive detection, such as, analysing patient …