{'fraction_non_alphanumeric': 0.027309703660662404, 'fraction_numerical': 0.005229517722254503, 'mean_word_length': 5.3076923076923075, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Artificial intelligence in medicine: current trends and future possibilities.

Artificial intelligence (AI) research within medicine is growing rapidly. In 2016, healthcare AI projects attracted more investment than AI projects within any other sector of the global economy.1 However, among the excitement, there is equal scepticism, with some urging caution at inflated expectations.2 This article takes a close look at current trends in medical AI and the future possibilities for general practice.

Informing clinical decision making through insights from past data is the essence of evidence-based medicine. Traditionally, statistical methods have approached this task by characterising patterns within data as mathematical equations, for example, linear regression suggests a ‘line of best fit’. Through ‘machine learning’ (ML), AI provides techniques that uncover complex associations which cannot easily be reduced to an equation. For example, neural networks represent data through vast numbers of interconnected neurones in a similar fashion to the human brain. This allows ML systems to approach complex problem solving just as a clinician might — by carefully weighing evidence to reach reasoned conclusions. However, unlike a single clinician, these systems can simultaneously observe and rapidly process an almost limitless number of inputs. For example, an AI-driven smartphone app now capably handles the task of triaging 1.2 million people in North London to Accident & Emergency (A&E).3 Furthermore, these systems are able to learn from each incremental case and can be exposed, within minutes, to more cases than a clinician could see in many lifetimes. This is why an AI-driven application is able to …