Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

Abstract

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

Keywords: Artificial intelligence; Barriers; Big data; Clinical informatics; Decision support; Diabetes; Facilitators; Machine learning; Personalised medicine; Review; Type 2 diabetes.

Fig. 1

Data-driven tools can include simple calculators, rule-based approaches and AI-driven approaches. Each approach has varied applications in diabetes care. ICR, insulin:carbohydrate ratio. This figure is available as part of a downloadable slideset

Fig. 2

An overview of AI and its subcomponents, including machine learning and deep learning. ^aA feature refers to an individual and measurable characteristic of data and is typically numerical or categorical. This figure is available as part of a downloadable slideset

Fig. 3

An overview of multisource data inputs (home, hospital and environmental data), analysis techniques (simple calculators, rule-based approaches and AI-based approaches [depicted by the image of the calculator, exclamation mark and cogs, respectively]) and potential clinically relevant and actionable outputs relevant to diabetes care. This figure is available as part of a downloadable slideset

Fig. 4

Why is clinician decision support necessary? As shown in the figure, many factors demand clinician time and drive the complexity of diabetes care. Clinician DSS aim to improve efficiency and reduce cognitive burden through prompts, automation and targeted advice. Targeted and timely DSS can make up-to-date, evidence-based information (e.g. guidelines, treatments, monitoring requirements) available to clinicians to facilitate informed decision-making. Additionally, through the analysis and synthesis of diverse data sources, DSS can support streamlined and efficient care, which is crucial to address challenges like limited appointment times and high patient volumes. This figure is available as part of a downloadable slideset