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

 

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.

 

 

Glossary of terms

Artificial intelligence

An umbrella group of techniques that enable a computer algorithm to perform tasks typically associated with human intelligence

Automation

The use of technology and machines to perform tasks or processes with minimal human intervention

Decision support system

A computer-based tool or software that assists individuals or organisations in making informed decisions by providing data or performing analyse

Deep learning

A subset of machine learning that uses neural networks (like neurons in the human brain) to imitate human-like intelligence, automatically learning and extracting data features

Digital exclusion

Circumstances in which individuals or population groups lack ability to effectively engage with digital technologies, such as computers or the internet. Limited access and digital skills are often influenced by economic, social and geographical factors

Digitisation

The process of converting analogue information (e.g. paper documentation) into digital format, making it accessible and manipulable by computers

Generative techniques

A group of artificial- intelligence techniques that utilise models capable of producing novel content (e.g. text, images, video, audio, code) resembling the data they were trained on
Interoperability The ability of different digital systems, devices or software to exchange and use data or information, allowing them to work together

Machine learning

A subset of artificial intelligence that uses statistical methods to enable machines to improve performance (learn) with experience. The developer must typically manually engineer features

Natural language processing

A field of artificial intelligence that involves enabling machines to understand, interpret and generate human language

Personalised medicine

The tailoring of medical treatment to an individual's unique circumstances (e.g. genetics, demographics, medical history) to provide more effective clinical interventions

Robotics

The design, construction, operation and use of robots, which are autonomous or semi- autonomous machines capable of performing tasks

Conclusion
Although the development of AI-driven functionality in healthcare is expanding rapidly, AI-enabled DSS largely remain in their infancy. Globally, the market for AI health technologies is expected to grow at a compound annual growth rate of 38.4% from 2022 to 2030, reaching 208 billion US dollars by 2030 [76].

 

In parallel, future diabetes projections look bleak; forecasts estimate that by 2050, 1.31 billion people will be living with diabetes [77]. Recent decades have seen major cultural shifts in eating habits and activity levels that have catalysed an obesity crisis and, in 2021, 96% of diabetes cases were reported as being type 2 diabetes.

 

It is evident that a comprehensive approach is necessary and, although AI-based tools alone will be no panacea, their benefits must not be ignored. Such tools can be delivered at low cost and scaled throughout a popu- lation or clinical workforce to deliver significant benefit. The anticipated increase in data availability, coupled with enhanced data access, is likely to yield superior predictive abilities, utility, adoption and widespread clinical impact. Yet, the practical, timely and ethical integration of these tools into existing clinical scenarios continues to pose a challenge.

 

Nevertheless, the momentum is unmistakably shifting, and all stakeholders—citizens, public institutionsnand private organisations—must swiftly adapt to both reap the benefits and reduce the risk that our digitised and AI- enabled new world could bring.

 

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