A rapid review exploring the effectiveness of artificial intelligence for cancer diagnosis

Abstract

There is growing demand for diagnostic services in the UK. This rapid review aimed to assess the effectiveness of artificial intelligence (AI) in diagnostic radiology with a focus on cancer diagnosis. A range of AI models including machine learning, deep learning and ensemble models, were assessed in this review. The review included an initial broad mapping exercise and a more in-depth synthesis of a specific sub-set of the evidence. The review included evidence available from 2018 until June 2023. A total of 92 comparative primary studies were included in the evidence map. The evidence map identified 52 studies in which the AI models were in the early stages of development and validation, and highlighted breast, lung and prostate cancers as the type of cancers most frequently reported on. 28 studies evaluating an established model and focusing on the diagnosis of breast, lung, and prostate cancer were included in the in-depth synthesis. All studies included in the in-depth synthesis were classified as diagnostic accuracy studies. Only one study evaluated an AI model that was commercially available in the UK. Most studies reported results in favour of the AI models, however, these improvements were not always statistically significant. The studies also varied considerably in terms of AI models studied, type of cancer, images used, and comparison made; and were limited in terms of their methodology. When used as a standalone diagnostic tool, there is evidence to suggest that AI can improve diagnostic accuracy or is comparable to experienced radiologists, however this may be dependent on the AI model being used. There is evidence to suggest that AI may be beneficial when used as a support tool for clinicians/radiologists with less experience. The impact of AI on the timeline involved in diagnosis appeared inconsistent. AI may speed up the diagnostic timeline when the level of cancer suspicion is low but may increase diagnostic timelines when the level of cancer suspicion is high. The evidence suggests that clinicians are accepting of AI-based assistance for cancer diagnosis. Policy and practice implications The overall evidence for effectiveness appeared in favour of AI and several factors were identified that impact the effectiveness of the AI models. AI may improve diagnostic accuracy in clinicians/radiologists with less experience of interpreting radiological images. However, further well-designed high-quality research is needed from the UK and similar countries to better understand the effectiveness of AI in cancer diagnosis. Economic considerations There is little evidence on the cost-effectiveness of using AI for cancer diagnosis. In theory, it might be possible for AI to assist with earlier diagnosis of cancer with both health and economic benefits. Funding statement The Public Health Wales Observatory was funded for this work by the Health and Care Research Wales Evidence Centre, itself funded by Health and Care Research Wales on behalf of Welsh Government. EXECUTIVE SUMMARY What is a Rapid Review? Our rapid reviews (RR) use a variation of the systematic review approach, abbreviating or omitting some components to generate the evidence to inform stakeholders promptly whilst maintaining attention to bias. Who is this Rapid Review for? The review question was suggested by the Health Sciences Directorate (Policy). Background / Aim of Rapid Review There is growing demand for diagnostic services in the UK. The use of artificial intelligence in diagnosis is part of the Welsh Government’s programme for transforming and modernising planned care and reducing waiting lists in Wales. This rapid review aimed to assess the effectiveness of artificial intelligence (AI) in diagnostic radiology with a focus on cancer diagnosis. A range of AI models including machine learning, deep learning and ensemble models, were assessed in this review. The term ‘AI models’ was therefore used to encompass these different types of AI models described in the literature. The review included an initial broad mapping exercise and a more in-depth synthesis of a specific sub-set of the evidence. The focus of the in-depth synthesis was informed by the review’s stakeholders based on the findings of the mapping exercise. Results Recency of the evidence base The review included evidence available from 2018 until June 2023. Extent of the evidence base A total of 92 comparative primary studies were included in the evidence map. The evidence map identified 52 studies in which the AI models were in the early stages of development and validation, and highlighted breast, lung and prostate cancers as the type of cancers most frequently reported on. 28 studies evaluating an established model and focusing on the diagnosis of breast (n=14), lung (n=7) and prostate (n=7) cancer were included in the in-depth synthesis. Studies included in the in-depth synthesis were conducted in the USA (n=8), Japan (n=5), UK (n=2), Italy (n=2), Turkey (n=2), Germany (n=2), Netherlands (n=2), Portugal (n=1), Greece (n=1) and Norway (n=1). Two studies were conducted across multiple countries. All studies included in the in-depth synthesis were classified as diagnostic accuracy studies . Only one study evaluated an AI model that was commercially available in the UK. A total of 14 studies compared AI models to human readers or to other diagnostic methods used in practice, 13 studies compared the impact of AI on human interpretation of radiologic images when diagnosing cancer, four studies compared multiple AI models, and one study compared an inexperienced AI-assisted reader with an experienced reader without AI. Five studies reported on the impact of AI on diagnostic timelines (time to diagnosis, assessment time, evaluation times, and reading time). Four studies also reported on the impact of AI on inter/intra-reader variability, reliability, and agreement. One study reported on clinicians’ acceptance and receptiveness of the use of AI for cancer diagnosis. Key findings and certainty of the evidence Most studies reported results in favour of the AI models, however, these improvements were not always statistically significant. The studies also varied considerably in terms of AI models studied, type of cancer, images used, and comparison made; and were limited in terms of their methodology (unclear level of certainty). Whe

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