The <scp><i>BJUI</i></scp> Editorial Team's view on artificial intelligence and machine learning

Thalmann, George N.; Klatte, Tobias; Papa, Nathan; Carlsson, Sigrid

doi:10.1111/bju.16024

BJU International

2023

DOI: 10.1111/bju.16024

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The BJUI Editorial Team's view on artificial intelligence and machine learning

George N. Thalmann

Tobias Klatte

Nathan Papa

et al.

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Cited by 3 publications

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“…As Professor Lionel Tarassenko of Oxford wrote back in 1998, "Neural networks are not, nor will they ever be, a 'black-box' solution into which data can be poured in the expectation that an answer will emerge." 5 Specifically in urology, consideration of the use of ML has led to recent guidelines from the editors of one journal 6 and guidance for reporting new applications under the STREAM-URO Framework. 7 The need for such guidance emphasizes that good practice and specialist guidance are needed to ensure the appropriate use of this clearly powerful tool.…”

mentioning

confidence: 99%

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI‐RS 2023

Gammie,

Arlandis,

Couri

et al. 2023

Neurourology and Urodynamics

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IntroductionA “Think Tank” at the International Consultation on Incontinence‐Research Society meeting held in Bristol, United Kingdom in June 2023 considered the progress and promise of machine learning (ML) applied to urodynamic data.MethodsExamples of the use of ML applied to data from uroflowmetry, pressure flow studies and imaging were presented. The advantages and limitations of ML were considered. Recommendations made during the subsequent debate for research studies were recorded.ResultsML analysis holds great promise for the kind of data generated in urodynamic studies. To date, ML techniques have not yet achieved sufficient accuracy for routine diagnostic application. Potential approaches that can improve the use of ML were agreed and research questions were proposed.ConclusionsML is well suited to the analysis of urodynamic data, but results to date have not achieved clinical utility. It is considered likely that further research can improve the analysis of the large, multifactorial data sets generated by urodynamic clinics, and improve to some extent data pattern recognition that is currently subject to observer error and artefactual noise.

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mentioning

confidence: 99%

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI‐RS 2023

Gammie,

Arlandis,

Couri

et al. 2023

Neurourology and Urodynamics

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ChatGPT and generative AI in urology and surgery—A narrative review

Qin,

Chislett,

Ischia

et al. 2024

BJUI Compass

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IntroductionChatGPT (generative pre‐trained transformer [GPT]), developed by OpenAI, is a type of generative artificial intelligence (AI) that has been widely utilised since its public release. It orchestrates an advanced conversational intelligence, producing sophisticated responses to questions. ChatGPT has been successfully demonstrated across several applications in healthcare, including patient management, academic research and clinical trials. We aim to evaluate the different ways ChatGPT has been utilised in urology and more broadly in surgery.MethodsWe conducted a literature search of the PubMed and Embase electronic databases for the purpose of writing a narrative review and identified relevant articles on ChatGPT in surgery from the years 2000 to 2023. A PRISMA flow chart was created to highlight the article selection process. The search terms ‘ChatGPT’ and ‘surgery’ were intentionally kept broad given the nascency of the field. Studies unrelated to these terms were excluded. Duplicates were removed.ResultsMultiple papers have been published about novel uses of ChatGPT in surgery, ranging from assisting in administrative tasks including answering frequently asked questions, surgical consent, writing operation reports, discharge summaries, grants, journal article drafts, reviewing journal articles and medical education. AI and machine learning has also been extensively researched in surgery with respect to patient diagnosis and predicting outcomes. There are also several limitations with the software including artificial hallucination, bias, out‐of‐date information and patient confidentiality.ConclusionThe potential of ChatGPT and related generative AI models are vast, heralding the beginning of a new era where AI may eventually become integrated seamlessly into surgical practice. Concerns with this new technology must not be disregarded in the urge to hasten progression, and potential risks impacting patients' interests must be considered. Appropriate regulation and governance of this technology will be key to optimising the benefits and addressing the intricate challenges of healthcare delivery and equity.

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Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI

Kwong,

Wu,

Malik

et al. 2024

npj Digit. Med.

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Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32−93) and median cohort size of 125 (IQR 93−309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.

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The BJUI Editorial Team's view on artificial intelligence and machine learning

Cited by 3 publications

References 9 publications

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI‐RS 2023

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI‐RS 2023

ChatGPT and generative AI in urology and surgery—A narrative review

Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI

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