DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology

Vasey, Baptiste; Novak, Alex; Ather, S.; Ibrahim, Mudathir; McCulloch, Peter

doi:10.1016/j.crad.2022.09.131

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…Such stage‐specific reporting guidelines for the early and live clinical evaluation of AI have already emerged in the comparatively mature field of AI‐driven radiology (reviewed in ref. [ 92 ]).…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Quantitative interpretation of bone marrow biopsies in MPN—What's the point in a molecular age?

Ryou,

Lomas,

Theissen

et al. 2023

Br J Haematol

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SummaryThe diagnosis of myeloproliferative neoplasms (MPN) requires the integration of clinical, morphological, genetic and immunophenotypic findings. Recently, there has been a transformation in our understanding of the cellular and molecular mechanisms underlying disease initiation and progression in MPN. This has been accompanied by the widespread application of high‐resolution quantitative molecular techniques. By contrast, microscopic interpretation of bone marrow biopsies by haematologists/haematopathologists remains subjective and qualitative. However, advances in tissue image analysis and artificial intelligence (AI) promise to transform haematopathology. Pioneering studies in bone marrow image analysis offer to refine our understanding of the boundaries between reactive samples and MPN subtypes and better capture the morphological correlates of high‐risk disease. They also demonstrate potential to improve the evaluation of current and novel therapeutics for MPN and other blood cancers. With increased therapeutic targeting of diverse molecular, cellular and extra‐cellular components of the marrow, these approaches can address the unmet need for improved objective and quantitative measures of disease modification in the context of clinical trials. This review focuses on the state‐of‐the‐art in image analysis/AI of bone marrow tissue, with an emphasis on its potential to complement and inform future clinical studies and research in MPN.

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Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Quantitative interpretation of bone marrow biopsies in MPN—What's the point in a molecular age?

Ryou,

Lomas,

Theissen

et al. 2023

Br J Haematol

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“…Arguably, one critical element of successful AI implementations in prospective, in contrast to retrospective, settings is the human–AI interaction, as experts need to conduct live appraisal of recommendations by AI algorithms and make decisions on clinical practice. Guidelines like DECIDE‐AI [ 122 , 123 , 124 ] have been developed to provide standards for reporting on the evaluation of AI systems in live clinical settings of patient care.…”

Section: Challenges For the Implementation Of Digital Image ...mentioning

confidence: 99%

Application of digital pathology‐based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

Wilkins

2023

The Journal of Pathology

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In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep‐learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep‐learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention‐based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well‐established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology‐based advanced analytics to meaningfully impact clinical care, specific measures of ‘added benefit’ to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre‐clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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“…In comparison with physical medicine, use cases for AI in mental healthcare have been limited in scope given the differences between mental health treatment and those applied in physical medicine. For example, AI use for diagnostic purposes in radiology is established, initial models have already been deployed and consensus governance frameworks developed (eg Vasey et. al 2023).…”

Section: Introductionmentioning

confidence: 99%

"Clinical Reach into the Cognitive Space" (CRITiCS) - An outline conceptual framework for the safe use of generative artificial intelligence in mental health decision making.

Hider¹,

Wright²

2023

Preprint

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This article provides a conceptual framework - “CRITiCS” - for potential use in the design and deployment of generative AI systems to support clinical decision making in mental health services. Recent advances in generative AI, via massive parameter transformer networks (large language models such as GPT4) have rendered possible the use of cognitive agents that may support clinical productivity in mental healthcare, particularly in secondary and tertiary services where information load is proportional to clinical complexity and chronicity. Thus, the way we conceptualise the ‘cognitive space’ that, up to 2023, was occupied solely by human clinical reasoning, will inevitably change. However, there are transparency issues in that the cognitive emulation observed in LLM outputs, while of obvious potential benefit as an analogue of clinical reasoning,is not amenable to granular understanding in the same way that clinical decision making is subject to interrogation by human governance and assurance bodies. A consensus conceptual framework is urgently needed, to direct the path of the use of generative agents in mental health care, which we argue has cognitive space features that distinguish it from physical medicine, towards which most AI deployment proposals to date have been directed. It is hoped that this paper will attract discussion and interest in both the clinical and development communities.

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DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology

Cited by 12 publications

References 34 publications

Quantitative interpretation of bone marrow biopsies in MPN—What's the point in a molecular age?

Quantitative interpretation of bone marrow biopsies in MPN—What's the point in a molecular age?

Application of digital pathology‐based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

"Clinical Reach into the Cognitive Space" (CRITiCS) - An outline conceptual framework for the safe use of generative artificial intelligence in mental health decision making.

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