Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

Nittas, Vasileios; Daniore, Paola; Gille, Felix; Amann, Julia; Hubbs, Shannon; Puhan, Milo; Vayena, Effy; Blasimme, Alessandro

doi:10.1371/journal.pdig.0000189

Cited by 7 publications

(5 citation statements)

References 114 publications

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“…We have chosen 2016 as the starting year of the review because while it was a year that showed significant advancement in AI, many were concerned about its ethical implications (Mills, 2016 ; Stone et al, 2016 ; Greene et al, 2019 ). Since AI is fast evolving, the literature from the recent years was used to obtain the emergent and most recent results (Nittas et al, 2023 ; Sukums et al, 2023 ). Figure 1 presents our review flowchart following PRISMA's guidelines (Moher et al, 2009 ).…”

Section: Methodsmentioning

confidence: 99%

Specific challenges posed by artificial intelligence in research ethics

Bouhouita-Guermech,

Gogognon,

Bélisle-Pipon

2023

Front. Artif. Intell.

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BackgroundThe twenty first century is often defined as the era of Artificial Intelligence (AI), which raises many questions regarding its impact on society. It is already significantly changing many practices in different fields. Research ethics (RE) is no exception. Many challenges, including responsibility, privacy, and transparency, are encountered. Research ethics boards (REB) have been established to ensure that ethical practices are adequately followed during research projects. This scoping review aims to bring out the challenges of AI in research ethics and to investigate if REBs are equipped to evaluate them.MethodsThree electronic databases were selected to collect peer-reviewed articles that fit the inclusion criteria (English or French, published between 2016 and 2021, containing AI, RE, and REB). Two instigators independently reviewed each piece by screening with Covidence and then coding with NVivo.ResultsFrom having a total of 657 articles to review, we were left with a final sample of 28 relevant papers for our scoping review. The selected literature described AI in research ethics (i.e., views on current guidelines, key ethical concept and approaches, key issues of the current state of AI-specific RE guidelines) and REBs regarding AI (i.e., their roles, scope and approaches, key practices and processes, limitations and challenges, stakeholder perceptions). However, the literature often described REBs ethical assessment practices of projects in AI research as lacking knowledge and tools.ConclusionEthical reflections are taking a step forward while normative guidelines adaptation to AI's reality is still dawdling. This impacts REBs and most stakeholders involved with AI. Indeed, REBs are not equipped enough to adequately evaluate AI research ethics and require standard guidelines to help them do so.

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Section: Methodsmentioning

confidence: 99%

Specific challenges posed by artificial intelligence in research ethics

Bouhouita-Guermech,

Gogognon,

Bélisle-Pipon

2023

Front. Artif. Intell.

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“…While AI-specific challenges, such as limited interpretability and bias risks have been widely discussed, especially in relation to its clinical use 1 , a lesser degree of attention has been paid to issues related to the evidence base supporting the implementation of AI-based applications in the clinical context. Recently, the importance of rigorous testing and real-world validation, including well-designed Randomized Controlled Trials (RCTs), has been emphasized before AI can be integrated into medical practice.…”

Section: Introductionmentioning

confidence: 99%

Ongoing and planned Randomized Controlled Trials of AI in medicine: An analysis of Clinicaltrials.gov registration data

Andreoletti,

Senkalfa,

Blasimme

2024

Preprint

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The integration of Artificial Intelligence (AI) technologies into clinical practice holds significant promise for revolutionizing healthcare. However, the realization of this potential requires rigorous evaluation and validation of AI applications to ensure their safety, efficacy, and clinical significance. Despite increasing awareness of the need for robust testing, the majority of AI-related Randomized Controlled Trials (RCTs) so far have exhibited notable limitations, impeding the generalizability and proper integration of their findings into clinical settings. To understand whether the field is progressing towards more robust testing, we conducted an analysis of the registration data of ongoing and planned RCTs of AI in medicine available in theClinicaltrials.govdatabase. Our analysis highlights several key trends and challenges. Effectively addressing these challenges is essential for advancing the field of medical AI and ensuring its successful integration into clinical practice.

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“…These approaches include various methodologies and techniques that are specifically designed to address the challenges inherent in breast ultrasound image segmentation [13][14][15]. Deep learning (DL) models, predominantly convolutional neural networks (CNNs), have proven remarkable success in accurately segmenting anatomical structures and identifying pathological regions in various medical imaging modalities, including X-ray, MRI, CT, and ultrasound [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet

Khaledyan

Marini

O’Connell

et al. 2023

Preprint

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Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it a popular choice among researchers in the medical image segmentation field. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the dice coefficient, specificity, sensitivity, and F1 score obtained values of 0.9283, 0.9936, 0.9426, and 0.9412, respectively. In addition, McNemar's statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperforms the earlier designed models and points towards improved breast lesion segmentation algorithms.

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Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

Cited by 7 publications

References 114 publications

Specific challenges posed by artificial intelligence in research ethics

Specific challenges posed by artificial intelligence in research ethics

Ongoing and planned Randomized Controlled Trials of AI in medicine: An analysis of Clinicaltrials.gov registration data

Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet

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