Regulatory Aspects of the Use of Artificial Intelligence Medical Software

Zanca, Federica; Brusasco, Caterina; Pesapane, Filippo; Kwade, Zuzanna; Beckers, R.; Avanzo, Michele

doi:10.1016/j.semradonc.2022.06.012

Cited by 28 publications

(16 citation statements)

References 26 publications

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“…Fully automatic segmentation based on DL networks is rapidly emerging, and many different algorithms have already been trained for image segmentation tasks of various organs. Such algorithms need ad hoc training and quality control, with manually contoured images as reference [ 9 , 69 ]. Generalizability of trained algorithms, however, is a major drawback, given that applying those algorithms on a different dataset often results in complete failure [ 9 , 68 ].…”

Section: What Radiomics Is and How It Work In Breast Imaging Workflowmentioning

confidence: 99%

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Pesapane

Marco

Rapino

et al. 2023

JCM

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Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical “how-to” guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

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Section: What Radiomics Is and How It Work In Breast Imaging Workflowmentioning

confidence: 99%

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Pesapane

Marco

Rapino

et al. 2023

JCM

Self Cite

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“…The use of interpretable rather than ML models was not discussed [60]. The unique challenges for ongoing governing and regulating of ML in healthcare were not reviewed [49,61]. Specific measures to mitigate the risks of implementation of AI were not discussed.…”

Section: Limitationsmentioning

confidence: 99%

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review

Monteith,

Glenn,

Geddes

et al. 2023

Pharmacopsychiatry

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This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications. Human understanding of how a clinical AI prediction tool performs in routine and exceptional situations is fundamental to successful implementation. Physicians must be involved in all aspects of the selection, implementation, and ongoing product monitoring of clinical AI prediction tools.

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“…Such individual data might be in use and benefit the company’s interests instead of the patients’ [ 5 ]. Digital twins’ technology has the potential to deliver significant societal benefits, but specific governance is demanded, including measures that ensure the transparency of data usage and derived benefits and data privacy [ 5 , 17 ]. Otherwise, instead of acting as a social equalizer by allowing for effective equalizing enhancement interventions, it could be a driver for inequality, given the fact that such technology could not be accessible to everyone and the fact that patterns identified across a population of digital twins could lead to segmentation and discrimination [ 5 , 16 ].…”

Section: Current Limits and Challenges Of Digital Twins Technologymentioning

confidence: 99%

Digital Twins in Radiology

Pesapane¹,

Rotili²,

Penco³

et al. 2022

JCM

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A digital twin is a virtual model developed to accurately reflect a physical thing or a system. In radiology, a digital twin of a radiological device enables developers to test its characteristics, make alterations to the design or materials, and test the success or failure of the modifications in a virtual environment. Innovative technologies, such as AI and -omics sciences, may build virtual models for patients that are continuously adjustable based on live-tracked health/lifestyle parameters. Accordingly, healthcare could use digital twins to improve personalized medicine. Furthermore, the accumulation of digital twin models from real-world deployments will enable large cohorts of digital patients that may be used for virtual clinical trials and population studies. Through their further refinement, development, and application into clinical practice, digital twins could be crucial in the era of personalized medicine, revolutionizing how diseases are detected and managed. Although significant challenges remain in the development of digital twins, a structural modification to the current operating models is occurring, and radiologists can guide the introduction of such technology into healthcare.

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Regulatory Aspects of the Use of Artificial Intelligence Medical Software

Cited by 28 publications

References 26 publications

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review

Digital Twins in Radiology

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