Legal Ramifications of Computer-Aided Detection in Mammography

Mezrich, Jonathan L.; Siegel, Eliot L.

doi:10.1016/j.jacr.2014.10.025

Cited by 14 publications

(5 citation statements)

References 7 publications

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“…If it turns out that the AI prompt was correct, the radiologist may have to justify his/her decision in front of a jury, and the AI finding would become the “built-in expert witness” against the radiologist. 100 …”

Section: Barriers To Adoption Of Aimentioning

confidence: 99%

Clinical applications of artificial intelligence in radiology

Mello‐Thoms

Mello

2023

The British Journal of Radiology

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The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI’s adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists’ liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.

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Section: Barriers To Adoption Of Aimentioning

confidence: 99%

Clinical applications of artificial intelligence in radiology

Mello‐Thoms

Mello

2023

The British Journal of Radiology

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“…this omission in front of a jury, and the CADe findings would become the built-in "expert" witness against the radiologist [12].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy

Mezrich

2022

American Journal of Roentgenology

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The publication of this Accepted Manuscript is provided to give early visibility to the contents of the article, which will undergo additional copyediting, typesetting, and review before it is published in its final form. During the production process, errors may be discovered that could affect the content of the Accepted Manuscript. All legal disclaimers that apply to the journal pertain. The reader is cautioned to consult the definitive version of record before relying on the contents of this document.

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“…There have been many papers describing a performance level in lesion detection similar to that of experienced radiologists [ 49 , 50 , 51 ]. CAD was approved by the Food and Drug Administration (FDA) and has been used for mammography in radiology practices [ 52 ]; however, improvement of the diagnostic ability has not been satisfactory, and the majority of radiologists have rarely changed their reports as a result of findings generated by CAD [ 53 , 54 , 55 ]. Machine learning has been reported to be unlikely to replace radiologists but will provide quantitative tools to increase the value of imaging as a biomarker including therapeutic response evaluation [ 56 ].…”

Section: Machine Learning For Imaging Cancer Heterogeneity and Intmentioning

confidence: 99%

Positron Emission Tomography for Response Evaluation in Microenvironment-Targeted Anti-Cancer Therapy

2020

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Therapeutic response is evaluated using the diameter of tumors and quantitative parameters of 2-[18F] fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET). Tumor response to molecular-targeted drugs and immune checkpoint inhibitors is different from conventional chemotherapy in terms of temporal metabolic alteration and morphological change after the therapy. Cancer stem cells, immunologically competent cells, and metabolism of cancer are considered targets of novel therapy. Accumulation of FDG reflects the glucose metabolism of cancer cells as well as immune cells in the tumor microenvironment, which differs among patients according to the individual immune function; however, FDG-PET could evaluate the viability of the tumor as a whole. On the other hand, specific imaging and cell tracking of cancer cell or immunological cell subsets does not elucidate tumor response in a complexed interaction in the tumor microenvironment. Considering tumor heterogeneity and individual variation in therapeutic response, a radiomics approach with quantitative features of multimodal images and deep learning algorithm with reference to pathologic and genetic data has the potential to improve response assessment for emerging cancer therapy.

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Legal Ramifications of Computer-Aided Detection in Mammography

Cited by 14 publications

References 7 publications

Clinical applications of artificial intelligence in radiology

Clinical applications of artificial intelligence in radiology

Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy

Positron Emission Tomography for Response Evaluation in Microenvironment-Targeted Anti-Cancer Therapy

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