Regulatory responses to medical machine learning

Minssen, Timo; Gerke, Sara; Aboy, Mateo; Price, Nicholson; Cohen, Glenn

doi:10.1093/jlb/lsaa002

Cited by 57 publications

(59 citation statements)

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“…The MDR also specifies the details of the classification of software. For example, "software intended to monitor physiological processes is classified as class IIa" only in cases where it is not "intended for monitoring of vital physiological parameters, where the nature of variations of those parameters is such that it could result in immediate danger to the patient" (Rule 11 in Chapter III of Annex VIII) 39,40 . also published guidelines for manufacturers that interpret the DiGAV and provide supplementary details on the procedure for inclusion in the register (SGB V, § 139e(8)) 12 .…”

mentioning

confidence: 99%

Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries

Gerke

Stern

Minssen³

2020

npj Digit. Med.

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135

101

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Reimbursement is a key challenge for many new digital health solutions, whose importance and value have been highlighted and expanded by the current COVID-19 pandemic. Germany’s new Digital Healthcare Act (Digitale–Versorgung–Gesetz or DVG) entitles all individuals covered by statutory health insurance to reimbursement for certain digital health applications (i.e., insurers will pay for their use). Since Germany, like the United States (US), is a multi-payer health care system, the new Act provides a particularly interesting case study for US policymakers. We first provide an overview of the new German DVG and outline the landscape for reimbursement of digital health solutions in the US, including recent changes to policies governing telehealth during the COVID-19 pandemic. We then discuss challenges and unanswered questions raised by the DVG, ranging from the limited scope of the Act to privacy issues. Lastly, we highlight early lessons and opportunities for other countries.

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mentioning

confidence: 99%

Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries

Gerke

Stern

Minssen³

2020

npj Digit. Med.

Self Cite

135

101

View full text Add to dashboard Cite

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“…Furthermore, DL models can handle high data complexity, yet are limited in demonstrating the reasoning behind their prediction. Particularly for health care utilization, it is crucial to build trust in these models and being able to understand their prediction, not at least for regulatory purposes 71 . Although considerable efforts have been made regarding explainable DL, the problem is still not solved at large 72 …”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for the management of pancreatic diseases

Gorris

Hoogenboom

Wallace

et al. 2020

Digestive Endoscopy

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Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.

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“…“Catastrophic forgetting,” where an algorithm abruptly deteriorates in its performance on the original task when a new task is learned, is a major barrier to the implementation of such continuous learning 76 . Nevertheless, the US Food and Drug Administration last year published a discussion paper outlining how such continuously adapting algorithms might be regulated in future clinical use 77,78 . Figure 3 outlines how neural networks could be embedded in either screening or specialist fetal cardiology workflows, with the potential inputs and outputs of such models.…”

Section: The Futurementioning

confidence: 99%

Artificial intelligence, fetal echocardiography, and congenital heart disease

et al. 2021

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There has been a recent explosion in the use of artificial intelligence (AI), which is now part of our everyday lives. Uptake in medicine has been more limited, although in several fields there have been encouraging results showing excellent performance when AI is used to assist in a well-defined medical task. Most of this work has been performed using retrospective data, and there have been few clinical trials published using prospective data. This review focuses on the potential uses of AI in the field of fetal cardiology. Ultrasound of the fetal heart is highly specific and sensitive in experienced hands, but despite this there is significant room for improvement in the rates of prenatal diagnosis of congenital heart disease in most countries. AI may be one way of improving this. Other potential applications in fetal cardiology include the provision of more accurate prognoses for individuals, and automatic quantification of various metrics including cardiac function. However, there are also ethical and governance concerns. These will need to be overcome before AI can be widely accepted in mainstream use. It is likely that a familiarity of the uses, and pitfalls, of AI will soon be mandatory for many healthcare professionals working in fetal cardiology. Key PointsWhat's already known about this topic? � Artificial intelligence (AI) is rapidly becoming part of everyday life, and interest is growing in its use in improving medical outcomes.� AI may be a useful tool in fetal cardiology to improve rates of antenatal diagnosis of congenital heart disease, as well as other potential benefits.� Potential pitfalls exist however, and future clinicians will likely need to have a thorough understanding of the risks and benefits of AI. What does this study add?� This review article summarizes the fundamentals of AI, the potential uses of AI in fetal cardiology, and what the future may hold.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Regulatory responses to medical machine learning

Cited by 57 publications

References 0 publications

Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries

Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries

Artificial intelligence for the management of pancreatic diseases

Artificial intelligence, fetal echocardiography, and congenital heart disease

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