Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis

Muehlematter, Urs J.; Daniore, Paola; Vokinger, Kerstin Noëlle

doi:10.1016/s2589-7500(20)30292-2

Cited by 332 publications

(248 citation statements)

References 16 publications

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“…While these scenarios delve into specific ways in which clinicians can inform the development and validation of ML tools in clinical care, the potential applications of AI in healthcare go from individualized recommendations with personalized medicine to informing policy in public health. A discussion of all the potential applications is beyond the scope of this article, but a comprehensive compilation of AI and ML-based medical devices approved by regulatory bodies in the United States and Europe provide a glimpse into the personalization of care (Muehlematter et al, 2021), while another article delves into the ways in which AI and ML can be used across populations to tailor policies to promote health, protect health, and improve the efficiency of services for communities within the greater population (Panch et al, 2019).…”

Section: Limitations and Additional Considerationsmentioning

confidence: 99%

The Clinician's Guide to the Machine Learning Galaxy

et al. 2021

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Machine learning (ML): a subfield of artificial intelligence, broadly refers to the ability of a computational platform to learn from data and make predictions or recommendations based on this data without being explicitly programmed. In general, there are two major categories of machine learning, supervised and unsupervised. Supervised learning is conducted with the concept of "truth" where the model tries to approximate the relationship between inputs and labeled outputs. For example, given images of cats and dogs, where each image has a correct answer, can you train a model that accurately identifies of cats versus dogs? Unsupervised learning is performed without data labels and the goal is for the computer to infer inherent structure or patterns in the data. For example, given a set of heart rate, accelerometer, and location data from a wearable fitness monitor, can the computer identify periods of rest versus exercise based on differences in the raw data?Neural networks (NN): a form of machine learning with a basic architecture consisting of nodes and connections existing in multiple layers, loosely analogous to neurons and synapses in the biological brain. This broad category is inclusive of many kinds of modern machine learning models which are used in tasks such as computer vision, voice recognition, bioinformatics, and among others. Deep learning:A broad family of neural network architectures that have multiple layers (aka deep).

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Section: Limitations and Additional Considerationsmentioning

confidence: 99%

The Clinician's Guide to the Machine Learning Galaxy

et al. 2021

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“…Unlike the way medical devices are regulated in the USA, with FDA being the central supervisory and certifying body, there is no such central authority in the EU. Certification is done through accredited third party, private entities called “Notified Bodies” [ 16 ]. There is no publicly available register of EU approved medical devices, and information sent to Notified Bodies and regulators is often confidential [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

“…Certification is done through accredited third party, private entities called “Notified Bodies” [ 16 ]. There is no publicly available register of EU approved medical devices, and information sent to Notified Bodies and regulators is often confidential [ 16 ]. There are plans to alleviate this, at least partly, with the introduction of Eudamed database in 2022 [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

“…There is no publicly available register of EU approved medical devices, and information sent to Notified Bodies and regulators is often confidential [ 16 ]. There are plans to alleviate this, at least partly, with the introduction of Eudamed database in 2022 [ 16 ]. Eudamed is supposed to contain registration of certificates of conformity, summary of safety, and clinical performance accessible by the public.…”

Section: Discussionmentioning

confidence: 99%

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Analysis and Comparison of Two Artificial Intelligence Diabetic Retinopathy Screening Algorithms in a Pilot Study: IDx-DR and Retinalyze

Grzybowski

Brona²

2021

JCM

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Background: The prevalence of diabetic retinopathy (DR) is expected to increase. This will put an increasing strain on health care resources. Recently, artificial intelligence-based, autonomous DR screening systems have been developed. A direct comparison between different systems is often difficult and only two such comparisons have been published so far. As different screening solutions are now available commercially, with more in the pipeline, choosing a system is not a simple matter. Based on the images gathered in a local DR screening program we performed a retrospective comparison of IDx-DR and Retinalyze. Methods: We chose a non-representative sample of all referable DR positive screening subjects (n = 60) and a random selection of DR negative patient images (n = 110). Only subjects with four good quality, 45-degree field of view images, a macula-centered and disc-centered image from both eyes were chosen for comparison. The images were captured by a Topcon NW-400 fundus camera, without mydriasis. The images were previously graded by a single ophthalmologist. For the purpose of this comparison, we assumed two screening strategies for Retinalyze—where either one or two out of the four images needed to be marked positive by the system for an overall positive result at the patient level. Results: Percentage agreement with a single reader in DR positive and DR negative cases respectively was: 93.3%, 95.5% for IDx-DR; 89.7% and 71.8% for Retinalyze strategy 1; 74.1% and 93.6% for Retinalyze under strategy 2. Conclusions: Both systems were able to analyse the vast majority of images. Both systems were easy to set up and use. There were several limitations to the current pilot study, concerning sample choice and the reference grading that need to be addressed before attempting a more robust future study.

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“…The USA and Europe have taken incremental steps in regulating artificial intelligence-based medical devices to advance the digital health market. A study conducted by Muehlmatter et al showed that around 222 devices in the USA and 240 devices in Europe were approved from 2015-2020 (Muehlematter et al, 2021). The effectiveness of these devices remains to be evaluated in the real-world setting.…”

Section: Medical Device Regulationmentioning

confidence: 99%

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Hassan

Dhali

Zaman

et al. 2021

Heliyon

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Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.

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Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis

Cited by 332 publications

References 16 publications

The Clinician's Guide to the Machine Learning Galaxy

The Clinician's Guide to the Machine Learning Galaxy

Analysis and Comparison of Two Artificial Intelligence Diabetic Retinopathy Screening Algorithms in a Pilot Study: IDx-DR and Retinalyze

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

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