The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Benjamens, Stan; Dhunnoo, Pranavsingh; Meskó, Bertalan

doi:10.1038/s41746-020-00324-0

Cited by 697 publications

(471 citation statements)

References 22 publications

(16 reference statements)

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“…Dozens of companies have obtained US FDA and European CE approval for medical imaging AI 37 , and commercial markets have begun to form as sustainable business models are created. For instance, regions of high-throughput healthcare, such as India and Thailand, have welcomed the deployment of technologies such as diabetic retinopathy screening systems 38 .…”

Section: Medical Imagingmentioning

confidence: 99%

Deep learning-enabled medical computer vision

et al. 2021

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A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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Section: Medical Imagingmentioning

confidence: 99%

Deep learning-enabled medical computer vision

et al. 2021

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“…Artificial intelligence-based technologies are rapidly evolving into applicable medical solutions and are actually revolutionizing the field (11). However, only few studies have been put effort to fulfill rigorous regulation standards to be approved by regulatory institutions such as the FDA (12). Most of these approved technologies were developed for the fields of radiology, cardiology and internal medicine.…”

Section: Introductionmentioning

confidence: 99%

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Dacal¹,

Bermejo-Peláez²,

Lin

et al. 2021

Preprint

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Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). Kato-Katz technique is the diagnosis method recommended by WHO and although is generally more sensitive than other microscopic methods in high transmission settings, it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence methods based on digitized samples can support diagnostics efforts by support diagnostics efforts by performing an automatic and objective quantification of disease infection.In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of soil-transmitted helminths. Our solution includes (1) a digitalization system based on a mobile app that digitizes the microscope samples using a low-cost 3D-printed microscope adapter, (2) a telemedicine platform for remote analysis and labelling and (3) novel deep learning algorithms for automatic assessment and quantification of parasitological infection of STH.This work has been evaluated by comparing the STH quantification using both a manual remote analysis based on the digitized images and the AI-assisted quantification against the reference method based on conventional microscopy. The deep learning algorithm has been trained and tested on 41 slides of stool samples containing 949 eggs from 6 different subjects using a cross-validation strategy obtaining a mean precision of 98,44% and mean recall of 80,94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs.In conclusion, this work has presented a comprehensive pipeline using smartphone-based microscopy integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using artificial intelligence models.

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“…One day, the use of AI may help to diagnose and even predict the occurrence of sport related injuries [ 26 ]. However, as of today, AI-based chatbots still appear to lag behind other algorithms using machine learning since natural language processing is still a complicated issue and most FDA approved AI-based medical technologies do not use it [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Use of artificial intelligence in sports medicine: a report of 5 fictional cases

Rigamonti

Estel

Gehlen

et al. 2021

BMC Sports Sci Med Rehabil

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Background Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies. Methods Based on a literature review and clinical expert experience, five fictional “common” cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies. Results A mean of 25–36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor’s consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected. Conclusions All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.

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The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Cited by 697 publications

References 22 publications

Deep learning-enabled medical computer vision

Deep learning-enabled medical computer vision

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Use of artificial intelligence in sports medicine: a report of 5 fictional cases

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