Artificial intelligence in medical device software and high-risk medical devices – a review of definitions, expert recommendations and regulatory initiatives

Fraser, Alan G.; Biasin, Elisabetta; Bijnens, Bart; Bruining, Nico; Caiani, Enrico G.; Cobbaert, Koen; Davies, Rhodri H.; Gilbert, Stephen; Hovestadt, Ludger; Kamenjašević, Erik; Kwade, Zuzanna; McGauran, Gearóid; O’Connor, G. A.; Vasey, Baptiste; Rademakers, Frank

doi:10.1080/17434440.2023.2184685

Cited by 17 publications

(8 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Distinct steps of development (training and testing), validation and clinical evaluation of machine learning-based algorithms for diagnosis or prediction purposes have been established ( 39 – 42 ). In the case of machine learning-derived tools either as standalone medical device software or embedded within an intracoronary imaging modality, there are additional challenges to be considered ( 43 ). A few studies have already reported effective applications of automatic ICI evaluation in a clinical context.…”

Section: Discussionmentioning

confidence: 99%

Automatic assessment of atherosclerotic plaque features by intracoronary imaging: a scoping review

Biccirè,

Mannhart,

Kakizaki

et al. 2024

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundThe diagnostic performance and clinical validity of automatic intracoronary imaging (ICI) tools for atherosclerotic plaque assessment have not been systematically investigated so far.MethodsWe performed a scoping review including studies on automatic tools for automatic plaque components assessment by means of optical coherence tomography (OCT) or intravascular imaging (IVUS). We summarized study characteristics and reported the specifics and diagnostic performance of developed tools.ResultsOverall, 42 OCT and 26 IVUS studies fulfilling the eligibility criteria were found, with the majority published in the last 5 years (86% of the OCT and 73% of the IVUS studies). A convolutional neural network deep-learning method was applied in 71% of OCT- and 34% of IVUS-studies. Calcium was the most frequent plaque feature analyzed (26/42 of OCT and 12/26 of IVUS studies), and both modalities showed high discriminatory performance in testing sets [range of area under the curve (AUC): 0.91–0.99 for OCT and 0.89–0.98 for IVUS]. Lipid component was investigated only in OCT studies (n = 26, AUC: 0.82–0.86). Fibrous cap thickness or thin-cap fibroatheroma were mainly investigated in OCT studies (n = 8, AUC: 0.82–0.94). Plaque burden was mainly assessed in IVUS studies (n = 15, testing set AUC reported in one study: 0.70).ConclusionA limited number of automatic machine learning-derived tools for ICI analysis is currently available. The majority have been developed for calcium detection for either OCT or IVUS images. The reporting of the development and validation process of automated intracoronary imaging analyses is heterogeneous and lacks critical information.Systematic Review RegistrationOpen Science Framework (OSF), https://osf.io/nps2b/.

show abstract

Section: Discussionmentioning

confidence: 99%

Automatic assessment of atherosclerotic plaque features by intracoronary imaging: a scoping review

Biccirè,

Mannhart,

Kakizaki

et al. 2024

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…Precise definitions and classifications for medical device software and AI systems differ between jurisdictions but in general AI or ML-based tools or algorithms when used for diagnostic or therapeutic purposes, including applications for GI endoscopy, will meet the definition of a medical device and should be appropriately developed and evaluated before they are approved for clinical use in accordance with the relevant regional regulation[ 47 ]. Similarly, clinical research including pilot studies to generate the clinical data required to validate and appraise novel and uncertified AI tools in endoscopy should be performed in accordance with applicable regulatory and ethical requirements.…”

Section: Regulation Supervision and Accountabilitymentioning

confidence: 99%

Human-artificial intelligence interaction in gastrointestinal endoscopy

Campion,

O'Connor,

Lahiff

2024

World J Gastrointest Endosc

View full text Add to dashboard Cite

The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we use are complex and are influenced by design, behavioural and psychological elements. Due to the substantial differences between AI and prior technologies, important differences may be expected in how we interact with advice from AI technologies. Human–AI interaction (HAII) may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability. Human factors influencing HAII may include automation bias, alarm fatigue, algorithm aversion, learning effect and deskilling. Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.

show abstract

“…However, the regulatory process can be lengthy, complex, and costly, and lack of clarity regarding the regulatory framework for AI-based medical devices can hinder their adoption. This limits the ability to develop and deploy AI algorithms rapidly [ 75 , 76 ].…”

Section: Expert Opinionmentioning

confidence: 99%

Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion

Pinton

2023

Annals of Medicine

View full text Add to dashboard Cite

Introduction Artificial intelligence (AI) is expected to impact all facets of inflammatory bowel disease (IBD) management, including disease assessment, treatment decisions, discovery and development of new biomarkers and therapeutics, as well as clinician–patient communication. Areas covered This perspective paper provides an overview of the application of AI in the clinical management of IBD through a review of the currently available AI models that could be potential tools for prognosis, shared decision-making, and precision medicine. This overview covers models that measure treatment response based on statistical or machine-learning methods, or a combination of the two. We briefly discuss a computational model that allows integration of immune/biological system knowledge with mathematical modeling and also involves a ‘digital twin’, which allows measurement of temporal trends in mucosal inflammatory activity for predicting treatment response. A viewpoint on AI-enabled wearables and nearables and their use to improve IBD management is also included. Expert opinion Although challenges regarding data quality, privacy, and security; ethical concerns; technical limitations; and regulatory barriers remain to be fully addressed, a growing body of evidence suggests a tremendous potential for integration of AI into daily clinical practice to enable precision medicine and shared decision-making.

show abstract

Artificial intelligence in medical device software and high-risk medical devices – a review of definitions, expert recommendations and regulatory initiatives

Cited by 17 publications

References 74 publications

Automatic assessment of atherosclerotic plaque features by intracoronary imaging: a scoping review

Automatic assessment of atherosclerotic plaque features by intracoronary imaging: a scoping review

Human-artificial intelligence interaction in gastrointestinal endoscopy

Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion

Contact Info

Product

Resources

About