ArticleMayo Clinic proceedings. Digital health2023
International Market Access Strategies for Artificial Intelligence-Based Medical Devices: Can We Standardize the Process to Faster Patient Access?
Article in Mayo Clinic proceedings. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Large Language Models for Postoperative Decision Support: Comparative Analysis.Journal of medical Internet research · 2026Article
- Integrating Fine-Tuning and Retrieval-Augmented Generation for Healthcare AI Systems: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Access and Reimbursement for Artificial Intelligence in Radiology: A Macau Perspective.Korean journal of radiology · 2026Article
- Beyond diagnostic accuracy: navigating the artificial intelligence data tsunami toward value-based implementation in gastroenterology.Frontiers in artificial intelligence · 2026Review
- Classification grid and evidence matrix for evaluating digital medical devices under the European union landscape.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The reimbursement of artificial intelligence (AI)-based medical devices (MDs) is a complex and challenging issue, depending on national market access pathways. We aim to analyze these pathways and propose recommendations for standardized market access processes. In the USA, companies sell AI-based MDs to each health insurance provider separately. In Europe, Germany is a pioneer in digital MD reimbursement, followed by France. However, it varies markedly without specific pathways in other European countries. In Asia and Australia, health insurance reimburses them. Despite important variations in assessment policies, reimbursement restrictions, and a lack of transparency in decision-making processes, collaboration among health technology assessment agencies at an international level can enhance evidence-based coverage decision-making. Regarding current barriers to market access standardization, understanding each country's needs and building high-quality evidence are mandatory. Solutions to promote standardized market access pathways are as follows: (1) accelerating approval; (2) standardizing health technology assessment; (3) generating health economics evidence; and (4) expanding insurance coverage. These perspectives aim to change paradigm from market access to patient access through a standardized assessment of AI-based MDs.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.