{rfName}
Re

Indexed in

License and use

Citations

1

Altmetrics

Grant support

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857223.

Analysis of institutional authors

López-Pérez, LauraCorresponding AuthorMerino, BeatrizAuthorRujas, MiguelAuthorCabrera, María FernandaAuthorArredondo, Maria TeresaAuthorFico, GiuseppeAuthor

Share

October 22, 2024
Publications
>
Proceedings Paper
No

Regulatory Frameworks and Validation Strategies for Advancing Artificial Intelligence in Healthcare

Publicated to:Comparative Study Of Machine Learning Methods For The Early Prediction Of Adherence To Medication. 113 260-265 - 2024-01-01 113(), DOI: 10.1007/978-3-031-61628-0_28

Authors: Lopez-Perez, Laura; Merino, Beatriz; Rujas, Miguel; Maccaro, Alessia; Guillen, Sergio; Pecchia, Leandro; Cabrera, Maria Fernanda; Arredondo, Maria Teresa; Fico, Giuseppe

Affiliations

ACTIVAGE Assoc, Madrid, Spain - Author
Univ Politecn Madrid, ETSIT, Life Supporting Technol Res Grp, Madrid, Spain - Author
Univ Warwick, Coventry, W Midlands, England - Author

Abstract

As AI technologies progress rapidly, there is an increasing need for tailored regulations that effectively address data provision, sharing, utilization, and knowledge generation. This paper delves into the essential regulations and emphasizes the crucial role of AI model validation in guaranteeing the dependability and effectiveness of AI-driven solutions. An innovative approach is introduced, detailing an organized four-phase methodology for external validation. The integration of these frameworks and the implementation of a DataLab are deemed imperative for fostering transparency, accountability, and enhancing patient outcomes within the swiftly evolving landscape of AI in healthcare. Through a comprehensive examination of key regulations and a structured validation approach, this research underscores the critical need for meticulous scrutiny and validation of AI models to ensure their reliability and efficacy in improving healthcare delivery. This study aims to lay the foundation for further exploration and advancement in this pivotal area, offering a roadmap for stakeholders, researchers, and policymakers to navigate the complexities of AI integration in healthcare while prioritizing patient safety and quality of care. The work has been done in the framework of the GATEKEEPER project, funded by the European Commission under the Horizon 2020 program.

Keywords

Address datumAi technologiesArtificial intelligenceDigital healthHealth careInnovative approachesKnowledge generationsLaws and legislationLiving labLiving labsModel validationRegulatory frameworksTechnology progressValidation strategies

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Comparative Study Of Machine Learning Methods For The Early Prediction Of Adherence To Medication, Q4 Agency Scopus (SJR), its regional focus and specialization in Bioengineering, give it significant recognition in a specific niche of scientific knowledge at an international level.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-15:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 6 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United Kingdom.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (LÓPEZ PÉREZ, LAURA) and Last Author (FICO, GIUSEPPE).

the author responsible for correspondence tasks has been LÓPEZ PÉREZ, LAURA.