To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.
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http://dx.doi.org/10.1038/ng.1054 | DOI Listing |
Curr Res Transl Med
January 2025
Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
View Article and Find Full Text PDFHealthcare (Basel)
December 2024
Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea.
Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, Paris, France.
Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology.
View Article and Find Full Text PDFJ Biomed Semantics
December 2024
Medical BioSciences Department, Radboud University Medical Center, Nijmegen, The Netherlands.
Motivation: We are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and can be misused to de-anonymize and (re-)identify individuals.
View Article and Find Full Text PDFHealth Res Policy Syst
December 2024
Texas A&M Health, School of Medicine, Health Professions Education Building 8447 Riverside Pkwy, Bryan, TX, 77807, United States of America.
Most forms of clinical research examine a very minute cross section of the patient journey. Much of the knowledge and evidence base driving current genomic medicine practice entails blind spots arising from underrepresentation and lack of research participation in clinicogenomic databases. The flaws are perpetuated in AI models and clinical practice guidelines that reflect the lack of diversity in data being used.
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