Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review.

Objectives: The continuous integration of artificial intelligence (AI) into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations.

Materials And Methods: The Appraisal of Guidelines, Research, and Evaluation II tool was used to assess the quality of guidelines based on 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence.

CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.

Background: Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent.

DS-PACK: Tool assembly for the end-to-end support of controlled access human data sharing.

The EU General Data Protection Regulation (GDPR) requirements have prompted a shift from centralised controlled access genome-phenome archives to federated models for sharing sensitive human data. In a data-sharing federation, a central node facilitates data discovery; meanwhile, distributed nodes are responsible for handling data access requests, concluding agreements with data users and providing secure access to the data. Research institutions that want to become part of such federations often lack the resources to set up the required controlled access processes.

The Translational Data Catalog - discoverable biomedical datasets.

The discoverability of datasets resulting from the diverse range of translational and biomedical projects remains sporadic. It is especially difficult for datasets emerging from pre-competitive projects, often due to the legal constraints of data-sharing agreements, and the different priorities of the private and public sectors. The Translational Data Catalog is a single discovery point for the projects and datasets produced by a number of major research programmes funded by the European Commission.

A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance.

As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods.