Permanent record · RIR–2043
Frameworks for Evaluating Trustworthiness in Machine Learning Systems for Clinical Infection Science
Machine learning offers potential for infection science but faces significant barriers to clinical implementation. Future research should develop and validate standardized frameworks for assessing the trustworthiness of these systems in real-world clinical environments.
What criteria are essential for establishing and validating the trustworthiness of machine learning systems in clinical infection science?
Knowledge gap
What remains worth asking
The source suggests that while ML is promising, the migration to clinical practice is limited, and it remains useful to test specific validation frameworks for regulatory and clinical acceptance.
Potential contribution
Why it may matter
Establishing trust is a prerequisite for the safe and effective integration of AI into clinical workflows.
Academic placement
OECD fields and topic tags
Scope: Clinical microbiology and hospital infection management systems. · Method signals: Systematic literature review, Expert Delphi study, Clinical workflow simulation
Possible study pathways
One question, different levels
Governance and innovation management in digital health technologies.
Trustworthy AI and clinical implementation science.
Qualification signal
88/100
- Focus on regulatory requirements and stakeholder trust.
- Open-access scholarly source and DOI metadata verified
Provenance
Research Idea Registry curation
- DOI and bibliographic metadata independently resolved
- Open-access status verified
- The research direction is transparently marked as AI-inferred
APA 7 source
McFadden, B. R., Reynolds, M., & Inglis, T. J. J. (2023). Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice. Frontiers in Digital Health, 5, Article 1260602. https://doi.org/10.3389/fdgth.2023.1260602
Paper abstract and discussion context; AI-inferred direction
Open source ↗