Permanent record
RIR-2043 · Logged 13 July 2026
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.
In plain words
Computer programs that learn from data could help hospitals deal with infections, yet few have made it into everyday use. This study would set and test the standards for judging whether such systems can be trusted.
Primary research question
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
Academic fields and topic tags
Study frame: 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
Professional master’s / MBA
Governance and innovation management in digital health technologies.
Doctoral
Trustworthy AI and clinical implementation science.
- originality
- Moderate
- methodology
- Moderate
- Data access
- Moderate
- ethics
- Advanced
Quality and audit
88/100 quality
Safeguard-based audit was not recorded under the prior standard.
- Focus on regulatory requirements and stakeholder trust.
- Open-access scholarly source and DOI metadata verified
This older record can be checked against the current standard on request. It changes only if every academic gate and independent safeguard passes.
Sign-in required · uses one AI creditProvenance
Research Idea curation
- DOI and bibliographic metadata independently resolved
- Open-access status verified
- The research direction is transparently marked as AI-inferred
The public contributor code contains no name or account email.
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
This is an AI-inferred direction. The source authors did not state it as a recommendation.
Open source ↗