Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Artificial. Delphi Consensus
Trial Snapshot
- Phase
- Not Applicable
- Enrollment
- 150
- Locations
- 1
- Primary Endpoint
- Degree of consensus
Study Overview
Brief Summary
The investigators aim to develop the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Artificial Intelligence Extension (PRISMA-AI) guideline as a stand-alone extension of the PRISMA statement, modified to reflect the particular requirements for the reporting of AI and its related topics (namely machine learning, deep learning, neuronal networking) in systematic reviews.
Detailed Description
With advances in artificial intelligence (AI) over the last two decades, enthusiasm and adoption of this technology in medicine have steadily increased. Yet despite the greater adoption of AI in medicine, the way such methodologies and results are reported varies widely and the readability of clinical studies utilizing AI can be challenging to the general clinician.
Systematic reviews of AI applications are an important area for which specific guidance is needed. An ongoing systematic review led by our team has shown that the number of systematic reviews on AI applications (with or without meta-analysis) is increasing dramatically over the time, yet the quality of reporting is still poor and heterogeneous, leading to inconsistencies in the reporting of informational details among individual studies. Consequently, the lack of these informational details may front problems for primary research and synthesis and potentially limits their usefulness for stakeholders interested in implementing AI or using the information in systematic reviews.
The criteria will derive from the consensus among multi-specialty experts (in each medical specialty) who have already published about AI applications in leading medical journals and the lead authors of PRISMA, STARD-AI, CONSORT-AI, SPIRIT-AI, TRIPOD-AI, PROBAST-AI, CLAIM-AI and DECIDE-AI to ensure that the criteria have global applicability in all the disciplines and for each type of study which involves the AI.
The proposed PRISMA-AI extension criteria focus on standardizing the reporting of methods and results for clinical studies utilizing AI. These criteria will reflect the most relevant technical details a data scientist requires for future reproducibility, yet they focus on the ability for the clinician reader to critically follow and ascertain the relevant outcomes of such studies.
The resultant PRISMA-AI extension will
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •experts in the use AI technology in medicine
- •experts in PRISMA
- •leading authors of STARD-AI, CONSORT-AI, SPIRIT-AI, TRIPOD-AI, PROBAST-AI, CLAIM-AI and DECIDE-AI
Exclusion Criteria
- •Panelists who were not able to commit to all rounds of the modified Delphi process will be excluded
Outcomes
Primary Outcomes
Degree of consensus
Time Frame: 3 months
The level of agreement for all statements achieving consensus from the expert panel; consensus is predefined as ≥ 80% of the panel rating a given statement
Secondary Outcomes
No secondary outcomes reported
Investigators
Giovanni Cacciamani
Assistant Professor of Research Urology and Radiology
University of Southern California