ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Impact of AI Decision Support on Clinical Experts' Radiographic Interpretation of Adamantinomatous Craniopharyngioma.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Review
- A Visual Analytics Framework for Assessing Interactive AI for Clinical Decision Support.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research explores the integration of Artificial Intelligence (AI) into clinical decision-making in pediatric brain tumor care, specifically Adamantinomatous Craniopharyngioma (ACP). We present a user-centered design approach to introducing AI tools into clinical workflows to support decision-making in managing Central Nervous System tumors. We conducted a controlled experiment with six clinical experts to explore the hypothesis that AI integrated into clinical contexts can improve the radiographic interpretation of ACP. We found that AI assistance reduced task difficulty and enhanced clinical efficiency; we also discovered variations in user behavior during the annotation process. We identified multiple challenges, including the interpretive complexity of radiographic images and increased disagreements among clinicians when AI was employed. Our study underscores the importance of a nuanced understanding of clinician experiences for successful AI integration into a high-stakes clinical workflow.
Indexed as
Identifiers
40417524PMC12099361What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.