ArticleRMD open2026
fAI-BRO: a multimodal AI decision-support system to address diagnostic delay in fibromyalgia syndrome.
Article in RMD open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the diagnostic accuracy and patient acceptability of fAI-BRO (Fibromyalgia AI-Based Rheumatology Observer), a multimodal artificial intelligence (AI) system integrating video-based visual descriptor analysis and psycholinguistic transcript evaluation, for distinguishing fibromyalgia syndrome (FMS) from other rheumatic and musculoskeletal diseases (RMDs).
methodsIn this cross-sectional proof-of-concept study, 100 patients at the University of Bari (50 FMS per ACR (American College of Rheumatology) 2016 criteria, 50 RMD controls including inflammatory arthritis, osteoarthritis and other conditions) underwent standardised interviews in Italian. Audio was processed with OpenAI Whisper in translate mode. Magistral-Small (Mistral AI), deployed locally in zero-shot configuration without FMS-specific fine-tuning or in-context examples, analysed four video frames using OpenCV-derived image-level descriptors (30% weight) and psycholinguistic transcript features (70% weight). Classification thresholds were optimised post hoc via Youden's J index.
resultsAmong 100 patients, fAI-BRO achieved 91% accuracy (94% sensitivity (95% CI 83.5% to 98.7%), 88% specificity (95% CI 75.7% to 95.5%), area under the curve 0.96) for FMS detection on a 0-100 composite score. Youden's J identified the empirically optimal cut-off at 47, rounded down to 45 to favour screening sensitivity. All inflammatory arthritis cases (rheumatoid arthritis (RA), psoriatic arthritis (PsA), spondyloarthritis (SpA), systemic lupus erythematosus (SLE); n=36) and mechanical conditions (OA, impingement; n=8) were correctly classified as RMD. All six false positives for FMS occurred in dermatomyositis (n=3) and polymyalgia rheumatica (n=3)-a biologically plausible overlap involving muscle tissue rather than synovial pathology, presented here as a hypothesis-generating observation rather than incidental noise. Perturbation analysis confirmed widespread pain descriptors as the dominant classification drivers. Binary classification was robust to the English translation effect. Patient comfort was high (mean 4.5/5). Mean processing time was 324 s.
conclusionfAI-BRO demonstrates promising diagnostic accuracy for FMS screening as a human-in-the-loop decision-support tool, with perfect inflammatory arthritis classification and high patient acceptability. Post hoc optimised thresholds require external validation before clinical deployment.
Indexed as
Identifiers
What 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.