AI‑Assisted Delphi for RNA‑Based Medicines: A Controlled Comparison of Human and Automated Revision (Preprint)
Notice bibliographique
Résumé
BACKGROUND: The Delphi method is widely used to derive expert consensus on complex clinical problems, yet it is slow and resource intensive. Recent advances in large language models and retrieval‑augmented generation (RAG) offer the possibility of accelerating consensus while maintaining methodological rigor. Large language models can retrieve and summarize evidence, but they frequently hallucinate and cannot reliably cite sources. At the same time, RNA‑based drugs and messenger RNA vaccines are rapidly moving from concept to clinic, generating a pressing need for timely, evidence‑based consensus on regulatory, manufacturing, and clinical issues. OBJECTIVE: We evaluated whether a modular, RAG‑enabled, multi‑agent artificial intelligence (AI) pipeline could replicate the post-round 1 behavior of a human reviewer in a Delphi study. The primary objective was to determine whether AI‑assisted statement revision could rescue a greater proportion of subthreshold statements and achieve a consensus comparable to that obtained through human revision by round 2. METHODS: A parallel, 2‑arm Delphi study was conducted on 28 statements about RNA medicines. In total, 50 international panelists (clinicians, researchers, and patient representatives) were randomized into human (arm A) and AI‑assisted (arm B) groups. After round 1, statements below the 75% agreement threshold were revised either manually by human reviewers or by an AI pipeline comprising the following software agents: (1) ReferenceDetector, to identify external citations; (2) Summarizer, to produce structured summaries of supporting PDFs; (3) a hybrid RAG module that combined dense and sparse retrieval with cross‑encoder reranking; and (4) Refiner, which generated revised statements, reasoning logs, and explicit citations. Two human reviewers with expertise in Delphi methodology and literature review who had contributed to statement development verified retrieved citations and approved or amended revisions. Agreement rates and vote distributions were compared across arms. RESULTS: Arm A reached consensus on 71.4% (20/28) of the statements in round 1, whereas arm B reached consensus on 46.4% (13/28). After revision, consensus increased to 92.9% (26/28) of the statements in arm A and 85.7% (24/28) in arm B. The AI arm exhibited a larger mean improvement (absolute difference between rounds 1 and 2=39.3 percentage points) because more statements were initially below the threshold. Nonetheless, the absolute difference between arms after round 2 was modest (7.2 percentage points). AI‑assisted revisions were particularly effective for statements far below the threshold, but both arms failed to rescue 2 to 3 statements owing to substantive disagreements. CONCLUSIONS: A modular, citation‑anchored AI pipeline can closely approximate human performance in Delphi consensus procedures while substantially reducing manual workload. When paired with human oversight, AI assistance accelerated revision and closed most of the performance gap by the second round. Adoption of AI‑assisted workflows could accelerate consensus development on emerging technologies such as RNA therapeutics provided that transparency, rigorous retrieval, and human review are maintained.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,040 | 0,107 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».