Abstract A060: OncoMindPro: An AI-augmented assistant to oncologists
Notice bibliographique
Résumé
Abstract Background: Medical oncologists are facing increasing challenges from accurate diagnosis throughout precise treatment. Oncologists usually need to review incredible amount of structured and unstructured data including patient history of present illness, pathological diagnosis, imaging reports, genomic test, and clinical laboratory results of a given patient for decision-making of accurate diagnosis and personalized treatment. The purpose of this study was to build an artificial intelligent system that augments the massive complex data and assists oncologists for decision-making of precise diagnosis and treatment options. Methods: This retrospective study involved 2036 patients with advanced cancer. Each case was evaluated using OncoMindPro along with 4 different large multimodal models (LMMs) (OpenAI, Grok3 API, BioMedLM, andDeepSeek R1) and oncologists. OncoMindPro was built on robust multimodal medical data fusion architecture and curated knowledgebase using LMMs. The augmented AI process generates patient medical records (PMR) with precisely summarized clinical and diagnostic indications. Qualitative analysis of the overall quality of AI-generated PMR along with 4 different LLMs and oncologists was conducted using the Kappa analysis. Furthermore, OncoMindPro and other 4 LLMs and oncologists were used to identify personalized treatment options. Five board-certified oncologists evaluated the overall quality of AI-generated PMRs using a 4-point scale and rated the likelihood of a treatment option coming from an LLM on a scale from 0 to 10 (0, extremely unlikely; 10, extremely likely) and decided whether the treatment option was clinically useful. Number of treatment options, precision, recall, F1 score of LLMs compared with expert oncologists and usefulness of recommendations. Results: For AI-generated PMR, there were no significant differences in qualitative scores between oncologists and OncoMindPro(p > 0.05). However, the qualitative scores of the other 4 LMMs were significantly lower than those of oncologists (p < 0.05). For 2036 cancer patients, a median(IQR) number of 4.0(4.0-4.0) compared with 4.2(3.8-5.1), 7.1(4.2-8.6), 8.7(6.3-9.8), 10.3(7.4-12.7), and 11.3(10.1-15.4) treatment options each was identified by the human expert and OncolMindPro and other 4 LLMs, respectively. When considering the expert as a criterion standard, 4 other LLMs-generated treatment options reached F1 scores of 0.06, 0.13, 0.18, and 0.21 across all patients combined. Treatment options from OncoMindPro allowed a precision of 0.36 and a recall of 0.38 for an F1 score of 0.37. Conclusions: We built OncoMindPro as a novel AI-driven smart healthcare by successful implementation of multimodal fusion and LMMs in precision oncology. The AI capabilities of OncoMindPro help accurately match optimal treatment options to a given patient, and provide prioritized treatment recommendations to oncologists. The overall quality of patient medical record and treatment options recommend by OncoMindPro were significantly surpassing the performance of other LMMs. Citation Format: Samuel D. Ding, Xinjia Ding, Shikai Wu, Yan Ding, Qin Huang. OncoMindPro: An AI-augmented assistant to oncologists [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A060.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
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 ».