Abstract A060: OncoMindPro: An AI-augmented assistant to oncologists
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".