Preventive, personalized, and precision medicine and biomarkers for breast cancer diagnosis: Artificial intelligence framework
Bibliographic record
Abstract
• AI models improve diagnostic accuracy in breast cancer imaging tasks (AUC >0.95). • Molecular profiling advances personalized treatment regimens. • DL algorithms show promise but require ethical oversight and prospective validation. Personalized medicine is needed since standard breast cancer diagnosis and treatment are not always exact or customized. To revolutionize breast cancer diagnosis, this paper examines the clinical utility of artificial intelligence (AI), specifically deep learning (DL). The accuracy with which AI can analyze vast medical databases—genetic data, clinical background, and images— enabling improved accuracy in diagnosis, staging, and treatment planning through integration of multimodal data. Recent studies have shown that DL algorithms have already attained 94–98 % diagnostic accuracies in recognizing breast cancer subtypes from histopathological images, against the overall accuracy of approximately 88 % of an average human pathologist. Additionally, CNNs have achieved AUC values of >0.95 in distinguishing between malignant and benign breast lesions based on mammography, substantiating the fact that AI enhances the diagnostic accuracy with a significant improvement. The study highlights a few case studies, such as fibroadenoma, invasive lobular carcinoma (ILC), lobular carcinoma in situ (LCIS), and sclerosing adenosis (SAD), to illustrate how DL algorithms outperform human experts in early detection and correct diagnosis. Incorporating DL into personalized medicine not only guarantees more specialized and effective treatment plans but also guarantees a future where every patient with breast cancer will be treated with personalized care. Other than referencing AI's revolutionizing impact on breast cancer screening, this research offers an unassailable case for adopting AI and signals a new epoch of accurate, patient-centered medicine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".