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Development of a Synthetic Oncology Pathology Dataset for Large Language Model Evaluation in Medical Text Classification.

Studies in health technology and informatics
BACKGROUND: Large Language Models (LLMs) offer promising applications in oncology pathology report classification, improving efficiency, accuracy, and automation. However, the use of real patient data is restricted due to legal and ethical concerns, ...

Enhancing Malignancy Detection and Tumor Classification in Pathology Reports: A Comparative Evaluation of Large Language Models.

Studies in health technology and informatics
BACKGROUND: Cancer registries require accurate and efficient documentation of malignancies, yet current manual methods are time-consuming and error-prone.

Integrative machine learning approach for identification of new molecular scaffold and prediction of inhibition responses in cancer cells using multi-omics data.

Briefings in functional genomics
MDM2 (Mouse Double Minute 2), a fundamental governor of the p53 tumor suppressor pathway, has garnered significant attention as a favorable target for cancer therapy. Recent years have witnessed the development and synthesis of potent MDM2 inhibitors...

Advancements in Circulating Tumor Cell Detection for Early Cancer Diagnosis: An Integration of Machine Learning Algorithms with Microfluidic Technologies.

Biosensors
Circulating tumor cells (CTCs) are vital indicators of metastasis and provide a non-invasive method for early cancer diagnosis, prognosis, and therapeutic monitoring. However, their low prevalence and heterogeneity in the bloodstream pose significant...

Validation of a Microfluidic Device Prototype for Cancer Detection and Identification: Circulating Tumor Cells Classification Based on Cell Trajectory Analysis Leveraging Cell-Based Modeling and Machine Learning.

International journal for numerical methods in biomedical engineering
Microfluidic devices (MDs) present a novel method for detecting circulating tumor cells (CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitat...

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.

Molecular cancer
Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern ...

Interoperability Framework of the European Health Data Space for the Secondary Use of Data: Interactive European Interoperability Framework-Based Standards Compliance Toolkit for AI-Driven Projects.

Journal of medical Internet research
The successful implementation of the European Health Data Space (EHDS) for the secondary use of data (known as EHDS2) hinges on overcoming significant challenges, including the proper implementation of interoperability standards, harmonization of div...

Artificial Intelligence in Cancer Care: Addressing Challenges and Health Equity.

Oncology (Williston Park, N.Y.)
Overdiagnosis in cancer care remains a significant concern, often resulting in unnecessary physical, emotional, and financial burdens on patients. Artificial intelligence (AI) has the potential to address this challenge by enabling more accurate, per...

Clinical Trial Notifications Triggered by Artificial Intelligence-Detected Cancer Progression: A Randomized Trial.

JAMA network open
IMPORTANCE: Historically, fewer than 10% of adults with cancer have enrolled in clinical trials. Computational tools have been developed to match patients to trials, but these tools are relevant only when patients need new treatment.