AIMC Topic: Medical Oncology

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Artificial Intelligence-assisted Biomedical Literature Knowledge Synthesis to Support Decision-making in Precision Oncology.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The delivery of effective targeted therapies requires comprehensive analyses of the molecular profiling of tumors and matching with clinical phenotypes in the context of existing knowledge described in biomedical literature, registries, and knowledge...

Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review.

Critical reviews in oncogenesis
The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be ...

Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges.

Critical reviews in oncogenesis
Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable su...

Integrating Artificial Intelligence and Machine Learning Into Cancer Clinical Trials.

Seminars in radiation oncology
The practice of oncology requires analyzing and synthesizing abundant data. From the patient's workup to determine eligibility to the therapies received to the post-treatment surveillance, practitioners must constantly juggle, evaluate, and weigh dec...

[To promote the clinical application of PET/MRI in oncology].

Zhonghua yi xue za zhi
PET/MRI integrates anatomical, functional and metabolic information, and is increasingly used in the field of clinical oncology, including early diagnosis of disease, local staging, detection of systemic metastases, evaluation of treatment efficacy a...

[Analysis of the Tumor Immune Microenvironment of Colorectal Cancer by Deep Learning-Based Imaging Cytometry].

Gan to kagaku ryoho. Cancer & chemotherapy
The tumor immune microenvironment(TIME)of colorectal cancer contains indicators of unique therapeutic outcomes for each cancer patient. Deep learning-based imaging cytometry(DL-IC), which can obtain objective and reproducible cell- related informatio...

Opportunities and Challenges of Synthetic Data Generation in Oncology.

JCO clinical cancer informatics
Widespread interest in artificial intelligence (AI) in health care has focused mainly on deductive systems that analyze available real-world data to discover patterns not otherwise visible. Generative adversarial network, a new type of inductive AI, ...