Recently, many AI tools have been developed for cancer research, including, but not limited to, diagnosis and treatment. Therefore, providing specific guidance may help the research community advance their interests.
To effectively use AI in cancer research, follow these key guidelines:
These steps ensure ethical, impactful, and clinically relevant AI integration into cancer research.
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Using AI in cancer research requires clear objectives, such as early detection, personalized treatments, or drug discovery, focusing on areas where AI can address existing gaps. High-quality, diverse datasets—ranging from medical images to genomic sequences and clinical records—must be curated, standardized, and pre-processed for accurate analysis. Advanced AI techniques, including machine learning for imaging tasks, natural language processing for extracting insights from clinical notes, and predictive modeling for biomarker identification, should be strategically applied. Interdisciplinary collaboration between oncologists, bioinformaticians, data scientists, and ethicists is essential to ensure robust research outcomes. Ethical considerations, including data privacy, bias mitigation, and patient-centric applications, must guide every stage to maximize AI's potential in advancing cancer research.
Big data of personal lifestyle, food, and environment.
Immune regulation and networks.
Signal transduction pathways of mTOR and AMPK.
Using Ai in cancer research cn significantly enhance diagnosis, treatment, and drug discovery, etc.
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