Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions DOI Creative Commons

Ellen N. Huhulea,

Lillian Huang,

Shirley Eng

и другие.

Biomedicines, Год журнала: 2025, Номер 13(4), С. 951 - 951

Опубликована: Апрель 13, 2025

Cancer remains one of the leading causes mortality worldwide, driving need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool oncology, with potential to revolutionize cancer diagnosis, treatment, management. This paper reviews recent advancements AI applications within research, focusing on early detection through computer-aided personalized treatment strategies, drug discovery. We survey AI-enhanced diagnostic explore techniques such deep learning, well integration nanomedicine immunotherapy care. Comparative analyses AI-based models versus traditional methods are presented, highlighting AI’s superior potential. Additionally, we discuss importance integrating social determinants health optimize Despite these advancements, challenges data quality, algorithmic biases, clinical validation remain, limiting widespread adoption. The review concludes discussion future directions emphasizing its reshape care by enhancing personalizing treatments targeted therapies, ultimately improving patient outcomes.

Язык: Английский

Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions DOI Creative Commons

Ellen N. Huhulea,

Lillian Huang,

Shirley Eng

и другие.

Biomedicines, Год журнала: 2025, Номер 13(4), С. 951 - 951

Опубликована: Апрель 13, 2025

Cancer remains one of the leading causes mortality worldwide, driving need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool oncology, with potential to revolutionize cancer diagnosis, treatment, management. This paper reviews recent advancements AI applications within research, focusing on early detection through computer-aided personalized treatment strategies, drug discovery. We survey AI-enhanced diagnostic explore techniques such deep learning, well integration nanomedicine immunotherapy care. Comparative analyses AI-based models versus traditional methods are presented, highlighting AI’s superior potential. Additionally, we discuss importance integrating social determinants health optimize Despite these advancements, challenges data quality, algorithmic biases, clinical validation remain, limiting widespread adoption. The review concludes discussion future directions emphasizing its reshape care by enhancing personalizing treatments targeted therapies, ultimately improving patient outcomes.

Язык: Английский

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