Scholarlink

Cancer remains a leading cause of mortality worldwide, requiring advancements in diagnosis, treatment, and patient care. Traditional oncology faces challenges, such as drug resistance, tumor heterogeneity, and variability in therapeutic response. Artificial intelligence (AI) revolutionizes cancer care by enhancing early detection, precision medicine, drug discovery, immunotherapy, and oncology nursing. However, AI integration in oncology remains in its early stages, necessitating further research and validation. This narrative review analyzes AI applications in mutation prediction, digital twin simulations, synthetic lethality modeling, AI-driven drug discovery, immunotherapy optimization, and AI-assisted oncology nursing. It highlights improvements in diagnosis, treatment, and patient care while addressing ethical and regulatory challenges. A literature search was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, arXiv, and bioRxiv, covering studies from 2015 to 2024. Boolean operators and thematic categorization extracted relevant studies on AI in cancer genomics, imaging, drug discovery, immuno-oncology, and nursing. Thematic analysis synthesized findings, identified trends, and evaluated challenges in AI-driven oncology. AI has significantly improved cancer detection, precision treatment, and drug development. AI-driven genomic analysis enhances mutation detection and therapy resistance forecasting. Deep learning algorithms improve early tumor detection, while digital twin technology personalizes therapy responses. AI accelerates drug discovery and nanomedicine, optimizing target identification and precision drug delivery. In immuno-oncology, AI aids immune checkpoint inhibitor selection, CAR-T cell therapy, and neoantigen prediction for personalized vaccines. AI-powered oncology nursing enhances remote monitoring, symptom tracking, and mental health assessment. However, ethical concerns, data biases, and regulatory challenges persist. AI is transforming oncology, improving diagnostics, accelerating drug discovery, and personalizing treatments. However, data biases, ethical concerns, and regulatory hurdles limit its clinical application. Future research should enhance AI transparency, diversify datasets, and establish standardized validation frameworks. Interdisciplinary collaboration is crucial to unlocking AI’s full potential in reducing global cancer mortality.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Oncology in the AI Era: Transforming Cancer Care Through Intelligent Diagnosis and Treatment

  • 章节

  • 发表日期：

    2025年

查看全文
  • Sonopant Joshi,
  • Husain Nadaf,
  • Mangesh Jabade,
  • Shital Pimpalekar

摘要

Cancer remains a leading cause of mortality worldwide, requiring advancements in diagnosis, treatment, and patient care. Traditional oncology faces challenges, such as drug resistance, tumor heterogeneity, and variability in therapeutic response. Artificial intelligence (AI) revolutionizes cancer care by enhancing early detection, precision medicine, drug discovery, immunotherapy, and oncology nursing. However, AI integration in oncology remains in its early stages, necessitating further research and validation. This narrative review analyzes AI applications in mutation prediction, digital twin simulations, synthetic lethality modeling, AI-driven drug discovery, immunotherapy optimization, and AI-assisted oncology nursing. It highlights improvements in diagnosis, treatment, and patient care while addressing ethical and regulatory challenges. A literature search was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, arXiv, and bioRxiv, covering studies from 2015 to 2024. Boolean operators and thematic categorization extracted relevant studies on AI in cancer genomics, imaging, drug discovery, immuno-oncology, and nursing. Thematic analysis synthesized findings, identified trends, and evaluated challenges in AI-driven oncology. AI has significantly improved cancer detection, precision treatment, and drug development. AI-driven genomic analysis enhances mutation detection and therapy resistance forecasting. Deep learning algorithms improve early tumor detection, while digital twin technology personalizes therapy responses. AI accelerates drug discovery and nanomedicine, optimizing target identification and precision drug delivery. In immuno-oncology, AI aids immune checkpoint inhibitor selection, CAR-T cell therapy, and neoantigen prediction for personalized vaccines. AI-powered oncology nursing enhances remote monitoring, symptom tracking, and mental health assessment. However, ethical concerns, data biases, and regulatory challenges persist. AI is transforming oncology, improving diagnostics, accelerating drug discovery, and personalizing treatments. However, data biases, ethical concerns, and regulatory hurdles limit its clinical application. Future research should enhance AI transparency, diversify datasets, and establish standardized validation frameworks. Interdisciplinary collaboration is crucial to unlocking AI’s full potential in reducing global cancer mortality.