Scholarlink

<p>This paper explores the revolutionary collaboration between artificial intelligence (AI) and nanotechnology in detecting and treating breast cancer. It highlights the synergistic potential of both fields to overcome significant limitations of modern approaches. Clinical applications and research demonstrate the diversity and depth of AI-based deep learning models in diagnostics, improving diagnostic accuracy and enabling precise, individualized therapy through advanced imaging and biomarker discovery. Through intelligent nanocarriers, nanotechnology contributes to these advancements by enabling targeted drug delivery, minimizing systemic toxicity, and providing theranostic capabilities for real-time monitoring. However, challenges remain, including data accessibility, model interpretability, scalability in nanocarrier manufacturing, and tumor diversity. Future improvements should focus on developing multifunctional nanoparticles, flexible AI algorithms, and scalable, cost-effective solutions to enhance accessibility and clinical integration. Hence, the study emphasizes the need for multidisciplinary collaboration to eliminate existing barriers and generate advancements to transform breast cancer therapies into more effective, safer, and individualized methods.</p> Graphical Abstract <p></p>

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

Artificial Intelligence-Assisted Design of Nanomedicines for Breast Cancer Diagnosis and Therapy: Advances, Challenges, and Future Directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月21日

查看全文
  • Maryam Shirzad,
  • Mina Shaban,
  • Vahideh Mohammadzadeh,
  • Abbas Rahdar,
  • Sonia Fathi-karkan,
  • Zakieh Sadat Hoseini,
  • Mehrdad Najafi,
  • Zelal Kharaba,
  • M. Ali Aboudzadeh

摘要

This paper explores the revolutionary collaboration between artificial intelligence (AI) and nanotechnology in detecting and treating breast cancer. It highlights the synergistic potential of both fields to overcome significant limitations of modern approaches. Clinical applications and research demonstrate the diversity and depth of AI-based deep learning models in diagnostics, improving diagnostic accuracy and enabling precise, individualized therapy through advanced imaging and biomarker discovery. Through intelligent nanocarriers, nanotechnology contributes to these advancements by enabling targeted drug delivery, minimizing systemic toxicity, and providing theranostic capabilities for real-time monitoring. However, challenges remain, including data accessibility, model interpretability, scalability in nanocarrier manufacturing, and tumor diversity. Future improvements should focus on developing multifunctional nanoparticles, flexible AI algorithms, and scalable, cost-effective solutions to enhance accessibility and clinical integration. Hence, the study emphasizes the need for multidisciplinary collaboration to eliminate existing barriers and generate advancements to transform breast cancer therapies into more effective, safer, and individualized methods.

Graphical Abstract