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



Shirzad M1 ; Shaban M1 ; Mohammadzadeh V1 ; Rahdar A2 ; Fathikarkan S3, 4 ; Hoseini ZS5 ; Najafi M6 ; Kharaba Z7 ; Aboudzadeh MA8
Authors
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Authors Affiliations
  1. 1. Pharmaceutical Technology Institute, Nanotechnology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
  2. 2. Department of Physics, University of Zabol, Zabol, Iran
  3. 3. Natural Products and Medicinal Plants Research Center, North Khorasan University of Medical Sciences, Bojnurd, 94531-55166, Iran
  4. 4. Department of Medical Nanotechnology, School of Medicine, North Khorasan University of Medical Science, Bojnurd, Iran
  5. 5. Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
  6. 6. Department of Chemical Engineering, Sharif University of Technology, Tehran, Iran
  7. 7. Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
  8. 8. POLYMAT and Department of Applied Chemistry, Faculty of Chemistry, University of the Basque Country UPV/EHU, Paseo Manuel de Lardizabal 3, Donostia-San Sebastian, 20018, Spain

Source: BioNanoScience Published:2025


Abstract

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. © The Author(s) 2025.