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PERSPECTIVE article

Front. Oncol.
Sec. Breast Cancer
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1465720
This article is part of the Research Topic Progressive Role of Artificial Intelligence in Treatment Decision - Making in the Field of Medical Oncology View all 3 articles

Empowering Precision Medicine: Regenerative AI in Breast Cancer

Provisionally accepted
Sudip Bhattacharya Sudip Bhattacharya 1*Dr Sheikh Mohd Saleem Dr Sheikh Mohd Saleem 2Alok Singh Alok Singh 3Sukhpreet Singh Sukhpreet Singh 4Dr Shailesh Tripathi Dr Shailesh Tripathi 5
  • 1 All India Institute of Medical Sciences, Deoghar (AIIMS Deoghar), Deoghar, India
  • 2 Government Medical College (GMC), Srinagar, Jammu and Kashmir, India
  • 3 Faculty of Medicine and Health Sciences, Shree Guru Gobind Singh Tricentenary University, Gurugram, Haryana, India
  • 4 Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, Haryana, India
  • 5 Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India

The final, formatted version of the article will be published soon.

    In the rapidly evolving landscape of breast cancer diagnosis and treatment, regenerative AI offers a promising avenue for improving patient outcomes and advancing personalized care. As an example, Niramai Health Analytics in Bangalore, India, developed an AI-based, low-cost, non-invasive solution for early breast cancer screening using body heat mapping. However, this transformative technology also faces several significant challenges that must be addressed to realize its full potential. Firstly, the need for large, diverse, and high-quality datasets presents a fundamental obstacle to training AI algorithms effectively. Overcoming this challenge requires collaborative efforts to collect and curate comprehensive datasets that encompass the full spectrum of patient demographics, clinical characteristics, and disease subtypes. Additionally, ensuring the fairness, transparency, and interpretability of AI-driven models is paramount for gaining clinician trust and regulatory approval. This involves implementing rigorous validation and testing protocols, as well as developing standards for algorithmic accountability and transparency. Moreover, integrating AI-powered tools into existing healthcare workflows poses logistical and organizational challenges, including interoperability with electronic health records and clinician resistance to adopting new technologies.Addressing these challenges requires a coordinated approach that involves stakeholders across the healthcare ecosystem, including patients, clinicians, researchers, policymakers, and technology developers.

    Keywords: breast cancer, regenerative AI, artificial intelligence, breast carcinoma, machine learning and AI, deep learning

    Received: 16 Jul 2024; Accepted: 27 Aug 2024.

    Copyright: © 2024 Bhattacharya, Saleem, Singh, Singh and Tripathi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sudip Bhattacharya, All India Institute of Medical Sciences, Deoghar (AIIMS Deoghar), Deoghar, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.