CONCEPTUAL ANALYSIS article

Front. Physiol.

Sec. Computational Physiology and Medicine

Volume 16 - 2025 | doi: 10.3389/fphys.2025.1649114

This article is part of the Research TopicMedical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine Volume IIView all 17 articles

Mass Medicine vs Personalized Medicine: from Mathematical Methods to Regulatory Implications

Provisionally accepted
Grigori  SigalovGrigori SigalovInderpal  S RandhawaInderpal S Randhawa*
  • Food Allergy Institute, Long Beach, United States

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

Clinical trials of a treatment in traditional mass medicine are based on the concept of proof of efficacy. It must be proven for a group of subjects that meet certain selection criteria. Subject variability must be demonstrated to exist and yet not to invalidate the proof of efficacy. If so, it is assumed that new patients meeting the same selection criteria would have a uniform response to treatment, irrespective of their individual traits. However, the variability that can be ignored for a group should not be ignored for an individual. Standard statistical methods are designed to estimate an average effect size for large enough groups, but they cannot predict an expected effect size for a single patient. Such predictions based on the patient's individual characteristics, rather than on their classification as a member of a target population or study group, are possible in personalized medicine. The latter employs multivariable predictive models via advanced mathematical methods implemented in Artificial Intelligence (AI), and it incorporates the subject variability in the predictive models to improve their accuracy and selectivity. There is a common misconception that personalized medicine belongs in a narrow area of rare diseases or genotype-guided care. In this paper, we argue that AI has potential to improve the treatment success estimates in traditional mass medicine as well at no extra cost to researchers. The clinical trial data on subject variability that are already routinely collected only need to be analyzed and interpreted using the methods of personalized medicine. To implement such improvements in medical practice, they need to be acknowledged and regulated by FDA and its counterparts in other countries.

Keywords: AI, machine learning, Adaptive clinical trials, patient variability, subgroup analysis, Multivariable models, Predictive Modeling, precision medicine

Received: 18 Jun 2025; Accepted: 30 Jun 2025.

Copyright: © 2025 Sigalov and Randhawa. 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: Inderpal S Randhawa, Food Allergy Institute, Long Beach, United States

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.