2nd Edition of Cell & Gene Therapy World Conference 2026

Abstract

ABSTRACT Medicine is living through one of those rare moments when the ground shifts beneath it. Just as antibiotics once split medical history into a “before” and an “after,” cell and gene therapies — now converging with artificial intelligence — are forcing us to redefine what it means to treat, to heal, and to be a physician. A truly disruptive technology is never a gradual evolution; it is a revolution that demands we rethink our rules. This talk traces the evolution of the medical paradigm through the question each era has asked. Evidence-based medicine asks: is it statistically proven? Value-based medicine asks: does it bring real benefit to the patient and the system? Regenerative and cellular medicine asks something more radical: can biological function itself be restored and extended? A rapidly maturing evidence base — more than six thousand clinical trials over the past fifteen years — shows this is no longer speculation. Yet the frontier now moves faster than the frameworks meant to govern it. Herein lies the central tension. The path from a laboratory breakthrough to a therapy a patient can trust runs through the least glamorous parts of our work: standardized technology transfer, GMP-grade manufacturing, reproducible protocols, clinical validation, and transparent regulation. Drawing on three decades spanning cardiology, clinical-trial leadership, and regenerative medicine, this presentation argues that standardization, scientific rigor, and ethics are not obstacles to innovation — they are the very forces that turn a breakthrough into medicine, and a promising cell product into an accessible one. The physician of the future will not be replaced by the algorithm, nor reduced to a technician of cells; they will become the guarantor of safety, reproducibility, and humanity at the frontier. Keywords:  regenerative medicine · cell & gene therapy · standardization & technology transfer · clinical translation · evidence-based medicine · medical ethics · AI in medicine