From artificial to organic: Rethinking the roots of intelligence for digital health

PLOS Digital Health | https://doi.org/10.1371/journal.pdig.0001109  December 1, 2025 1 / 5   OPEN ACCESS Citation: Ghimire P, Ashkan K (2025) From artificial to organic: Rethinking the roots of intelligence for digital health. PLOS Digit Health 4(12): e0001109. https://doi.org/10.1371/ journal.pdig.0001109 Editor: Hadi Ghasemi, Shahid Beheshti University of Medical Sciences School of Dentistry, IRAN, ISLAMIC REPUBLIC OF Published: December 1, 2025 Copyright: © 2025 Ghimire, Ashkan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. OPINION From artificial to organic: Rethinking the roots of intelligence for digital health Prajwal Ghimire 1,2*, Keyoumars Ashkan1,2,3 1  School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom, 2  Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London, United Kingdom, 3  Institute of Psychology, Psychiatry and Neuroscience, King’s College London, London, United Kingdom * prajwal.1.ghimire@kcl.ac.uk Abstract The term “artificial” implies an inherent dichotomy from the natural or organic. How­ ever, AI, as we know it, is a product of organic ingenuity—designed, implemented, and iteratively improved by human cognition. The very principles that underpin AI systems, from neural networks to decision-making algorithms, are inspired by the organic intelligence embedded in human neurobiology and evolutionary processes. The path from “organic” to “artificial” intelligence in digital health is neither mystical nor merely a matter of parameter count—it is fundamentally about organization and adaption. Thus, the boundaries between “artificial” and “organic” are far less distinct than the nomenclature suggests. Introduction The mid-20th century was a formative era for the study of machine intelligence. In 1950, the British mathematician Alan Turing proposed a thought experiment—later known as the Turing Test—to probe a fundamental question: could a machine ever think? Turing argued that if a computer could execute a conversation so seamlessly that a human judge could not distinguish it from a real person, then, for all practical purposes, the machine was “thinking” [1]. His idea gave early researchers a criterion for comparing artificial behavior to human cognition, even if no one believed it to be a perfect or final measure. Just a few years later, in 1956, the Dartmouth Summer Research Project on Artificial Intelligence brought together a small group of visionary scientists [2]. They gave the new field its name, Artificial Intelligence (AI), and set forth the bold goal of replicating or exceeding human cognitive capabilities in non-biological substrates. These early pioneers approached their work as a grand quest to construct minds out of silicon and algorithms, rather than flesh and neurons. If Turing’s thought experi­ ment was a philosophical spark, the Dartmouth gathering ignited an entire academic discipline. PLOS Digital Health | https://doi.org/10.1371/journal.pdig.0001109  December 1, 2025 2 / 5 From these beginnings, the notion took hold that machine intelligence might evolve into a distinct and separate entity, growing ever more sophisticated until it approached or even surpassed human intellect [3]. Roots: Human inputs and patterns of thought To date, we continue to use Turing’s framework and the Dartmouth-inspired term “Artificial Intelligence”. Yet as AI technology has advanced, and particularly as data- driven machine learning systems have come to dominate the field, our understanding of what makes these systems “intelligent” has shifted. Instead of observing entirely new forms of reasoning emerging from isolated digital minds, we see something more nuanced: these systems are deeply and inescapably rooted in human inputs, human culture, and human patterns of thought [4]. For all the complexity of modern machine learning, the fact remains that today’s AI models learn from data we provide. Whether they are identifying objects in images, translating languages, recognizing speech, or engaging in human-like conversation, their abilities flow from patterns observed in massive, human-curated datasets [5]. The clever turns of phrase in a language model’s output are echoes of human writing. The refined decision-making of a recommendation system arises from signals in human behavior. Even the architecture of neural networks are designed, tuned, and improved by people drawing inspiration from biological brains and mathematical insights [6]. Terminology: Artificial, organic, and intelligence This interconnectedness underscores a crucial point

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