‘Artificial’ label obscures human responsibility in medical AI

AI intelligence is not artificial in the sense of being detached from organic origins. The study reframes intelligence as a property defined by organization and adaptability rather than by the material substrate on which it operates. From this perspective, intelligence expressed through silicon circuits can still be fundamentally organic in nature because it is derived from human cognitive processes.

A new opinion study warns that mischaracterizing AI as non-human obscures both its origins and its risks. The research, titled From Artificial to Organic: Rethinking the Roots of Intelligence for Digital Health, published in PLOS Digital Health, argues that AI systems are not synthetic minds detached from biology, but inorganic extensions of organic human intelligence shaped by human data, values, biases, and design choices. This conceptual shift, the authors say, carries major implications for accountability, safety, and governance in healthcare.

Why artificial intelligence is not truly artificial

The study traces the origins of AI thinking back to the mid-20th century, when early pioneers framed machine intelligence as a separate, engineered phenomenon. Landmark moments such as the proposal of the Turing Test and the Dartmouth conference established the ambition of building intelligence outside the human brain. Over time, this framing solidified into the idea that artificial intelligence could become an autonomous cognitive force.

However, the authors argue that modern AI systems tell a different story. Contemporary models, especially those used in healthcare, do not generate intelligence independently. They learn from vast collections of human-generated data, including clinical records, medical images, scientific literature, and behavioral signals. Every output they produce is statistically derived from patterns embedded in human activity and knowledge.

Neural networks, often cited as evidence of machine cognition, are themselves inspired by biological brain structures. Their architecture, optimization, and evaluation are the result of decades of neuroscience research translated into mathematical form. Even their apparent creativity or decision-making ability emerges from exposure to human language, medical reasoning, and clinical examples.

This means that AI intelligence is not artificial in the sense of being detached from organic origins. The study reframes intelligence as a property defined by organization and adaptability rather than by the material substrate on which it operates. From this perspective, intelligence expressed through silicon circuits can still be fundamentally organic in nature because it is derived from human cognitive processes.

This distinction matters because it changes how responsibility is assigned. If AI systems reflect human inputs, then their errors, biases, and limitations are not accidental machine failures. They are amplified expressions of human choices encoded into data, algorithms, and objectives.

Implications for digital health and medical decision-making

The study places particular focus on digital health, where AI systems are increasingly entrusted with high-stakes decisions. From radiology triage and cancer stratification to predictive analytics and patient monitoring, AI tools are now integrated into clinical environments that demand accuracy, fairness, and accountability.

Understanding AI as organically rooted has direct consequences for how these systems are evaluated and governed. Bias in medical AI, for example, cannot be dismissed as a technical glitch. It reflects biases present in clinical datasets, institutional practices, and historical healthcare inequalities. If certain populations are underrepresented or misrepresented in training data, AI systems will reproduce those distortions at scale.

The authors argue that reframing AI as an extension of organic intelligence clarifies ethical responsibility. Clinicians, developers, and institutions remain accountable for AI-driven outcomes because the intelligence guiding those systems originates in human decisions. This challenges narratives that treat AI errors as unpredictable or unavoidable consequences of autonomous machines.

The paper also addresses growing interest in artificial general intelligence and superintelligence within healthcare. While advances in hardware and algorithms have expanded AI capabilities, the authors caution against equating scale with intelligence. Larger models may process more data faster, but without careful organization, explainability, and safeguards, they risk magnifying errors rather than improving care.

In clinical settings, speed must be balanced with safety. The study highlights the need for uncertainty-aware systems that can signal when predictions are unreliable, as well as mechanisms for rollback and human intervention. Explainability is framed not as a luxury but as a safety requirement, especially when AI recommendations influence diagnosis or treatment.

The research also points to practical constraints that limit AI deployment in healthcare, including data quality, cross-institution variability, computational cost, and energy consumption. These constraints further reinforce the need for designs inspired by biological efficiency rather than brute-force computation.

Rethinking intelligence to reshape AI governance

The study argues that the language used to describe AI shapes research priorities and regulatory approaches. The artificial versus natural divide encourages a focus on scale, performance benchmarks, and competition between humans and machines. By contrast, an organic versus inorganic framing emphasizes adaptability, integration, and shared responsibility.

This shift has implications for how AI systems are tested and regulated. Instead of static benchmarks that measure accuracy on fixed datasets, the authors advocate for dynamic evaluation methods that assess adaptability, calibration under changing conditions, and resilience to distribution shifts. These qualities are particularly relevant in healthcare, where patient populations, technologies, and clinical practices evolve over time.

The organic framing also encourages deeper collaboration across disciplines. Neuroscientists, clinicians, and AI engineers are urged to work together, treating intelligence as a continuum rather than a categorical divide. This integration could lead to systems that better align with human cognition and clinical workflows, reducing the risk of unsafe automation.

Accountability mechanisms are another key focus. The study calls for governance structures that are embedded into AI systems rather than added as afterthoughts. This includes architectural features that log changes, track model evolution, and trigger abstention when confidence is low. Such measures would make AI behavior more transparent and auditable, aligning it with medical and legal standards of responsibility.

The authors also address broader concerns around the future of intelligence. As discussions around superintelligence gain momentum, particularly in policy and industry circles, the paper urges caution. Intelligence, they argue, should not be defined solely by performance metrics or autonomy. In healthcare, intelligence must be measured by its ability to support human judgment, preserve safety, and respect ethical boundaries.