The poverty of diagnostic essentialism: reimagining diagnosis in the age of artificial intelligence
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Cory Rohlfsen
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Andrew S. Parsons
Abstract
The pursuit of medical diagnosis has long been shaped by an epistemic framework that assumes diseases have inherent, discoverable essences. This essentialist approach, deeply rooted in Aristotelian thought, has historically guided diagnostic reasoning and classification for over a century. However, the rise of artificial intelligence (AI) is catalyzing a philosophical and practical shift toward nominalism – a framework in which diagnoses are derived from dynamic, data-driven pattern recognition rather than fixed disease categories. This transition, if it occurs, would be revolutionary, exposing core limitations of essentialist thinking and reframing diagnosis as a process rather than a static conclusion. In doing so, it challenges the conventional concept of an ‘endpoint diagnosis’ – the idea that diseases can be definitively and completely categorized. Instead, diagnosis emerges as a contingent narrative point within broader clinical trajectories, calling for a reimagining of diagnostic reasoning in the AI era.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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