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Massive Individualization: Making the Exception the Rule in Clinical Care

Submitted:

07 July 2026

Posted:

08 July 2026

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Abstract
Precision medicine can already produce extraordinary individualized acts of care, often in high-stakes cases where standard pathways have failed. Yet when many patients need comparable work, clinical care cannot replicate it reliably across conditions, payers, and settings. What exists is a precision medicine of exceptions: acts generated under rare conditions rather than through system functions able to make them reliably available to all who could benefit. We name the alternative massive individualization: clinical acts tailored to each case, at scale. The obstacle is whether everyday practice can turn what is known about a person into an individualized course of care for each patient who would benefit. The usual incremental response adds pieces around that work, including funding, referral, decision support, and data sharing, but does not make the care itself repeatable. Drawing on a human-led rapid scoping review with AI-assisted retrieval and synthesis, we identify seven walls: no agreed way to pay for shaping and revising such care; capability that cannot yet be composed around each case; no systematic way to identify patients as candidates for individualization; authority tied to fixed approvals even as the patient’s situation changes; learning trapped inside the boundaries that produced it; integration that delivers outputs without sustaining synthesis; and no one accountable for the care as a whole. These are the operating functions massive individualization requires and current care lacks. Increasingly capable AI, including autonomous agents, makes these functions newly attainable, and their absence more dangerous, because such systems strengthen the arrangements already in place. The decisive test lies in building the functions that make the individualized act the rule rather than the exception, answerable at the level where each patient bears the consequence.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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