JMIR Publications

ABSTRACT

Artificial intelligence is changing the conditions of biomedical research. Candidate mechanisms and experiments can now be generated and evaluated faster than research teams can interpret them or separate them empirically, creating a task-relative imbalance that we describe as computational abundance. This condition enlarges what researchers can consider, yet its scientific value depends on whether the candidates preserve different causal accounts and lead to observations capable of changing their relative standing. We propose potential search density for this qualitative opportunity, in which mechanistically different candidates remain jointly inspectable, imply discriminating observations, and have traceable provenance. Proactive researchers and accountable teams carry this opportunity into science through reciprocal reconstruction: they compare alternatives, select a discriminating observation, revise the working account in light of the result, and return the changed problem to computation. Because that transition can redirect the work, we propose inferential ownership as a normative criterion requiring three elements to remain reconstructable: the assumption supporting the interpretation, the alternative displaced by the decision, and the observation that would reopen it. Computational abundance can also stabilize the starting question when correlated outputs imitate independent convergence, proposal generation outruns verification, or automation concentrates reasoning at failure. In biomedical research, these limits acquire meaning through the unresolved patient. A clinical course that resists the working explanation can move upstream as a discrepancy, reopen comparison among mechanisms, and revise the experiment and next computational objective. Computational abundance contributes to better science when the proactive function preserves the path from candidate generation to discriminating evidence and allows that evidence to change what is tested next.