Categories \ Health Sciences
Ryan Mehra, Anshoo Mehra
Large-language-model (LLM) “hallucinations” are usually condemned as reliability faults because they generate confident yet false statements [1]. Emerging research, however, finds that such confabulations mirror divergent thinking and can seed novel hypotheses [2, 3]. This study is conducted by an independent investigators with no physical laboratory but unlimited API access to OpenAI models(4o, 4o-mini, 4.1, 4.1-mini)—tests whether deliberately elicited hallucinations can accelerate medical innovation. We target three translational aims: (i) epistemological creativity for medicine, where speculative errors inspire fresh research questions; (ii) generative biomedical design, exemplified by hallucinated protein and drug candidates later validated in vitro [4]; and (iii) speculative clinical engineering, where imaginative missteps suggest prototypes such as infection resistant catheters [5]. A controlled prompt-engineering experiment compares a truth-constrained baseline to a hallucination-promoting condition across the four OpenAI models. Crucially, all outputs are scored for novelty and prospective clinical utility by an autonomous LLM-based “judge” system, adapted from recent self-evaluation frameworks [6], instead of human experts. The LLM judge reports that hallucination-friendly prompts yield 2–3× more ideas rated simultaneously novel and potentially useful, albeit with increased low-quality noise. These findings illustrate a cost-effective workflow in which consumer-accessible LLMs act both as idea generator and evaluator, expanding the biomedical creative search space while automated convergence techniques preserve epistemic rigor—reframing hallucination from flaw to feature in at-home medical R&D.
10.69831/e59eafc04e