Positive act of reporting negative results in large language model research: a call for transparency.

Abstract

To highlight the importance of reporting negative results in large language model (LLM) research, particularly as these systems are increasingly integrated into healthcare. LLMs offer transformative capabilities in text generation, summarization, and clinical decision support. Transparent documentation of both successes and failures can accelerate innovation, improve reproducibility, and guide safe deployment. Publication bias toward positive findings conceals model limitations, biases, and reproducibility challenges. In healthcare, underreporting failures risks patient safety, ethical lapses, and wasted resources. Structural barriers, including a lack of standards and limited funding for failure analysis, perpetuate this cycle. Negative results should be recognized as valuable contributions that delineate the boundaries of LLM applicability. Structured reporting, educational initiatives, and stronger incentives for transparency are essential to ensure responsible, equitable, and trustworthy use of LLMs in healthcare.