Clinical outcomes and reporting quality of large language model interventions in practice: a systematic evidence map

Large language models (LLMs) are being deployed in clinical settings despite an underdeveloped evidence base regarding their real-world effectiveness. This study employed systematic evidence mapping to characterize outcome measures used in published studies and registered clinical trials (Jan 2022–Jun 2025) evaluating LLM performance. Analysis of 55 included studies revealed a predominance of human-AI collaborative designs (65.5%) for decision support and symptom management. LLM-only interventions focused on functional performance and operational or process impact outcomes (e.g., accuracy and time saving), whereas LLM-assisted interventions showed positive clinical effects, particularly in psychological health endpoints. Critical evidence gaps persist: diagnostic accuracy in randomized trials was notably lower and more variable (range 0.65–0.88) compared to non-randomized studies (typically ≥ 0.80); clinical efficiency impacts were inconsistent, and reporting quality was suboptimal (78.8% mean CONSORT-AI adherence), with critical omissions in handling data quality and performance errors. These findings indicate a heterogeneous and insufficient evidence landscape, necessitating standardized core outcome sets, mandatory use of specialized reporting guidelines, and robust clinical trials to ensure the safe integration of LLMs.