Key result
Severe renal impairment increased improper dosing risk (AOR 3.34, p=0.004), and AI models evaluating these adjustments showed limited accuracy, with DeepThink at 67.5% and ChatGPT 5.0 at 61.7%.
Why the study?
Inappropriate drug dosing persists in renal impairment, and the utility of AI tools in supporting renal dose adjustments remains understudied.
Can AI models accurately assess renal dose adjustments in hospitalized patients compared to UpToDate guidelines?
Observational (n=148)
Can AI models accurately assess renal dose adjustments in hospitalized patients compared to UpToDate guidelines?
AI models show promise for assessing renal dose adjustments but currently lack the accuracy and specificity required to reliably replace evidence-based guidelines in clinical practice.
Answers are grounded in this paper and cite their sources.
High inappropriate renal dosing rates warrant enhanced clinical oversight; leaves open AI integration pending prospective validation.
Background Due to altered drug clearance, renal impairment necessitates drug dose adjustments to prevent toxicity or therapeutic failure, yet inappropriate dosing persists. The utility of AI tools (e.g., ChatGPT®, DeepSeek®) in supporting renal dose adjustments remains understudied. Objective Evaluate renal dose adjustment practices in hospitalized patients and compare AI models (ChatGPT®, DeepSeek®) against UpToDate® guidelines. Method A prospective observational study (January–April 2024) included hospitalized patients with creatinine clearance <60 mL/min in a general medicine ward. Medication regimens were assessed against UpToDate® guidelines. Five AI models (ChatGPT® 3.5, ChatGPT® 4.0, ChatGPT® 5.0, DeepSeek®, DeepThink®) were tested using 348 tailored prompts; sensitivity, specificity, and accuracy were calculated. Results Renal impairment prevalence was 30.9 %. Of 1461 drug orders, 23.8 % (348) required adjustment, with 63.5 % (221/348) inappropriately dosed in 76.4 % (113/148) of patients. Errors included 134 (38.5 %) unadjusted, 75 (21.6 %) inappropriately adjusted, and 12 (3.4 %) contraindicated regimens. Piperacillin/tazobactam (66.7 %), levofloxacin (83.33 %), and ranitidine (89.1 %) were most frequently inappropriately dosed. Severe renal impairment (CrCl ≤30 mL/min) increased improper dosing risk (AOR: 3.34; p = 0.004). DeepThink® showed the highest sensitivity (81.6 %) but low specificity (29 %) and 67.5 % accuracy. With advances in the ChatGPT® model, there was a modest improvement in prediction capacity, with the latest ChatGPT® 5.0 achieving balanced performance (64.3 % sensitivity, 54.8 % specificity, 61.7 % accuracy). Conclusion Inappropriate renal dosing is prevalent among hospitalized patients with renal impairment. While AI models show promise as clinical decision support tools, their accuracy limitations require further optimization via evidence-based database training, prompt refinement, and integration of clinical expertise for reliable implementation.
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A 2025 study conducted an observational in Renal impairment (n=148). AI models (ChatGPT, DeepSeek, DeepThink) vs. UpToDate guidelines was evaluated on Accuracy in assessing renal dose adjustments. Severe renal impairment increased improper dosing risk (AOR 3.34, p=0.004), and AI models evaluating these adjustments showed limited accuracy, with DeepThink at 67.5% and ChatGPT 5.0 at 61.7%.
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