ArticleExploratory research in clinical and social pharmacy2026
Dosage adjustments in renal impairment among medical ward patients: ChatGPT® and DeepSeek® models' effectiveness in assessing those adjustments.
Article in Exploratory research in clinical and social pharmacy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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; 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.