Evidence map›Paper›PMID 42799235›Full record

ArticleCureus2026

Artificial Intelligence Should Not Prescribe Alone: A Risk-Stratified Pharmacy Governance Framework for Agentic Clinical Systems.

Esteban Zavaleta-Monestel, José A Castro-Gamboa, Jeaustin Mora-Jiménez

Abstract readEditorial
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Esteban Zavaleta-MonestelResearch Department, Hospital Clínica Bíblica, San José, CRI.
José A Castro-GamboaPharmacy Department, Hospital Clínica Bíblica, San José, CRI.
Jeaustin Mora-JiménezResearch Department, Hospital Clínica Bíblica, San José, CRI.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Agentic clinical artificial intelligence (AI) is moving beyond conversational assistance towards systems capable of generating medication-related recommendations and structured clinical actions. Although recent models have shown strong performance in simulated disease-management and medication-reasoning tasks, benchmark performance does not establish the safety of autonomous prescribing. Medication use depends on patient-specific information, local formularies, medicine availability, monitoring capacity, clinical workflows, and professional accountability. This editorial argues that medication-related AI should be governed according to clinical risk. Lower-risk functions may support medication reconciliation, monitoring and patient communication, whereas treatment initiation, dose adjustment, antimicrobial selection, therapeutic substitution and deprescribing should require explicit pharmacist and prescriber validation. Local prospective evaluation, transparent documentation, version control, incident monitoring, and sufficient professional authority are essential. Agentic AI may offer meaningful clinical support in medication-related workflows, but its role in prescribing should remain pharmacist-governed, locally validated, and auditable rather than autonomous.

Indexed as

agentic artificial intelligenceartificial intelligence governanceclinical decision supportmedication safetypharmacy practiceprescribing

Identifiers

PMID42799235
PMCPMC13614363

What OpenQuestion holds

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Registered trials

None linked

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.