Aug 2026· Frontiers in Drug Safety and Regulation· 0 citations· 33 references
TL;DR
This perspective examines recent developments in AI for PV and introduces a conceptual framework of “computable PV,” in which tasks are evaluated based on their computational tractability and suitability for automation.
Abstract
Advances in generative artificial intelligence (AI), particularly large language models (LLMs), have sparked discussions in automating pharmacovigilance (PV) workflows. It remains unclear whether these technological advancements fundamentally change the prior conclusions that full automation of Individual Case Safety Report (ICSR) processing is not feasible.
This perspective examines recent developments in AI for PV and introduces a conceptual framework of “computable PV,” in which tasks are evaluated based on their computational tractability and suitability for automation.
Routine, well-defined PV tasks, including completeness checks, detection of duplicated ICSRs, and structured information extraction, are increasingly amenable to automation. In contrast, complex activities such as case-level causality assessment remain difficult to formalize and continue to rely on expert judgment. The emergence of LLMs enables broader, cross-task capabilities compared to traditional task-specific, “small” models, but introduces challenges related to reliability, auditability, and governance. As a result, hybrid architecture combining large models, small models, and rule-based components is increasingly necessary.
Generative AI, as of today, does not signal full automation of PV but rather shifts toward hybrid human-AI systems. While AI can augment efficiency and support evidence synthesis, final decisions must remain under human oversight. Future PV systems should prioritize transparency, validation, and the integration of AI outputs into expert-driven decision-making.
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
A rapidly advancing precision-therapy pipeline-including antisense oligonucleotides to upregulate the intact allele, AAV-based gene replacement, CRISPR-mediated transcriptional activation, epigenetic modulators, and rational pathway-targeted small molecules-offers realistic prospects for disease modification.
This paper proposes the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a portable evaluation contract layer for agentic systems and describes an open-source reference implementation that includes adapters for LangChain, LlamaIndex, CrewAI, and PydanticAI.
Large Language Models show potential in their diagnostic accuracy and consequent ability to reduce clinician burden, and may provide the greatest benefit when used to optimise referral quality at source, improving both clinician and potentially LLM triage downstream.
K. Surendran, I. Aziz, Glyndwr Jenkins· Current Surgery Reports· 0 citations
Background Ambient AI documentation tools, known as scribes, are entering routine clinical practice at scale, but the evidence comparing the notes they produce against clinician-written notes is dominated by single-site, single-language studies that rely on human review to find errors, a method known to miss most documentation errors. Methods We conducted a paired simulation across five countries and languages (Cambridge/English, Barcelona/Spanish, Milan/Italian, Paris/French, Cologne/German; 385 paired consultations, 770 notes). From each actor-performed consultation, an AI scribe (Heidi) and a junior-to-middle-grade clinician independently produced a note. Notes were scored on the PDQI-9 by evaluators blinded to authorship. Documentation errors were identified by two methods of deliberately different sensitivity - clinician adjudication, and a calibrated automated reviewer externally validated against a blinded ten-clinician panel - then graded for clinical risk by a three-model panel. The co-primary outcomes were PDQI-9 total and Critical+High error burden, the latter reported under both detection arms. The analysis plan was registered before any pooling across sites. Results AI notes scored higher than clinician notes on the PDQI-9 (40.6 vs 35.6; difference +5.08, 95% CI 4.6-5.6; Cohen dz=0.55), consistently across all five sites (dz 0.41-0.75), and were less dispersed (5.7% of AI vs 27.8% of clinician notes fell below the study pre-specified low-score threshold (<32)). On the principal safety outcome - the paired probability that a note carried [≥]Critical+High error - clinician notes were affected more often under both detection arms: 61.0% versus 24.4% by the calibrated reviewer (relative risk 2.50, 95% CI 2.09-3.00) and 21.8% versus 6.2% by clinician adjudication (relative risk 3.50, 95% CI 2.32-5.27). The difference was largest for omissions. Unaided clinician review identified roughly 12% of the errors the calibrated reviewer retained, and a smaller fraction in AI notes than in clinician notes. Conclusions In this simulation, AI-generated notes scored higher on documentation quality, varied less, and carried fewer clinically significant errors than notes written on the same consultations by junior-to-middle-grade clinicians. The magnitude of the safety difference depends on the sensitivity of error detection, so we report both detection regimes and bound rather than point-estimate the absolute error rate. Extension to live practice, consultant-authored documentation, and notes as filed after clinician editing remains to be established.
H. Bergman, V. Liu, B. Austin et al.· medRxiv· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
