In the search phase, we used 3 major databases: PubMed, Web of Science, and Google Scholar. We queried databases for potentially relevant records using a broad range of keywords stratified into five groups: (1) Pharma R&D, (2) drug-related terms, (3) ADE-related terms, (4) the type of algorithm that was used, and (5) the task that the algorithm was supposed to perform. We combined the keywords within each group using the OR operator, and all groups were combined using the AND operator. To search for papers, we applied the default settings of the respective databases using the title and abstract fields. Table 2 contains the search keywords used in the process.

To ensure relevance, we applied specific inclusion and exclusion criteria, as reported in Textbox 1, namely papers written in English, with ADEs as the main topic, involving mammalian species, and published in peer-reviewed journals or conference proceedings between January 1, 2015, and October 15, 2025. To focus on the recent trend in the AI field, we excluded papers that did not use LMs in their modeling approach.

Three researchers (OS, AY, and DT) independently screened the titles and abstracts. The resulting set was cross-checked by IG and RK. Then, OS, AY, IG, RK, and DT read the full texts independently and extracted item information using a standard spreadsheet created before the analysis in line with the research questions. Any differences in including or excluding full-text studies were resolved during a consensus meeting. The final dataset was based on the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) checklist and includes the publication date, whether the study was published in a journal or a conference, country of the corresponding author, source of data used, task formulation, toxicity endpoints that were predicted, affected organ, metrics of performance evaluation, the algorithm used, the nature of the algorithm used, the type of LM, the features that were used for the modeling, and the drug design and development stage.

We analyzed the data using Microsoft Excel for Mac (Microsoft Office 365, version 16.69). We used descriptive statistics like frequencies and ranges and presented the data graphically and in tabular format, as needed.

The search query resulted in 1397 records from PubMed (n=695), Web of Science (n=514), and Google Scholar (n=188), which, after removing duplicates (n=503), and performing a “title & abstract” screening (n=894; n=836 records excluded), and a full-text eligibility assessment (n=58; n=9 records excluded), were narrowed down to 49 records included for analysis. Our study selection flowchart is shown in Figure 1.

This review covers studies published both in scientific journals (n=42) and conferences (n=7). Based on the corresponding author’s affiliation, 11 of 49 papers are located in China, equaling the number in the United States (n=11), 5 in India, and 4 in Korea, followed by 12 other countries. The increased number of studies on this field over the past 10 years (Figure 2A) and the large geographical spread of the included studies highlight the growing global interest in developing AI algorithms based on LMs for Pharma R&D safety risk assessment, as well as the potential for international collaborations in this regard. As we can note from Figure 2B, studies cover a large variety of tasks, including ADE-related information extraction from free text, such as named entity recognition (NER) (n=23) [3,23-31], safety prediction based on molecular structure, such as toxicity prediction (n=13) [32-35], and disproportionality analysis for signal detection (n=6) [28,34,36-39].

As shown in Figure 3, we can categorize AI applications for safety risk assessment in drug design and development into 2 main groups: safety prediction and ADE detection. These different applications are found across four of the five stages of drug design and development [40]: (1) discovery and development, (2) preclinical research, (3) clinical research, (4) regulatory review, and (5) postmarket safety monitoring. Due to the challenge of specifying the exact stage that the study addresses, for simplicity, we grouped these 5 stages into 2: pharmaceutical research (premarket) and postmarket safety monitoring (postmarket). Premarket encompasses from stage 1 (discovery and development) to stage 3 (clinical research), including clinical trials from phase I to phase III, while the postmarket stage (safety monitoring—stage 5) includes applications related to clinical trials in phase IV and pharmacovigilance. Regulatory review (stage 4) acts as a bridge between the premarket and the postmarket phases and is not covered in this review.

The safety prediction category (top image of Figure 3) encompasses LM-based applications that can predict safety risks before synthesis and preclinical or clinical testing, given a drug or compound formulation. These applications can be used during the discovery and development stage to support screening and during the preclinical and clinical research stages to support safety risk assessment. Prediction studies can be further subdivided into 3 predictive application use cases: toxicity [32,33,41-48], ADEs [23,49,50], and severity [51]. Toxicity prediction methods are often binary classifiers that predict whether a drug or compound will be toxic for an organ, such as drug-induced liver injury prediction [52-55], or regressors that predict toxicity properties, such as skin sensitization scores [56,57]. In ADE prediction, AI methods supported by LMs are designed to predict the occurrence of ADEs, that is, injuries resulting from the use of a drug. These methods are usually multiclass, multilabel classifiers that infer the occurrence of adverse event categories, such as those proposed by the Medical Dictionary for Regulatory Activities (MedDRA) terminology. Conversely, methods for ADE severity prediction are usually binary classifiers that aim to infer the severity of ADEs, such as serious versus nonserious or death versus nondeath events. In terms of phase, these studies (n=16) are concentrated in the premarket pharmaceutical research stage, with the notable exception of the study by Mazuz et al [51], which focuses on predicting drug withdrawal based on safety concerns.

The ADE detection category (bottom image of Figure 3) encompasses LM applications that extract ADE-related information from individual documents in a given corpus so that signal detection can be performed. These detection studies can be further subdivided into 2 categories: information extraction, including NER [3,36,58-65], relation extraction (RE) [24,27,62,66-69], entity linking (EL) [28,36,59,61,62], and document classification, including ADE mentioning in documents [37,70-74] and their seriousness [27,39,75]. NER methods are used to identify ADE-related entities, such as drugs, dosage, route of administration, and ADE names, in relevant pharmacovigilance corpora, such as patient forums [26,28-31,59,61,63,76] and social media [30,31,59-61,63,65,70,71,76]. RE methods are often combined with NER methods to identify relationships between ADE-related entities. For example, they can establish whether a drug is associated with an ADE [24,27,62,66,67], while EL methods are used to normalize ADE entities against standard terminologies in the field, such as MedDRA [23,27,28,39,59,60,62]. These tasks ultimately enable the structuring of ADE-related information found in free-text corpora, allowing for further computation of ADE cases for a given drug and the application of signal detection algorithms. Document classification is a simpler task, in which text passages, such as tweets [70,71,74], posts in patient forums [29,61,76], or incident reports [36,37,62,64,75], are classified as containing ADE information or the seriousness of the reported ADE [27,39,75]. Unlike the information extraction category, the goal here is to triage large corpora to reduce the cost of manual processing or enable further automated information extraction. Stage-wise, these studies (n=33) are concentrated in the postmarket safety monitoring stage (Table 3).

ADEs can manifest in various organs; yet, most reviewed studies addressed ADE prediction in a general context rather than focusing on specific organ toxicities. This is mainly the case of AI models for ADE detection in the postmarket safety monitoring stage. These models often leverage terminologies such as the MedDRA [78] or the World Health Organization Anatomical Therapeutic Chemical classification system [79] to infer the occurrence of adverse event categories across broad organ systems [80,81]. When it comes to the pharmaceutical research (premarket) stage, we see a shift toward predicting organ-specific toxicities with binary classifiers, which assess whether a compound will be toxic to particular organs [52,55,82-86].

The heart (n=6) [32-34,41,46,48] and liver (n=4) [32-34,46] emerge as the most studied organs for organ-specific ADE prediction (Table 4), likely due to their roles in drug metabolism and systemic effects, respectively. Our screening shows that predicting ADEs in other organs, such as the brain or pancreas, poses a greater challenge due to limited experimental data and the inherent difficulty that comes from the resource-intensive and complex methodologies needed in assessing toxicity for these organs [87,88].

Of the many toxicity endpoints that different groups have tried to develop models for, the prediction of cardiotoxicity [32,34,48] and drug-induced liver injury [33,46] are particularly well-represented in the literature (Table 4), reflecting their clinical significance and data availability for these toxicity endpoints. Models able to provide such predictions can serve as a filter to identify potentially harmful drugs in the premarket stage and reduce the failure risk in the later drug development stages.

Assessment of LM use over time reveals a clear evolution in methodological choices (Figure 4A). Until 2018, all studies relied exclusively on static LMs [29,65,66,71]. Between 2019 and 2022, a more balanced use of static and contextualized LMs was observed, reflecting a transitional phase toward context-aware representations [3,36,37,49,50,58-61,64]. From 2023 onward, more advanced architectures emerged, with the introduction of encoder-decoder models and LLMs [23,25,30,31,47,51,63], which rapidly gained prominence, accounting for approximately half of the studies included by 2025. Notably, no publication from 2024 used encoder-decoder models or LLMs. This pattern may be because most of the papers published in 2024 that we included in our review [41,42,46] were preclinical studies, which primarily rely on discriminative models rather than more recent generative LMs. However, stochastic variation in publication trends cannot be excluded.

Further analysis of the LMs used across the included studies shows that the majority relied on the bidirectional encoder representations from transformers (BERT) architecture (n=24; Figure 4B) [3,26-28,41,51,59,69,75,77]. This predominance is consistent with the architectural design of BERT as an encoder-only, bidirectional model, which is particularly well-suited for generating high-quality contextualized embeddings for downstream tasks such as classification, similarity analysis, and information retrieval. In contrast, large generative models such as GPT-4 or LLaMA are primarily optimized for autoregressive text generation rather than embedding extraction, making BERT-based models generally more precise and computationally efficient for representation learning purposes. The word2vec model (n=13) was the second most frequently used [36,43,49,50,61,62,64-66,71], followed by more recent approaches, such as RoBERTa (n=7) [26,31,59,70,72,73,75] and GPT (n=6) [30,31,48,63,73,76] architectures. A substantial proportion of studies using these models was conducted for safety risk assessment in postmarket settings. This predominance of postmarket applications can be explained by the fact that the tasks most related to this phase, that is, information extraction and document classification (Figure 2B), rely extensively on natural language processing. In contrast, the use of LMs, such as ChemBERTa, especially for molecular representations in premarket studies, is a more recent adaptation of language modeling to other data modalities.

If we look at the model architectures across the included studies (Figure 4C), we see a clear predominance of discriminative approaches (n=44), that is, LMs that focus on learning the boundaries between different tokens in a corpus, such as BERT [3,41,51,69,75], word2vec [36,43,49,62,64], and XLNet [26,31]. These models are used for tasks such as NER and RE, as well as toxicity and ADE prediction [23,24,49,50,66,67,70-73]. Discriminative models, often implemented as fine-tuned, encoder-based transformer architectures, are particularly well-suited to these objectives, as they are optimized for classification and sequence labeling tasks that rely on well-defined input-output mappings. In contrast, studies leveraging generative LMs, that is, models that generate text by predicting the next token based on preceding context, focus on NER [25,30,31,63,73,76] (n=6), benefiting from the zero-shot learning (ie, without annotated data) capabilities of those models. As seen from Figure 4A, the use of these models has significantly increased in the last year of the survey, benefiting from the recent progress made in LLMs.

LLM-based models are often used in combination with classical ML (n=4) [28,29,66,68] and deep learning (DL) models (n=31) [32,42-44,50,62,64,70,71,76]. Among the ML and DL models used in parallel or in combination with LLMs, support vector machines (SVMs) are the most prevalent (n=13) [28,29,33,41,44,47,48,50,68,71], followed by graph neural networks (GNNs; n=12) [32,33,35,41-43,47,48,51,70,76], then recurrent neural networks (RNNs; n=10) [3,35,43,47,58,60,62,64,65], with convolutional neural networks (CNNs; n=8) [3,35,43,46,47,60,71,74] used at a comparable frequency to conditional random fields (CRFs; n=8) [3,28,29,58,62,64-66]. Overall, SVM and CRF are the most used among classical ML approaches, whereas GNNs, RNNs, and CNNs are predominant among DL methods. In addition, across the reviewed studies, GNNs are primarily used for molecular representations [32,33,35,41-43,47,48,51], whereas SVMs, RNNs, CNNs, and CRFs are mainly used for both molecular representation [33,35,41-44,46-48,50] and clinical text data [3,58,66,68].

AI-based safety assessment studies in Pharma R&D leverage diverse structured and unstructured data sources for safety prediction and ADE detection (Table 5). Premarket studies focusing on molecular representations primarily rely on curated chemical and pharmacological datasets, with SIDER (n=6) [33,41,44,48-50] and ClinTox (n=3) [33,41,45] being the most frequently used resources, reflecting their central role in modeling toxicity and drug-ADE associations. Knowledge bases such as DrugBank, ChEMBL, and PubChem further complement these analyses by providing chemical and biological context.

Postmarket safety monitoring predominantly exploits real-world data, including social media, clinical text, and reporting systems. Twitter-based datasets from SMM4H (n=8) [25,26,31,59,60,63,70,76] and CADEC (n=5) [26,31,59,63,76] are widely used to capture patient-reported ADEs and enable near real-time signal detection. Clinical text-based studies mainly rely on annotated EHRs (n=5) [38,66-68,72], as well as MADE and n2c2 datasets, to support supervised ADE extraction. Across all data focuses, scientific literature sources (eg, ClinicalTrials.gov and PubMed or MEDLINE) [23,27,45,50,69,70] and spontaneous reporting systems such as the FDA Adverse Event Reporting System Database (FAERS) [34,36,39,44,48,61] provide complementary evidence for large-scale pharmacovigilance analyses.

As shown in Figure 5A, distinct methodological trends can be observed with respect to the targeted tasks across the different data focus categories, namely, molecular representation, social media, clinical text, scientific literature, and incident reports. Studies relying on molecular representations predominantly focus on toxicity prediction [32-35,41-48,77], reflecting their emphasis on molecular structure features for safety risk assessment. In contrast, studies leveraging social media, clinical text, scientific literature, and incident reports primarily address NER [3,36,58-65], as these data sources consist largely of unstructured text for which entity identification is a foundational step. Within these text-based domains, social media–based studies also place a strong emphasis on EL, aiming to normalize patient-reported mentions of drugs and ADEs to standardized vocabularies [28,59,61]. Clinical text-focused studies, on the other hand, more frequently target RE as the additional task [24,66-68], seeking to identify explicit associations between drugs and ADEs within clinical narratives. Studies based on incident reports exhibit a broader task spectrum, commonly integrating NER, EL, and RE [36,62,64], in addition to document classification [37] and signal detection [36,37,39], to facilitate triage and fully exploit structured reporting formats for pharmacovigilance signal detection, enabling a comprehensive pharmacovigilance analysis.

With respect to the evaluation metrics (Figure 5B) used, clear differences emerge between premarket and postmarket evaluation practices. Metrics such as the F₁-score, sensitivity (recall), and precision are most frequently reported overall, largely due to their extensive use in postmarket studies [3,27,29,58-60,62,63,69,74]. In contrast, evaluation metrics, including the area under the receiver operating characteristic curve (AUROC), Matthews correlation coefficient, accuracy, specificity, and balanced accuracy, are predominantly used to assess premarket studies [32,33,41-45,48,49,77]. Certain metrics, notably the reporting odds ratio and proportional reporting ratio, are exclusively used in postmarket settings for statistical signal detection [28,34,36-39].

Most of the reviewed papers use English datasets (n=28) [3,23,36,49,58-61,63,70] and publicly available data (n=38) [3,23,41,46-49,58,60,73] (Figure 5D), reflecting the widespread use of open premarketing chemical and postmarketing pharmacological databases in this domain [34,35,48,51,60,61,63,65,71,77]. A minority of papers analyze other European languages, such as French (n=2) [28,75] and Swedish (n=2) [66,67], and Asian languages, such as Korean (n=1) [62] and Chinese (n=1) [64]. In contrast, studies focusing on clinical text are proportionally more likely to rely on private data sources [38,66-68,72], highlighting the restricted access associated with clinical records and institutional electronic health data.

Regarding the features used for different tasks (Table 6), word embeddings and molecular embeddings are the most used. Word embeddings are predominantly used for NER (n=23) [24,25,31,38,63-67,76] but are also applied across most other tasks [37,39,50,68-72,74,75], except for toxicity prediction. In this case, LMs are primarily for feature engineering, converting natural language symbols into dense representations. Toxicity prediction tasks, which are mainly addressed in premarket studies, rely primarily on molecular embedding features (n=10) [32-35,41-43,45,46,48], derived using LMs (molecular or protein LMs to be specific). N-gram features rank third in terms of use frequency and are used to a lesser extent than embeddings, often as complementary features. As with word embeddings, n-grams are not used for toxicity prediction, further highlighting the task’s reliance on structured molecular representations rather than textual features.

Despite rapid methodological progress, the reviewed literature reveals that there are several persistent limitations that constrain the reliability, generalizability, and practical utility of LM-based methods for safety prediction and detection across the drug development lifecycle.

Many studies rely on English-language corpora [3,49,58,60], spanning social media benchmarks, patient forums, and biomedical literature, as well as many publicly available ADE corpora used in modeling pipelines. This creates a bias, whereby models trained and evaluated primarily on English might underperform when deployed on non-English narratives, where lexical variation or different naming conventions could alter information extraction performance. Beyond language, geographic bias [32,36,61,64] could emerge because prescribing patterns or drug availability vary across health care systems. Models trained on US data sources may not be applicable in other jurisdictions without adaptation.

Although many postmarket studies extract ADE mentions or compute signals from real-world sources, none validate downstream findings against established postmarketing evidence. For example, several studies have extracted and normalized ADE from social media [60,63,65]. However, none of them report how these findings compare with ADEs validated in phase IV clinical trials. Without robust external validation, it remains difficult to quantify the proportion of signals representing actionable findings rather than noise. ADE detection in postmarket settings is often framed as information extraction, primarily NER [30,31,73] and RE [24,27,68], to structure what is stated in narratives. The goal of these systems is not to establish biomedical causality. Instead, they aim to identify causal attributions as expressed by the author, whether clinician or patient, and to make that information usable for downstream pharmacovigilance workflows. However, translating extracted attributions into actionable safety assessments requires further analysis. Real-world safety evaluation must account for factors such as confounding by indication and differences in target populations. This is essential because the likelihood and severity of ADEs are context-dependent and can vary with determinants such as underlying disease, dose, and treatment duration. This contextuality also highlights a key limitation of many premarket resources and toxicity prediction pipelines. These resources and pipelines often rely on decontextualized representations of compounds and therefore miss patient- and regimen-specific determinants that shape how adverse outcomes manifest in practice.

Across studies, evaluation most commonly centers on F₁-score [24,28,48], precision or recall [34,74,75], and AUROC [33,38,48]. In premarket safety prediction studies, AUROC and accuracy are predominant. While these are useful metrics, they should be interpreted with caution in the presence of class imbalance, which is common in toxicity and ADE datasets. In such settings, AUROC or accuracy may mask poor performance on rare but safety-critical events and should therefore be reported alongside more robust metrics such as balanced accuracy, Matthews correlation coefficient, and F₁-score. Moreover, calibration is rarely assessed, despite being essential when model outputs are used to rank safety risks or trigger alerts. A model can achieve strong discrimination while producing poorly calibrated probabilities, which can lead to inappropriate decision thresholds and misinterpretation of predicted risk. In addition, the literature remains skewed toward postmarket detection, with comparatively few LM applications that integrate into early development decisions. This imbalance restricts the ability of LM methods to support proactive risk mitigation, where earlier detection could reduce attrition and patient harm.

AI, and LMs in particular, is increasingly influencing how ADEs are anticipated and monitored throughout the drug development lifecycle. The recent acceleration in this field is not simply a result of wider adoption of AI. It also reflects a methodological consolidation where LMs provide a common framework for learning from molecular structure [41-43] and human-generated text [3,58,63]. By mapping LM applications from premarket pharmaceutical research through postmarket safety monitoring, this scoping review captures an evolution from static embedding methods [29,65,66] toward contextualized transformer encoders [45,70,72] and, more recently, LLMs [25,30,63].

Across the 49 studies included in this review, which were published between 2015 and October 15, 2025, LM-based applications are more strongly represented in postmarket ADE detection (n=33) than in premarket safety prediction (n=16). Postmarket studies typically implement pharmacovigilance as information extraction tasks, including NER, RE, EL [59,61,66], and document-level classification [39,51,72,74], tasks for which LMs are naturally designed. In contrast, premarket studies most often frame safety as a predictive modeling problem over compound representations, in which LMs act as feature learners that enable toxicity prediction [32,33,46] before synthesis, extensive preclinical testing, or clinical evaluation. Taken together, these 2 aspects illustrate how LMs function as a bridging technology throughout the process by standardizing representation learning across data modalities and enabling safety assessment within a unified methodological family.

With respect to the safety outcomes addressed, organ-specific prediction remains focused on a limited set of endpoints that are both clinically relevant and well supported by the available training data. Heart and liver toxicities predominate among organ-targeted studies, and accordingly, cardiotoxicity and hepatotoxicity emerge as the most modeled endpoints [32,33,41]. Conversely, less frequently modeled organs, such as the brain [44,48], experience a scarcity of high-quality labeled datasets and the experimental complexity required to generate ground truth for these tissues. As a result, methodological progress is currently strongest, where data availability is favorable, while important gaps persist for rarer, complex, or difficult-to-measure toxicities.

The included literature shows a clear methodological progression from static representations in earlier years to contextualized encoders and, more recently, to the recent emergence of LLMs. Despite increased attention to generative systems, most of the included studies remain centered on discriminative, encoder-based architectures, aligning with the predominance of supervised extraction and classification tasks. BERT-family models are the most frequently used contextualized architectures [32,45,76], while word2vec remains common [36,43,61], often within hybrid pipelines that combine learned embeddings with classical ML or DL classifiers [50,66,71]. The continuing dominance of discriminative approaches is consistent with their comparatively lower computational requirements and stable supervised training behavior, as well as the availability of annotated datasets suited for supervised learning [50,65,72]. Furthermore, the pharmaceutical industry’s regulatory framework favors deterministic reliability over generative flexibility. While LLMs offer powerful zero-shot capabilities, their propensity for hallucination poses a safety risk in pharmacovigilance, where a fabricated ADE signal is as dangerous as a missed one. The future dominance of LLMs will likely depend less on their generative fluency and more on the development of guardrails and grounding mechanisms that can satisfy regulatory rigor.

At the same time, the increasing appearance of LLMs, particularly in the most recent portion of the review period, suggests a shift toward approaches that can exploit zero-shot or few-shot capabilities, which could reduce dependence on costly annotation. In the reviewed evidence, however, this generative turn is most represented in postmarket extraction settings rather than spanning the full drug development lifecycle uniformly.

The data ecosystems underlying pre- and postmarket applications differ systematically. Premarket prediction studies largely rely on curated chemical and toxicity resources (eg, SIDER, ClinTox, and CTD) [44,45,49] and are frequently complemented by established knowledge bases providing chemical and biological context (eg, DrugBank, ChEMBL, and PubChem) [44,45,50]. Postmarket detection studies predominantly leverage real-world narrative sources, including social media benchmarks (eg, SMM4H and CADEC) [31,63,76] and clinical text resources (eg, private EHR datasets, MADE, and n2c2) [3,24,72]. These differences in the data are mirrored in the evaluation practices, with postmarket studies typically reporting precision, recall, and F₁-scores [58,60,62], whereas premarket studies more frequently emphasize AUROC and accuracy [32,34,77]. While such metric choices are conventional for the respective task families, they complicate comparisons across pipeline stages and can obscure safety-relevant weaknesses. High entity-level F₁, for example, does not necessarily translate into reliable pharmacovigilance decisions unless downstream signal utility is demonstrated. Similarly, AUROC and accuracy can remain favorable under class imbalance, potentially masking poor performance on rare but clinically significant events. Consequently, the field experiences a metric-utility misalignment. In safety surveillance, the cost of a false negative (missing a fatal ADE) vastly outweighs the cost of a false positive (unnecessary review). Yet, many reviewed studies optimize for balanced metrics like F₁-score rather than prioritizing high-sensitivity configurations (recall>95%) that act as effective safety nets. Future benchmarks must penalize missed (serious) signals more heavily to reflect the operational realities of drug safety.

Beyond evaluation metrics, the reviewed literature reveals several recurring limitations. Linguistic and geographic bias in training data remains common. Downstream validation is often limited, and extracted ADE information is rarely leveraged in deeper analyses, while calibration is also infrequently assessed. Together, these issues constrain the generalizability and practical utility of LM-based pharmacovigilance systems. LM-based methods are appealing because they can support safety analyses across the drug development lifecycle while preserving existing practices. However, their value proposition depends on moving beyond isolated tasks toward demonstrable gains in workflow efficiency and patient safety.

Taken together, the evidence indicates that LM-based methods have evolved from representation learning into a diverse toolkit supporting both premarket safety prediction and postmarket ADE detection. While the field remains anchored in discriminative transformers and supervised training, generative LLMs are emerging as a complementary paradigm that could accelerate adaptation thanks to their zero-shot capabilities. Future progress is likely to depend strongly on robust generalizability and downstream validation. Moreover, there is a clear need for greater methodological convergence between chemical LMs and text-based LMs, particularly through multimodal approaches that incorporate chemical-, patient-, and regimen-level information. Currently, the bridge provided by LMs is methodological rather than functional; premarket models do not learn from postmarket text, and vice versa. A true paradigm shift will occur only when multimodal architectures are trained jointly on molecular structures and safety-related narratives. This would allow a model to analyze a chemical structure and directly generate its potential postmarket safety profile, effectively closing the feedback loop that currently takes years to traverse [23]. In the context of safety prediction, such efforts would help bridge the gap between decontextualized, compound-centric predictions and the context-dependent determinants of ADE risk in real-world scenarios. This suggests that the performance ceiling in toxicity prediction is not algorithmic, but conceptual. By modeling molecules as isolated static entities, current LMs ignore the physiological context (eg, metabolism and genetics) that defines toxicity. Future breakthroughs will require moving from molecule-centric LMs to interaction-centric systems that embed compounds within virtual biological environments.

This review has several limitations. First, the scope was deliberately narrowed to studies that explicitly use LM techniques. While this keeps the research questions we posed tractable, it inevitably underrepresents the large body of preclinical safety work that still relies on nonlinguistic DL (eg, pure graph or image models) and may therefore give the impression that the use of LMs is more mature than it really is [18], or that it is the only approach to apply AI for drug safety. Another limitation lies in the fact that almost all of the papers ultimately included analyze English corpora, with only a handful making use of non-English corpora. This linguistic bias, reinforced by the exclusion of non-English papers, already criticized in earlier pharmacovigilance reviews [16], limits the study’s external validity in markets where social media posts in languages, such as Spanish, Portuguese, Hindi, and Arabic, dominate. This mirrors the selection bias that Chekroud et al [89] have highlighted for efficacy prediction using clinical trials and probably inflates the apparent dominance of English-language, Twitter-based, postmarketing studies. From a methodological point of view, our search strings were compiled using high-level terms (eg, “language model”) and will have missed papers that mention only specific algorithms (eg, “ELMo” and “BERT”) without the umbrella “LM” label.

While the application of LMs to ADE analysis in pharmaceutical research and development is still in its early stages, this scoping review highlights the field’s rapid maturation and considerable potential. Our findings demonstrate that LMs can be integrated across various stages of the drug development lifecycle, from early toxicity prediction in the premarket phase to large-scale ADE detection in postmarket surveillance.

The reviewed studies indicate that recent advances in contextualized LMs and LLMs have led to meaningful improvements in the extraction, representation, and analysis of safety-related information from both molecular and textual data sources. As these methodologies continue to evolve, they show great promise in enhancing drug safety assessment, improving the efficiency of pharmacovigilance systems, and ultimately reducing preventable patient harm and health care costs.

Looking ahead, broader adoption of LLM-based approaches will depend on continued progress in model validation, interpretability, and integration into real-world regulatory and clinical workflows. With sustained methodological refinement and closer alignment with pharmacovigilance practice, LMs are well-positioned to play a transformative role in future drug safety monitoring and decision-making, thereby fostering greater trust and acceptance in therapeutic and regulatory settings.