Collecting and analyzing adverse event monitoring data for high-risk medical devices is a crucial means of ensuring their safety and effectiveness [1]. Currently, medical device regulatory authorities have widely adopted electronic monitoring systems, which not only enhance data collection efficiency and convenience but also lead to a sustained increase in the volume of adverse event reports submitted [2]. Badnjević et al [3] found that despite clear regulatory mandates for postmarket surveillance obligations across countries, issues persist in adverse event reporting, including inconsistent standards, noncomparable data, and fragmented oversight. In China, infant incubators are classified as class III high-risk medical devices. Adverse event monitoring for these products involves interdisciplinary knowledge integration across multiple fields. Challenges include large volumes of adverse event data, predominantly in unstructured and nonstandardized formats [4], as well as significant variation in data quality reported by health care institutions [5]. These factors substantially increase the workload and complexity of analyzing and processing reports. Recent innovations in natural language processing technology offer new possibilities for balancing efficiency and professionalism. Ruseva et al [6] proposed a deep learning model based on the TensorFlow framework, demonstrating strong performance in assessing adverse drug reaction risks and enhancing postmarketing drug surveillance capabilities. Hauben [7] developed artificial intelligence (AI)–based methods to enhance drug-drug interaction detection and prediction. Borjali et al [8] integrated deep learning to accurately and promptly detect adverse medical events from free-text medical narratives.

While traditional deep learning models can perform text classification, they lack long-range contextual associations [9] and causal reasoning capabilities [10], exhibit weak generalization, and demonstrate poor transferability between events [11]. Lu et al [12] found that Transformer models consistently outperformed traditional convolutional neural network models in nearly all medical text classification scenarios, with convolutional neural networks demonstrating acceptable performance only when categories were balanced. Naik et al [13] highlighted that adapting event extractors to new medical domains suffers from covariate shift issues, rendering direct transfer ineffective and necessitating specialized domain adaptation techniques.

Although large language models (LLMs) can provide solutions to core challenges in medical device adverse event monitoring, pretraining foundational models from scratch for vertical domains incurs prohibitively high costs. For instance, first, training a model with tens of billions of parameters requires multiple graphics processing units and months of computational time [14], demanding substantial computational resources and time investment. Second, pretraining often requires datasets in the gigabyte or even terabyte range [15], while vertical domain data are typically strictly confidential [16]. Publicly available data may constitute less than 0.1% of pretraining requirements, and the processes of data collection, cleaning, annotation, and storage involve substantial human and material resources. Finally, pretraining imposes steep technical barriers, demanding both deep algorithmic engineering expertise and the development of large-scale distributed training frameworks [17]—a burden beyond the reach of most small-to-medium teams.

Therefore, this study constructs an infant incubator adverse event analysis model by fine-tuning LLMs. Methods such as low-rank adaptation (LoRA) and infused adapter by inhibiting and amplifying inner activations, that is, (IA)³, require only minor weight updates, preserving general language prior knowledge while incorporating domain-specific expertise [18]. However, for tasks involving the analysis of adverse events related to infant incubators—which are complex, densely populated with specialized terminology, and characterized by imbalanced samples—a single method has inherent limitations. LoRA excels at capturing long-range contextual dependencies and temporal event correlations through low-rank updates, but its global low-rank matrix may lack sufficient feature resolution when handling rare fault descriptions or subtle terminological variations. Meanwhile, (IA)³ enables fine-grained control over the strength of specific features through dynamic scaling of activation values, but it struggles to model long-range semantic relationships across sentences or paragraphs. Neither approach alone can comprehensively address these challenges.

To this end, this study proposes a “dual-adapter joint fine-tuning” architecture. Its novelty lies not in simple superposition but in a hierarchical division of labor and collaboration: the LoRA component focuses on modeling logical chains and long-range dependencies within adverse events, while the (IA)³ component performs local feature enhancement and normalization on terminology and key entities to suppress noise. Coupled through a dynamic weight transfer mechanism, this approach enables the model to achieve deep adaptation to vertical domain knowledge while preserving general medical semantic understanding. Although vertical domains often lack extensive data, Wang et al [19] demonstrated the feasibility of studying small-sample adverse event data in medical devices when investigating knowledge transfer and calibration for small-sample medical signals (epileptic electroencephalography).

High-quality and task-relevant data form the cornerstone for ensuring that fine-tuned models achieve the expected performance, accuracy, and specific behavioral patterns. Data quality directly determines the nuances learned by the model, the biases it may inherit or mitigate, and its generalization capabilities [20]. The data source used in this study comprises nonpublic adverse event data from infant incubators within China’s Medical Device Adverse Event Monitoring System (the details of adverse events are shown in Multimedia Appendix 1). This dataset authentically captures adverse events generated by infant incubator use in medical institutions in recent years, encompassing complex and diverse semantic expressions within the domain. Real-world noise reflects the challenges of practical application scenarios; mild noise can actually compel the model to learn deeper semantic features rather than relying on spurious patterns in the data, thereby enhancing model robustness [21].

It is important to note that even after model fine-tuning, hallucination persists. From a computational theory perspective, all AI models—including current mainstream LLMs—are fundamentally probabilistic mathematical function approximators [22]. These models map inputs to output spaces through complex parameter matrix operations, generating outputs that are essentially responses sampled from high-dimensional probability distributions. Retrieval-augmented generation (RAG) significantly mitigates this issue. It is an AI technique that combines information retrieval technology with language generation models [23]. This approach enhances the model’s capability to handle knowledge-intensive tasks by retrieving relevant information from external knowledge bases and feeding it as prompts to LLMs.

In recent years, the rapid advancement of AI technology has increasingly highlighted the application value of large-scale pretrained models across multiple professional domains. Advanced models such as GPT and Bidirectional Encoder Representations from Transformers (BERT), with their exceptional language comprehension and generation capabilities, have demonstrated significant application potential in the health care sector. Research data indicate that models based on the Transformer architecture possess unique advantages in medical text processing: Kim et al [24] confirmed that domain-adapted BERT variants perform well in predicting appropriate medical departments based on patient symptom descriptions and in answering specialized medical questions. Zhang et al [25] utilized LLMs for medical record generation tasks, saving physicians approximately 0.5-1 hour of work time daily.

Notably, the application scope of such models has expanded beyond text processing into medical devices and regulatory domains. Tang et al [26] highlighted that the global AI medical device industry is experiencing robust growth, while the urgent need for regulatory oversight is becoming increasingly apparent—a trend expected to intensify. For instance, Kim et al [27] developed a deep learning predictive model to rapidly and accurately detect mechanical ventilation requirements in neonatal intensive care units using electronic health records; Matheny et al [28] systematically explored how applying LLMs to the US Food and Drug Administration’s (FDA) medical product safety surveillance system enhances the identification and assessment of adverse drug events.

Regarding fine-tuning medical LLMs, multiple studies have explored application methods. For instance, Jia et al [29] employed LoRA for instruction fine-tuning of pretrained LLMs, ultimately enhancing the automated generation of children’s health science resource labels. Abbasian et al [30] utilized electronic health records from patients with diabetes for P-tuning and LoRA fine-tuning of large models, opening new avenues for diabetes management and improving therapeutic efficacy. Integrating large models with RAG has also become a research hotspot. Hu et al [31] introduced a pioneering personalized dementia care approach by integrating GPT-4, fine-tuned and RAG-enabled, with a vector database. Kulshreshtha et al [32] enhanced a medical chatbot based on the Llama source model using RAG methods to generate reliable, contextually relevant responses, thereby improving clinical judgment and reducing health care workers’ workload.

The adverse event analysis model AE2IN proposed in this study for infant incubators comprises 2 core modules: a data preprocessing layer and a knowledge-enhanced analysis layer. The specific framework is illustrated in Figure 1.

At the data preprocessing layer, the system first standardizes input adverse event reports for infant incubators through data filtering, cleaning, and augmentation. The data standardization, cleaning, and augmentation processes for this study’s training dataset leveraged GPT-4 as a teacher model for auxiliary processing. Through knowledge distillation in a teacher-student paradigm, it provided high-quality training samples for subsequent domain-specific fine-tuning of the Qwen2_7B model. Standardization during conventional inference fully utilizes specialized medical dictionaries and regular expression rules to ensure accurate recognition of diverse terminology. The preprocessed structured data are subsequently fed into the knowledge-enhanced analysis layer.

The knowledge-enhanced analysis layer serves as the system’s core, employing a dual-path parallel processing mechanism. On the one hand, supervised comparison-based semantic optimization fine-tunes the embedded model FINBGE to vectorize queries and retrieve the most relevant clause content from the medical device regulatory knowledge base. On the other hand, the Qwen2_7B model, jointly fine-tuned via dual adapters, performs deep semantic understanding of the input text. Notably, this model innovatively combines 2 parameter-efficient fine-tuning techniques: LoRA and (IA)³.

When analyzing adverse events involving infant incubators, core elements are of paramount importance. Yan and Huifang [34] proposed analyzing both the manifestations and root causes within adverse event data for infant incubators. Kaur et al [35] suggested describing the implemented control measures. Additionally, according to the writing requirements of the guidelines issued by the National Medical Products Administration, adverse event reports typically require information such as location, time, source, injury manifestation, device malfunction, cause analysis, and specific control measures. Accordingly, we summarize the core and secondary elements of infant incubator adverse events. The core elements are the “occurrence date” of the adverse event, the “abnormal condition” of the infant incubator, and the “follow-up measures” for risk control. Secondary elements include the “cause of failure” and the “risk triggered” by the infant incubator. Beyond these core and secondary elements, any additional information captured from the original text is categorized as supplementary notes.

Therefore, during testing experiments, in addition to employing general evaluation metrics, we have developed specialized evaluation indicators for adverse events in infant incubators: the element recall rate and the information density index of adverse events with correction factor (IDIAE). The formulas are as follows:

To efficiently align the model with the desired training outcomes, this study designs 3 prompts for different scenarios, as shown in Figures 7-9. Figure 7 is used to assist in generating a suitable fine-tuning dataset. Figure 8 presents the system prompt embedded in the inference stage, aiming to standardize the model output. The retrieval prompt integration employs a structured constraint template, as shown in Figure 9. System instructions enforce that the model must base its responses solely on retrieved content, annotating each sentence with a source identifier [Ref-N] after interpretation. Each retrieved chunk is organized according to a fixed format. The prompt template constrains model behavior through 3 core rules:

In typical small-sample medical data analysis tasks, r=32 has been validated as an effective LoRA range [36]. Before experimentation, we initially favored selecting r=32. However, to rigorously validate the impact of rank-learning rate combinations on the performance of the infant incubator adverse event question-answering task, we conducted a 4 × 4 grid scan while fixing all other hyperparameters. The resulting F₁-scores are visualized as a heatmap, as shown in Figure 10. The heatmap shows that the F₁-score peaks at 0.823 at rank=32 and lr=1 × 10^–4, then decreases toward the surrounding edges, exhibiting an overall bell-shaped “medium-high-medium” distribution. This phenomenon occurs because when the rank is too low, the low-rank matrix lacks expressive power and cannot fully capture the long-range dependencies and medical terminology details in event descriptions; when the rank is too high, the number of parameters increases, which can lead to overfitting on domain data and weaken generalization. Regarding the learning rate, 5 × 10^–5 converges too slowly, whereas 5 × 10^–4 has an excessively large step size, making it difficult for the model to stabilize at the optimal weights. By contrast, 1 × 10^–4 strikes a balance between convergence speed and stability. Therefore, the experiment ultimately adopted the combination of rank=32 and lr=1 × 10^–4.

To investigate the impact of batch size on training stability and performance, we conducted experiments while fixing the other hyperparameters. As shown in Figure 11A, when batch size is 1, the loss curve oscillates notably and converges slowly due to high gradient update noise; a batch size of 2 (the configuration selected for this study) achieves an optimal balance between training stability and convergence speed, with a smoothly declining loss curve and the lowest final convergence value; increasing the batch size to 4 or 8 yields a more stable training process but slightly slower convergence; a batch size of 16 fails to iterate sufficiently due to graphics processing unit memory constraints. Figure 11B further quantifies the final performance metrics across different batch sizes: a batch size of 2 achieves peak values for F₁-score, accuracy, and IDIAE, showing average improvements of 4.0% and 3.4%, compared with batch sizes of 1 and 8, respectively. Therefore, the final experimental configuration adopts a batch size of 2.

In supervised contrastive semantic optimization, the temperature coefficient τ controls the sharpness of the similarity distribution. To achieve optimal results, we fixed the other parameters and conducted experiments on the temperature coefficient. As shown in Figure 12, when τ=0.05, the similarity distribution becomes excessively sharp, causing the loss curve to oscillate and converge slowly. At τ=0.15 (the value selected for this study), the loss curve declines smoothly and rapidly to a stable region. When τ increases beyond 0.3, the similarity distribution becomes overly smooth, notably slowing loss convergence. Therefore, τ=0.15 is determined to be the optimal configuration for medical device regulatory retrieval tasks.

Our data originate from China’s medical device regulatory authorities. The data consist of medical device adverse event reports (similar to the US FDA’s Manufacturer and User Facility Device Experience [MAUDE] database). The content primarily includes failure information recorded during device use. These data contain no patient information; therefore, formal ethics approval is not required. This project was jointly completed with regulatory agencies under government-funded research initiatives. All data were authorized, and the research complies with relevant regulations.

This large-model experimental dataset comprises general-domain data and domain-specific data. The general-domain data originate from the PediaBench dataset publicly released by the State Key Laboratory of Public Big Data at the School of Computer Science and Technology, Guizhou University, China. It contains 1565 question-and-answer entries covering common pediatric diseases such as neonatal disorders, pediatric infectious diseases, pediatric respiratory diseases, and pediatric primary care and developmental behavioral disorders. Domain-specific data originate from private adverse event data on infant incubators within China’s Key Monitoring Information System for Medical Device Adverse Events. After manual and GPT-4 screening, data cleaning, and augmentation, combined with prompt engineering, the data were processed into a specialized question-and-answer dataset on infant incubator adverse events, totaling 2530 entries (Table 1).

We split the data at the adverse-event report level: all question-and-answer pairs derived from the same report were assigned entirely to either the training, validation, or test set, ensuring that no report appears in more than one set. This experimental dataset allocates 60% (2457/4095) to the training set, 30% (1229/4095) to the test set, and 10% (409/4095) to the validation set.

The knowledge base data originate from regulatory documents issued by the China National Medical Products Administration. We selected administrative regulations and departmental rules to form the regulatory provisions knowledge base, comprising a total of 29 documents. The fine-tuning dataset for the embedding model is derived from these regulatory documents. We utilized documents from this knowledge base and, combined with prompt engineering, leveraged GPT-4 to generate fine-tuning datasets. This dataset comprises 1488 entries, with 60% (893/1488) allocated to the training set, 30% (446/1488) to the test set, and 10% (149/1488) to the validation set (Table 2).

To mitigate the “student-teacher” loop bias introduced by large-model rewriting, we collaborated with the Henan Drug Administration to implement a manual quality control process for all data generated by large models. First, we established a 3-tier annotation standard (perfect equivalence/minor drift/significant drift) and trained annotators (κ≥0.78). Next, data were grouped into batches of 100 items for double-blind annotation by pairs of annotators, with a third party serving as the arbitrator. Only when the “significant drift” rate was controlled at ≤5% were the data released. Finally, senior experts centrally reviewed and refined the “significant drift” data.

This work selected Qwen2_7B as the source model and employed a dual-adapter joint fine-tuning approach for training, with specific configurations detailed in Tables 3-5.

This study selected BGE-Large-ZH-V1.5 as the embedding source model. Here, BGE-Large-ZH-V1.5 was compared with M3E-Large, Text2Vec-Large, and Luotuo on the Massive Text Embedding Benchmark (MTEB) and the Chinese Massive Text Embedding Benchmark (C-MTEB). The embedding dimension was unified to 1024, and the evaluation metrics selected were retrieval, semantic textual similarity, pairwise classification, and classification.

Table 6 compares BGE-Large-ZH-V1.5 with mainstream models, demonstrating that it achieves leading performance on both MTEB and C-MTEB. Its high recall score of 70.46 indicates that it is particularly well-suited as a foundation for RAG systems, demonstrating a superior ability to accurately match specialized terminology within knowledge bases compared with other models.

For large models, this paper compares base-model performance by evaluating Qwen2_7B, Llama3_8B, and GLM4_9B across 8 metrics. The results are shown in Table 7.

Table 7 shows that the Qwen2 model has stronger overall performance than the other 2 models. Qwen2 performs well on the Massive Multitask Language Understanding (MMLU) medical subset and C-Eval medical questions, indicating that the model is stable in diagnostic reasoning and clinical terminology comprehension, but exhibits slight errors in complex calculation questions. Meanwhile, the fine-tuned AE2IN model shows score decreases ranging from 2.1 to 2.7 on the MMLU medical subset and C-Eval medical tasks. Because of the incorporation of PediaBench data during fine-tuning, its scores slightly surpass those of the base model by 0.4 points. By contrast, models fine-tuned solely on domain-specific datasets experienced score declines ranging from 5.4 to 8.0 on the MMLU medical subset and C-Eval medical tasks, along with a 6.2-point drop on PediaBench. These comparative results validate that AE2IN effectively mitigates catastrophic forgetting.

We selected 5 models for comparative testing with the fine-tuned model. The results are shown in Table 8.

Through systematic quantitative evaluation, the AE2IN model demonstrates notable advantages across all key performance indicators in the domain of infant incubator adverse events. In terms of text generation quality, AE2IN attained a BLEU-4 score of 0.215, reflecting a 56.9% improvement relative to the baseline model and a 32.7% improvement over the baseline equipped with the same RAG framework. Its Recall-Oriented Understudy for Gisting Evaluation - Longest Common Subsequence (ROUGE-L) score reached 0.484, corresponding to a 53.7% increase. For core element analysis of infant incubator adverse events, the element recall rate reached 0.815, representing a 17.9% improvement over the baseline model and a 14.0% improvement over the baseline integrated with the same RAG framework. In the experiments, the dual-adapter architecture exhibited strong causal reasoning capacity when processing complex alarm system failure events.

To conduct a more comprehensive analysis of the AE2IN model, we also selected cases in which accuracy, element recall, and IDIAE were all below 0.5. We found that misjudgment of core elements directly affected completeness conclusions and notably impacted both accuracy and IDIAE values. In failed cases, we observed that for rare fault descriptions—such as the onomatopoeic “pushing clack-clack sound”—low frequency and susceptibility to semantic integrity disruption caused the inferred Query-Key similarity to be drowned out by the attention distribution of primary terms after softmax. This prevented the activation of corresponding neurons during value vector weighting. Furthermore, the scaling coefficient update for (IA)³ relies on batch statistics, leading to inconsistent weighting across cases. When encountering such descriptions, the update process may become stuck at a saddle point, failing to amplify this feature.

We continuously record training and validation loss, along with key evaluation metrics, to determine in real time whether the model is in a state of normal convergence, overfitting, or underfitting. As shown in Figure 13, validation loss and training loss decrease smoothly and synchronously over the first 600 steps, maintaining a small difference of less than 0.15, indicating that the learning rate of 1 × 10^–4 effectively balances model capacity and regularization. BLEU-4 increases monotonically from 0.07 to 0.22, only 0.005 away from the final test set score of 0.215, confirming that the validation and test sets are identically distributed and that the model has fully converged. Feature recall also increases from 0.60 to 0.815 over the first 600 steps, rising in parallel with BLEU-4 without intersecting, indicating that the model improves fluency without sacrificing its ability to extract key information. All 3 metrics plateau simultaneously, demonstrating stable convergence without the need for early stopping or secondary parameter tuning. This also indicates that the final test score is not a random peak but the result of stable convergence.

As shown in Figure 14, when determining the completeness of adverse events related to infant incubators, AE2IN directly extracts core elements—incident date, nature of the abnormality, and subsequent corrective actions—while supplementing them with details such as operator information, risk warnings, and injury status. This approach concisely highlights essential information.

However, compared with other models, AE2IN’s responses tend to be more concise. To investigate whether this brevity affects safety, a supplementary risk recall assessment experiment was conducted to quantify the quality of model outputs from a safety perspective. First, a core safety item list was established through expert consultation and alignment with the “Measures for the Administration of Medical Device Adverse Event Monitoring and Re-evaluation” and the national standard “GB 9706.219-2021.” This list encompasses adverse event phenomenon identification, safety risk identification, immediate patient safety measures, equipment disposal measures, adverse event reporting measures, and risk cause localization. Next, 100 high-quality data entries, each containing core safety items, were extracted and annotated, and the process was repeated 5 times. The results of the risk recall experiment are shown in Table 9.

AE2IN demonstrates the highest risk recall rate, achieving an improvement of approximately 15% over other models. Although some data may be underreported due to rare fault descriptions, its overall risk recall rate confirms that AE2IN effectively maximizes risk extraction. By outputting structured information that highlights core details, it helps ensure safety.

We used the AE2IN model with the same RAG framework and different embedding models for regulatory question answering. The experiments used a regulatory clause validation set to compare the performance of the fine-tuned embedding models. The comparative results are shown in Table 10.

The experimental results in Table 11 show that the AE2IN model, when equipped with different embedding models, exhibits a clear performance gradient in the regulatory question-answering task. The FINBGE model leads by a notable margin, achieving the highest levels across the 3 core metrics—accuracy, recall, and F₁-score—compared with the source model BGE-Large-ZH-V1.5. This result validates the effectiveness of the supervised contrastive semantic optimization fine-tuning strategy, which successfully narrows the vector distance between the question and the correct answer. Furthermore, comparison with common models reveals that Llama3_8B achieves the lowest scores, likely because the test dataset consists entirely of Chinese regulatory question-answer pairs. The best-performing general model, GLM4_9B, achieves scores of 0.727, 0.671, and 0.698 in accuracy, recall, and F₁-score, respectively, but still lags behind AE2IN. This demonstrates the necessity of domain adaptation and the RAG architecture, which can mitigate model hallucinations, address knowledge timeliness, and improve accuracy.

Similarly, to conduct a more comprehensive analysis of the system model, we selected cases in which regulatory question-answering accuracy was below 0.5 for further study. We found that hallucinations still occurred—for instance, when the model should have referenced article 23 of the “Administrative Measures for the Monitoring and Re-evaluation of Adverse Events of Medical Devices,” it instead cited article 24. This occurs because clauses with high similarity but different article numbers exhibit only minor differences in cosine similarity, leading to the retrieval of incorrect clauses and subsequently triggering hallucinations.

To validate the contributions of each component in the dual-adapter joint fine-tuning architecture, this study designed a systematic ablation experiment, maintaining the same testing environment and hyperparameter settings as previously described. The experiment employed a controlled variable approach, sequentially removing key components and observing changes in model performance.

Table 11 shows that the complete model improves feature recall by an average of 9.25% compared with the single-adapter solution, validating the complementarity of low-rank updates and feature scaling in equation (1).

When the LoRA component is removed, BLEU-4 and ROUGE-L decrease notably. This is mainly because LoRA optimizes the long-range dependency modeling capability of the Transformer layer through low-rank decomposition. Its absence makes it difficult for the model to effectively capture the logic in infant incubator failure events. At the same time, the feature recall rate decreases by 10.0%, further validating LoRA’s key role in structured information extraction—its trainable parameters are concentrated in the ΔW matrix of the attention layer, enabling more accurate localization of core elements in fault descriptions.

When the (IA)³ component is removed, the IDIAE index decreases notably, due to the weakening of the dynamic feature-scaling function achieved by (IA)³ through the gating mechanism. Specifically, the key-value intervention vector in (IA)³ suppresses the activation of irrelevant features while enhancing the response strength of neurons associated with medical terms.

Following full fine-tuning, metrics exhibited varying degrees of decline compared with AE2IN or single-method fine-tuning. This phenomenon stems from the inevitable overfitting of the 7-billion-parameter model on finite, small samples, validating the necessity of structured parameter fine-tuning for efficiency in small-sample scenarios.

Observation of Table 12 data reveals that, in long-text scenarios, the recall rate of the LoRA configuration alone exhibits a lower decay rate than (IA)³, demonstrating that LoRA’s low-rank updates effectively capture long-range semantic associations across clauses. Figure 15 further shows that the attention weights in the LoRA branch decay by 16% for long-distance word pairs, representing a 14-percentage-point reduction compared with the baseline. This indicates that LoRA effectively enhances the interaction strength of distant semantic units. (IA)³ can boost weights over short distances, but long-range dependencies are easily lost, leading to reduced recall rates. This also explains why, under dual-adapter collaboration, the final performance in the infant incubator adverse-event domain improved by approximately 14%, further demonstrating that the 2 adapters can complement each other across different distance scales.

This study proposes AE2IN, a large-model framework that integrates RAG with dual-adapter joint fine-tuning for the intelligent analysis of adverse events in infant incubators. Experimental results demonstrate that this framework significantly outperforms baseline models across key metrics, including text generation quality, element recall rate, and regulatory question-and-answer accuracy. Under the same experimental conditions, the element recall rate reaches 0.815, the accuracy of infant incubator adverse event analysis is 0.898, and the accuracy in regulatory clause question-answering tasks attains 0.938. The dual-adapter architecture, that is, LoRA + (IA)³, effectively infuses domain knowledge while preserving model generalization, thereby mitigating catastrophic forgetting. The RAG module, combined with the FINBGE embedding model optimized through supervised contrastive semantic learning, significantly enhances knowledge retrieval accuracy and reduces hallucination.

Compared with traditional deep learning models, AE2IN demonstrates superior contextual modeling and causal reasoning capabilities, making it particularly well-suited for complex tasks involving multiple intertwined factors and specialized terminology, such as adverse events in infant incubators. Compared with single fine-tuning approaches, the dual-adapter architecture proposed in this paper achieves a better balance between parameter efficiency and task adaptability. Compared with existing RAG medical applications, AE2IN further introduces a domain-specific embedding model fine-tuning mechanism, enhancing the matching accuracy of Chinese regulatory clauses. When benchmarked against general models such as Qwen and Llama, AE2IN demonstrates higher professional consistency and stronger element-extraction capabilities in specialized tasks, particularly excelling in structured output and terminology standardization.

First, the dataset size is limited. The infant incubator adverse-event dataset used in this study comprises 2530 entries. Despite undergoing joint cleaning and enhancement by human experts and GPT-4, the dataset remains insufficient for rare fault types or complex scenarios. To mitigate this issue, we introduced PediaBench general pediatric data for mixed training, employing a 3:1 domain-to-general data sampling ratio to enhance generalization. Future efforts could expand multicenter, multiyear data through collaborations with additional medical institutions to further improve model robustness. Second, the regulatory knowledge base updates lag. The regulatory knowledge base used in this study cannot guarantee real-time coverage of the latest policy revisions. We partially mitigated this knowledge lag by improving the embedding model’s semantic understanding of clauses through supervised contrastive fine-tuning. Future work should consider establishing a dynamic regulatory update mechanism, such as periodically crawling the National Medical Products Administration website or integrating official application programming interfaces to enable automatic knowledge base synchronization. Third, prospective clinical validation was not conducted. This retrospective study did not verify the model’s usability and safety within actual clinical workflows. We collaborated with the Henan Drug Administration to implement manual quality control, maintaining a “significant drift” rate ≤5%. However, there remains a lack of assessment regarding clinicians’ adoption of model recommendations and the impact of false positives. Future prospective studies should be conducted, inviting hospital equipment departments or adverse-event monitoring personnel to participate in pilot programs and collecting user feedback for iterative optimization.

Current research is limited to adverse events involving infant incubators. Future studies may further explore data-level approaches, such as constructing larger-scale, multicenter, multilingual adverse-event databases covering more high-risk medical device types to enhance model generalizability. Then, at the fine-tuning method level, future work could draw inspiration from distributed entropy regularization [37] to further optimize the learning objective of scaling factors, thereby improving sensitivity to uncommon failure patterns in the training data. At the model level, multimodal fusion could be explored by incorporating visual encoders to process device alarm screenshots or failure photographs, enabling more comprehensive fault identification. Finally, at the system level, an integrated regulatory support system should be developed. This system would integrate the AE2IN model, regulatory retrieval, report generation, and manual review workflows to support multiparty collaboration among hospital equipment departments, regulatory agencies, and manufacturers, thereby advancing the implementation of intelligent regulation.

The safety management of medical devices encompasses all stages of the product life cycle, including research and development, design, manufacturing, clinical application, maintenance, and product recalls, all of which require strict quality control measures. By establishing standardized protocols, refining risk-prevention mechanisms, conducting continuous monitoring, and enhancing professional training, the failure rate of medical devices can be significantly reduced, thereby effectively safeguarding the safety interests of both medical professionals and patients. To advance the management of adverse events related to medical devices and comprehensively support professionals in this field—such as surveillance personnel—this study proposes a large-model solution that integrates RAG technology with dual-adapter fine-tuning. This approach provides an efficient and reliable technical pathway for analyzing adverse events involving infant incubators.