Medical Texts Research Articles

Viability of Open Large Language Models for Clinical Documentation in German Health Care: Real-World Model Evaluation Study.

The use of large language models (LLMs) as writing assistance for medical professionals is a promising approach to reduce the time required for documentation, but there may be practical, ethical, and legal challenges in many jurisdictions complicating the use of the most powerful commercial LLM solutions. In this study, we assessed the feasibility of using nonproprietary LLMs of the GPT variety as writing assistance for medical professionals in an on-premise setting with restricted compute resources, generating German medical text. We trained four 7-billion-parameter models with 3 different architectures for our task and evaluated their performance using a powerful commercial LLM, namely Anthropic's Claude-v2, as a rater. Based on this, we selected the best-performing model and evaluated its practical usability with 2 independent human raters on real-world data. In the automated evaluation with Claude-v2, BLOOM-CLP-German, a model trained from scratch on the German text, achieved the best results. In the manual evaluation by human experts, 95 (93.1%) of the 102 reports generated by that model were evaluated as usable as is or with only minor changes by both human raters. The results show that even with restricted compute resources, it is possible to generate medical texts that are suitable for documentation in routine clinical practice. However, the target language should be considered in the model selection when processing non-English text.

The Promising Role of Artificial Intelligence in Navigating Lung Cancer Prognosis

Incorporating AI in lung cancer management is a disruptive innovation that has improved diagnosis accuracy, prognosis prediction and treatment modalities. In this literature review, we seek to identify the role of artificial intelligence (AI) and machine learning (ML) in lung cancer detection, diagnosis and treatment between 2010 and 2023. A total of 55 studies were selected systematically from databases such as IEEE Xplore, Scopus and PubMed via a PRISMA-based approach. The analysis reveals that artificial intelligence (AI) techniques, specifically convolutional neural networks (CNNs) and natural language processing (NLP), highly improve the precision of initial detection and imaging of lung cancer. Also, CNN distinguishes between benign and malignant nodules, thus aiding early diagnosis and reducing unnecessary biopsies. On the other hand, NLP is utilized to extract relevant clinical information from electronic health records and unstructured medical texts, thereby enhancing the understanding of patient histories and improving treatment planning. Sensitive and specific scores usually higher than standard techniques characterize these technologies. Results show that traditional statistical approaches couldn’t match AI models whose predictive accuracies are outstanding while providing better care to patients through personalised treatment plans. Furthermore, multi-omics data analysis for personalised treatment planning and clinical decision-making optimisation via Clinical Decision Support Systems (CDSS) powered by AI are some ways artificial intelligence has exhibited its potential in this area. Given this, future studies should aim to fine-tune AI algorithms, improve data integration, and address ethical issues promoting responsible use of AI technologies in clinical practice settings. Despite these advances, data quality, model interpretability, and integration into clinical workflows persist. This review demonstrates the demand for continued research and collaboration from different disciplines so that the complete possibilities of AI in fighting lung cancer may be realised.

A vision-language foundation model for the generation of realistic chest X-ray images.

The paucity of high-quality medical imaging datasets could be mitigated by machine learning models that generate compositionally diverse images that faithfully represent medical concepts and pathologies. However, large vision-language models are trained on natural images, and the diversity distribution of the generated images substantially differs from that of medical images. Moreover, medical language involves specific and semantically rich vocabulary. Here we describe a domain-adaptation strategy for large vision-language models that overcomes distributional shifts. Specifically, by leveraging publicly available datasets of chest X-ray images and the corresponding radiology reports, we adapted a latent diffusion model pre-trained on pairs of natural images and text descriptors to generate diverse and visually plausible synthetic chest X-ray images (as confirmed by board-certified radiologists) whose appearance can be controlled with free-form medical text prompts. The domain-adaptation strategy for the text-conditioned synthesis of medical images can be used to augment training datasets and is a viable alternative to the sharing of real medical images for model training and fine-tuning.

Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review.

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as "black boxes." Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process. In this paper, we present a scoping review exploring the application of different XAI techniques in medical text classification, identifying two main types: model-specific and model-agnostic methods. Despite some positive feedback from developers, formal evaluations with medical end users of these techniques remain limited. The review highlights the necessity for further research in XAI to enhance trust and transparency in AI-driven decision-making processes in healthcare.

Evaluation of Medical Subject Headings assignment in simulated patient articles.

To evaluate human-based Medical Subject Headings (MeSH) allocation in articles about 'patient simulation'-a technique that mimics real-life patient scenarios with controlled patient responses. A validation set of articles indexed before the Medical Text Indexer-Auto implementation (in 2019) was created with 150 combinations potentially referring to 'patient simulation'. Articles were classified into four categories of simulation studies. Allocation of seven MeSH terms (Simulation Training, Patient Simulation, High Fidelity Simulation Training, Computer Simulation, Patient-Specific Modelling, Virtual Reality, and Virtual Reality Exposure Therapy) was investigated. Accuracy metrics (sensitivity, precision, or positive predictive value) were calculated for each category of studies. A set of 7213 articles was obtained from 53 different word combinations, with 2634 excluded as irrelevant. 'Simulated patient' and 'standardized/standardized patient' were the most used terms. The 4579 included articles, published in 1044 different journals, were classified into: 'Machine/Automation' (8.6%), 'Education' (75.9%) and 'Practice audit' (11.4%); 4.1% were 'Unclear'. Articles were indexed with a median of 10 MeSH (IQR 8-13); however, 45.5% were not indexed with any of the seven MeSH terms. Patient Simulation was the most prevalent MeSH (24.0%). Automation articles were more associated with Computer Simulation MeSH (sensitivity = 54.5%; precision = 25.1%), while Education articles were associated with Patient Simulation MeSH (sensitivity = 40.2%; precision = 80.9%). Practice audit articles were also polarized to Patient Simulation MeSH (sensitivity = 34.6%; precision = 10.5%). Inconsistent use of free-text words related to patient simulation was observed, as well as inaccuracies in human-based MeSH assignments. These limitations can compromise relevant literature retrieval to support evidence synthesis exercises.

Evaluation of Generative Language Models in Personalizing Medical Information: Instrument Validation Study.

Although uncertainties exist regarding implementation, artificial intelligence-driven generative language models (GLMs) have enormous potential in medicine. Deployment of GLMs could improve patient comprehension of clinical texts and improve low health literacy. The goal of this study is to evaluate the potential of ChatGPT-3.5 and GPT-4 to tailor the complexity of medical information to patient-specific input education level, which is crucial if it is to serve as a tool in addressing low health literacy. Input templates related to 2 prevalent chronic diseases-type II diabetes and hypertension-were designed. Each clinical vignette was adjusted for hypothetical patient education levels to evaluate output personalization. To assess the success of a GLM (GPT-3.5 and GPT-4) in tailoring output writing, the readability of pre- and posttransformation outputs were quantified using the Flesch reading ease score (FKRE) and the Flesch-Kincaid grade level (FKGL). Responses (n=80) were generated using GPT-3.5 and GPT-4 across 2 clinical vignettes. For GPT-3.5, FKRE means were 57.75 (SD 4.75), 51.28 (SD 5.14), 32.28 (SD 4.52), and 28.31 (SD 5.22) for 6th grade, 8th grade, high school, and bachelor's, respectively; FKGL mean scores were 9.08 (SD 0.90), 10.27 (SD 1.06), 13.4 (SD 0.80), and 13.74 (SD 1.18). GPT-3.5 only aligned with the prespecified education levels at the bachelor's degree. Conversely, GPT-4's FKRE mean scores were 74.54 (SD 2.6), 71.25 (SD 4.96), 47.61 (SD 6.13), and 13.71 (SD 5.77), with FKGL mean scores of 6.3 (SD 0.73), 6.7 (SD 1.11), 11.09 (SD 1.26), and 17.03 (SD 1.11) for the same respective education levels. GPT-4 met the target readability for all groups except the 6th-grade FKRE average. Both GLMs produced outputs with statistically significant differences (P<.001; 8th grade P<.001; high school P<.001; bachelors P=.003; FKGL: 6th grade P=.001; 8th grade P<.001; high school P<.001; bachelors P<.001) between mean FKRE and FKGL across input education levels. GLMs can change the structure and readability of medical text outputs according to input-specified education. However, GLMs categorize input education designation into 3 broad tiers of output readability: easy (6th and 8th grade), medium (high school), and difficult (bachelor's degree). This is the first result to suggest that there are broader boundaries in the success of GLMs in output text simplification. Future research must establish how GLMs can reliably personalize medical texts to prespecified education levels to enable a broader impact on health care literacy.

A Novel Rational Medicine Use System Based on Domain Knowledge Graph

Medication errors, which could often be detected in advance, are a significant cause of patient deaths each year, highlighting the critical importance of medication safety. The rapid advancement of data analysis technologies has made intelligent medication assistance applications possible, and these applications rely heavily on medical knowledge graphs. However, current knowledge graph construction techniques are predominantly focused on general domains, leaving a gap in specialized fields, particularly in the medical domain for medication assistance. The specialized nature of medical knowledge and the distinct distribution of vocabulary between general and biomedical texts pose challenges. Applying general natural language processing techniques directly to the medical domain often results in lower accuracy due to the inadequate utilization of contextual semantics and entity information. To address these issues and enhance knowledge graph production, this paper proposes an optimized model for named entity recognition and relationship extraction in the Chinese medical domain. Key innovations include utilizing Medical Bidirectional Encoder Representations from Transformers (MCBERT) for character-level embeddings pre-trained on Chinese biomedical corpora, employing Bi-directional Gated Recurrent Unit (BiGRU) networks for extracting enriched contextual features, integrating a Conditional Random Field (CRF) layer for optimal label sequence output, using the Piecewise Convolutional Neural Network (PCNN) to capture comprehensive semantic information and fusing it with entity features for better classification accuracy, and implementing a microservices architecture for the medication assistance review system. These enhancements significantly improve the accuracy of entity relationship classification in Chinese medical texts. The model achieved good performance in recognizing most entity types, with an accuracy of 88.3%, a recall rate of 85.8%, and an F1 score of 87.0%. In the relationship extraction stage, the accuracy reached 85.7%, the recall rate 82.5%, and the F1 score 84.0%.

Masketeer: An Ensemble-Based Pseudonymization Tool with Entity Recognition for German Unstructured Medical Free Text

Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With the Masketeer algorithm, we present an effective tool to de-identify German medical text. Methods: We used an ensemble of different masking classes to remove references to identifiable data from over 35,000 clinical notes in accordance with the HIPAA Safe Harbor Guidelines. To retain additional context for readers, we implemented an entity recognition scheme and corpus-wide pseudonymization. Results: The algorithm performed with a sensitivity of 0.943 and specificity of 0.933. Further performance analyses showed linear runtime complexity (O(n)) with both increasing text length and corpus size. Conclusions: In the future, large language models will likely be able to de-identify medical free text more effectively and thoroughly than handcrafted rules. However, such gold-standard de-identification tools based on large language models are yet to emerge. In the current absence of such, we hope to provide best practices for a robust rule-based algorithm designed with expert domain knowledge.

Pre-1950s Medicalisation of Sleep

Many social studies emphasize that sleep medicalisation has emerged in the 1950s, with the rise of somnology. In this paper, I argue that the medicalisation of sleep is rather selective and incomplete. Building on the remark of one of the founding fathers of the sociology of sleep, Simon Williams’(2005), that if we revisited the nineteenth-century medical texts, we would notice evidence of early, pre-1950s medicalisation of sleep, I analyzed nineteenth-century public health policies and documents in Romania and found evidence that, during the nineteenth century and the first half of the twentieth century, sleep was, de facto, medicalised due to the general trend to medicalise the body. I outline two dimensions of this pre-1950s medicalization of sleep: the rationalization and the hygienisation of sleep.

MedNER: A Service-Oriented Framework for Chinese Medical Named-Entity Recognition with Real-World Application

Named-entity recognition (NER) is a crucial task in natural language processing, especially for extracting meaningful information from unstructured text data. In the healthcare domain, accurate NER can significantly enhance patient care by enabling efficient extraction and analysis of clinical information. This paper presents MedNER, a novel service-oriented framework designed specifically for medical NER in Chinese medical texts. MedNER leverages advanced deep learning techniques and domain-specific linguistic resources to achieve good performance in identifying diabetes-related entities such as symptoms, tests, and drugs. The framework integrates seamlessly with real-world healthcare systems, offering scalable and efficient solutions for processing large volumes of clinical data. This paper provides an in-depth discussion on the architecture and implementation of MedNER, featuring the concept of Deep Learning as a Service (DLaaS). A prototype has encapsulated BiLSTM-CRF and BERT-BiLSTM-CRF models into the core service, demonstrating its flexibility, usability, and effectiveness in addressing the unique challenges of Chinese medical text processing.

Functions of idioms in English as lingua franca: An appraisal system account

AbstractIdiomatic expressions are not just neutral equivalents of their literal counterparts, but exert some sort of evaluative force. This study employed the appraisal system (AS) to explore the functions of idiomatic expressions and their variations in English as a lingua franca context, and to compare different academic domains in terms of use of evaluative language in the form of idiomatic expressions. To this end, the corpus of English as a lingua franca in academic settings and the Vienna–Oxford International Corpus of English were searched for idiom tokens through whole‐phrase and keyword search. The idioms and their variations were classified based on the AS. The findings indicated that social and behavioral scientific texts included higher proportions of evaluative idiomatic expressions whereas medical and natural scientific texts generated lower percentages of appraisal idioms. There was a stronger tendency in both corpora toward using idioms as appreciation as compared to judgment and affect. Given the employed methodology, the AS could not be applied to some idioms for their compositionality nature.

Joint extraction of Chinese medical entities and relations based on RoBERTa and single-module global pointer

BackgroundMost Chinese joint entity and relation extraction tasks in medicine involve numerous nested entities, overlapping relations, and other challenging extraction issues. In response to these problems, some traditional methods decompose the joint extraction task into multiple steps or multiple modules, resulting in local dependency in the meantime.MethodsTo alleviate this issue, we propose a joint extraction model of Chinese medical entities and relations based on RoBERTa and single-module global pointer, namely RSGP, which formulates joint extraction as a global pointer linking problem. Considering the uniqueness of Chinese language structure, we introduce the RoBERTa-wwm pre-trained language model at the encoding layer to obtain a better embedding representation. Then, we represent the input sentence as a third-order tensor and score each position in the tensor to prepare for the subsequent process of decoding the triples. In the end, we design a novel single-module global pointer decoding approach to alleviate the generation of redundant information. Specifically, we analyze the decoding process of single character entities individually, improving the time and space performance of RSGP to some extent.ResultsIn order to verify the effectiveness of our model in extracting Chinese medical entities and relations, we carry out the experiments on the public dataset, CMeIE. Experimental results show that RSGP performs significantly better on the joint extraction of Chinese medical entities and relations, and achieves state-of-the-art results compared with baseline models.ConclusionThe proposed RSGP can effectively extract entities and relations from Chinese medical texts and help to realize the structure of Chinese medical texts, so as to provide high-quality data support for the construction of Chinese medical knowledge graphs.

An Optimal Model for Medical Text Classification Based on Adaptive Genetic Algorithm

AbstractAutomatic text classification, in which textual data is categorized into specified categories based on its content, is a classic issue in the science of Natural Language Processing. In recent years, there has been a notable surge in research on medical text classification due to the increasing availability of medical data like patient medical records and medical literature. Machine learning and statistical methods, such as those used in medical text classification, have proven to be highly efficient for these tasks. However, a significant amount of manual labor is still required to categorize the extensive dataset utilized for training. Recent research have demonstrated the effectiveness of pretrained language models, including machine learning models, in reducing the time and effort required for feature engineering by medical experts. However, there is no statistically significant enhancement in performance when directly applying the machine learning model to the classification task. In this paper, we present a hybrid machine learning model that combines individual traditional algorithms augmented by a genetic algorithm. However, the improved model is designed to enhance performance by optimizing the weight parameter. In this context, the best single model demonstrated commendable accuracy. In addition, when applying the hybridization approach and optimizing the weight parameters, the results were substantially enhanced. The results underscore the superiority of our augmented hybrid model over individual traditional algorithms. We conduct experiments using two distinct types of datasets: one comprising medical records, such as the Heart Failure Clinical Record and another consisting of medical literature, such as PubMed 20k RCT. So, the objective is to clearly showcase the effectiveness of our approach by highlighting the significant enhancements in accuracy, precision, F1-score and Recall achieved through our improved model.

Ocimum Tenuiflorum Linne: A Narrative Review

Indonesia's diverse flora offers significant potential for medicinal plant production, yet reliance on anecdotal experiences prompts a deeper examination. This study focuses on Ruku-ruku (Ocimum Tenuiflorum Linne), traditionally a culinary herb in West Sumatra. Despite its local culinary use, Ruku-ruku remains largely unexplored for medicinal properties, emphasizing the importance of phytochemical screening to uncover its therapeutic potential. Ocimum Sanctum, known as Holy Basil or Tulsi, holds sacred status in India and boasts a rich history documented in ancient medical texts. This study aimed to review the evidence for therapeutic efficacy and botanical and chemical aspects. The research methodology, employing a narrative literature review, assesses 40 studies (2015-2023). Results underscore Tulsi's holistic benefits, covering metabolic disorders, cardiovascular and cognitive health, infections, cancer prevention, oral health, wound healing, and weight management. Diverse formulations, from essential oils to capsules and gels, highlight the extensive applications of Ocimum Tenuiflorum Linne. The study explores botanical and chemical aspects, including shoot induction and essential oil extraction. Ocimum Sanctum's adaptogenic and antimicrobial properties are discussed, showcasing its potential under stress conditions and against various infections. In conclusion, this review stresses the vast therapeutic potential of Ocimum Tenuiflorum Linne, urging further research to unlock its full medicinal benefits. Integrating traditional wisdom with modern science positions these plants as valuable subjects for pharmaceutical and medicinal applications, potentially addressing contemporary health challenges, including the COVID-19 pandemic. This research contributes to providing information about Ocimum Tenuiflorum Linne and its benefits for health care as therapeutic properties.

What makes a medical translator?

Advancements in healthcare and the rise of patient-centredness have increased workloads for professional medical translators. In parallel, new and emerging technologies are impacting translators’ output and workflows, though investigations into the medical translation domain remain scarce. This article presents the results of an international survey on 165 professional medical translators to identify trends concerning their socio-demographic and professional profiles, the textual- and process-related challenges of translating medical texts aimed at patients as laypersons, and their use of technology, specifically Computer-Assisted Translation (CAT) tools and Automatic Speech Recognition (ASR). Results show that the medical translators surveyed have decades-long experience in translation and come primarily from a linguistic background. They find specialised terminology and delivery deadlines as most challenging when translating patient-facing medical texts. Finally, most medical translators in our survey use CAT tools when translating or post-editing, although a small minority also integrate ASR tools into their workflows.

Jiawei Taohe Chengqi Decoction attenuates CCl4 induced hepatic fibrosis by inhibiting HSCs activation via TGF-β1/CUGBP1 and IFN-γ/Smad7 pathway

BackgroundHepatic fibrosis (HF) is an essential stage in the progression of different chronic liver conditions to cirrhosis and even hepatocellular carcinoma. The activation of hepatic stellate cells (HSCs) plays a crucial role in the progression of HF. IFN- γ/Smad7 pathway can inhibit HSCs activation, while TGF-β1/CUGBP1 pathway can inhibit IFN-γ/Smad7 pathway transduction and promote HSCs activation. Thus, inhibiting the TGF-β1/CUGBP1 pathway and activating the IFN-γ/Smad7 pathway reverses HSCs activation and inhibits HF. Jiawei Taohe Chengqi Decoction (JTCD) was derived from the Taohe Chengqi Tang in the ancient Chinese medical text titled “Treatise on Febrile Diseases”. We found several anti-HF components in JTCD including ginsenoside Rb1 and others, but the specific mechanism of anti-HF in JTCD is not clear. PurposeTo elucidate the specific mechanism by which JTCD reverses HF by inhibiting the activation of HSCs, and to establish a scientific foundation for treating HF with Traditional Chinese medicine (TCM). MethodsWe constructed a CCl4-induced mice HF model in vivo and activated human hepatic stellate cell line (LX-2) with TGF-β1 in vitro, after which they were treated with JTCD and the corresponding inhibitors. We examined the expression of pivotal molecules in the two pathways mentioned above by immunofluorescence staining, Western blotting and RT-PCR. ResultsJTCD attenuated liver injury and reduced serum ALT and AST levels in mice. In addition, JTCD attenuated CCl4-induced HF by decreasing the expression of α-SMA, COL1A1 and other markers of HSCs activation in mice liver tissue. Moreover, JTCD effectively suppressed the levels of TGF-β1, p-Smad3, p-p38MAPK, p-ATF2, and CUGBP1 in vivo and in vitro and upregulated the levels of IFN-γ, p-STAT1, and Smad7. Mechanically, after using the inhibitors of both pathways in vitro, we found that JTCD inhibited the activation of HSCs by restoring the balance of the TGF-β1/CUGBP1 and IFN-γ/Smad7 pathways. ConclusionWe demonstrated that JTCD inhibited HSCs activation and reversed HF by inhibiting the TGF-β1/CUGBP1 signalling pathway and upregulating the IFN-γ/Smad7 signalling pathway. Moreover, we have identified specific links where JTCD interferes with both pathways to inhibit HSCs activation. JTCD is an effective candidate for the clinical treatment of HF.

Non-targeted metabolomics and explainable artificial intelligence: Effects of processing and color on coniferyl aldehyde levels in Eucommiae cortex

Eucommia ulmoides, a plant native to China, is valued for its medicinal properties and has applications in food, health products, and traditional Chinese medicine. Processed Eucommiae Cortex (EC) has historically been a highly valued medicine. Ancient doctors had ample experience processing EC, especially with ginger juice, as documented in traditional Chinese medical texts. The combination of EC and ginger juice helps release and transform the active ingredients, strengthening the medicine's effectiveness and improving its taste and shelf life. However, the lack of quality control standards for Ginger-Eucommiae Cortex (G-EC), processed from EC and ginger, presents challenges for its industrial and clinical use. This study optimized G-EC processing using the CRITIC and Box-Behnken methods. Metabolomics showed 517 chemical changes between raw and processed G-EC, particularly an increase in coniferyl aldehyde (CFA). Explainable artificial intelligence techniques revealed the feasibility of using color to CFA content, providing insights into quality indicators.

RoGraphER: Enhanced Extraction of Chinese Medical Entity Relationships Using RoFormer Pre-Trained Model and Weighted Graph Convolution

Unstructured Chinese medical texts are rich sources of entity and relational information. The extraction of entity relationships from medical texts is pivotal for the construction of medical knowledge graphs and aiding healthcare professionals in making swift and informed decisions. However, the extraction of entity relationships from these texts presents a formidable challenge, notably due to the issue of overlapping entity relationships. This study introduces a novel extraction model that leverages RoFormer’s rotational position encoding (RoPE) technique for an efficient implementation of relative position encoding. This approach not only optimizes positional information utilization but also captures syntactic dependency information by constructing a weighted adjacency matrix. During the feature fusion phase, the model employs an entity attention mechanism for a deeper integration of features, effectively addressing the challenge of overlapping entity relationships. Experimental outcomes demonstrate that our model achieves an F1 score of 83.42 on datasets featuring overlapping entity relations, significantly outperforming other baseline models.

MedT2T: An adaptive pointer constrain generating method for a new medical text-to-table task

Medical information extraction is a crucial task in the governance of healthcare data within medical information systems in the medical internet network, aimed at extracting vital information from existing content. However, structuring this key information into a table is currently a challenge, hindering the development of AI-driven smart health. In this study, we study the medical text-to-table task based on a new generative perspective. To address the challenges of ineffective numerical embedding, flexible table formats, and dense medical terminology and numerical entities in an end-to-end manner, we present the innovative medical text-to-table model called MedT2T. This model, built on the BART backbone, operates in an end-to-end manner and comprises three essential modules: Encoder, Decoder, and Adapter. The Encoder utilizes an innovative adaptive medical numerical constraint to facilitate precise embedding and generation of medical numerical data. The generated output of the Decoder adheres to relational constraints and table formats, ensuring the desired structure and organization. Additionally, the Adapter incorporates an adaptive pointer generation mechanism, allowing for dynamic referencing of medical terminology and numerical information either from the source text or generated through the vocabulary distribution of the Decoder. Our method outperforms existing baselines in terms of exact match, character level match, and BERTScore. We also proved that MedT2T can serve as an essential table extraction tool to bring informative gains for medical downstream classifiers and predictors. This study not only achieved accurate entity generation for tables from lengthy medical texts to improve physician efficiency in accessing critical information for decision-making, but also provided large-scale structured training table data for downstream tasks such as AI-driven smart healthcare.

Advancements and Impact of Medical Translation During the Golden Age: A Comprehensive Analysis

This article presents a comprehensive analysis of the advancements and impact of medical translation during the Golden Age of Translation. It focuses on the fundamental role that translators and scholars played in disseminating medical knowledge, shaping medical practices, and fostering intellectual and cultural exchange. It also discusses the significance of the House of Wisdom in Baghdad, key translators and their contributions, as well as the influence of translated medical texts such as Avicenna's Canon of Medicine, Al-Razi's Book of Healing, and Al-Zahrawi's Comprehensive Book on Medicine. Also examined is the impact of translation on the standardization of medical terminology and the transmission of medical knowledge across cultures and regions. The article concludes by highlighting the lasting legacy of translations in terms of their impact on global medical knowledge and suggests possible areas in which to conduct further research on medical translation history, as well as the role of modern technology in understanding and interpreting historical medical texts.