Medical Translation Research Articles

Healthcare professionals' experiences of the usefulness and ease of using a digital medical translation tool in communicating with minority language-speaking hospitalized heart surgery patients

Abstract Background/Introduction Healthcare professionals are required to deliver high-quality care while adhering to the human right to equity for all patients. Language barriers often result in miscommunication between healthcare professionals and patients, leading to decreased patient satisfaction, compromised patient safety (such as increased adverse events), and diminished healthcare quality. Studies focusing on implementing digital medical translation tools and measuring their usefulness during patients’ hospitalization are highly warranted. Purpose To survey and explore healthcare professionals' experiences of the usefulness and ease of using a digital medical translation tool in communicating with minority language-speaking heart surgery patients during hospitalization. Methods Participants completed an internationally published six-item, study-specific questionnaire on health care professionals' experiences regarding perceived usefulness and ease of use from October 2022 to February 2023. The first three items, namely: (1) 'The digital tool was easy to use,' (2) 'The languages were easy to understand,' and (3) 'The digital tool was useful,' were measured on a 5-point Likert scale ranging from 1 ('totally agree') to 5 ('totally disagree'). The last three items were open-ended: (4) 'What they liked best,' (5) 'What they liked the least,' and (6) 'Suggestions for improvement'. Results Health care professionals (i.e., registered nurses, intensive care nurses, operating room nurses, anaesthesia nurses and physiotherapists) (n=53) aged, mean 35 (SD, min-max 9.7, 25-58). They reported the digital medical translation tool was easy to use ("totally agree/agree" 15/33, 91%), easy to understand ("totally agree/agree" 30/22, 97%), and useful ("totally agree/agree" 24/25, 92%) as illustrated in figure 1. Illustrative citations from the open responses: What health care professionals liked best: "Enabling communication with minority language-speaking patients feels safe in terms of patient safety" and "The opportunity to form a clinical picture of the patient at the beginning of the shift gives me confidence. The possibility to meet the patient's psychosocial needs and the opportunity to more easily clarify situations and pains and discomforts." They liked the least that it "Took some time to find my way around between questions at the beginning of using the tool" and suggestions for improvement were cited as "Split up the phrases in the tool in several categories (icons) e.g., intubation phase" and "Add more phrases specific for our heart surgery patients". Conclusions Healthcare professionals found a digital medical translation tool to be useful and easy to use in meeting the needs of patients and improving patient safety during communication with minority-speaking heart surgery patients.

Exploring ethical issues in the Chinese translation of medical terms: The convergence of translation ethics and medical ethics

This article explores the ethical aspects of translating medical terminology into English, with a particular emphasis on the joint influence of medical ethics and translation ethics. This study identified four key issues in current translation practice through the analysis of various cases from the perspective of Chestermans five kinds of translation ethics, including representation ethics, service ethics, communication ethics, norm-based ethics, and commitment ethics. The four issues involve discrimination, superstition, contempt, and overgeneralization. These issues may hinder the provision of appropriate medical services and social acceptance by fueling social stigma, public misunderstandings, and negative perceptions of diseases. This study suggests that medical translation is not only about language conversion, but also involves considerations of cultural, ethical, and social factors. This article attempts to provide practical advice for medical translators and healthcare workers by combining medical ethics with translation ethics. It suggests that future research should prioritize interdisciplinary collaboration to improve ethical standards in medical translation and promote more accurate and respectful descriptions of diseases in different cultures.

The Construction of Ethical Norms for Translation Technology for Medical Translators

This study explores the construction of ethical norms for medical translators who use translation technologies such as Machine Translation (MT), Neural Machine Translation (NMT), and Computer-Aided Translation (CAT) tools. As these technologies become integral to medical translation, ethical concerns surrounding accuracy, confidentiality, and humanitarian considerations arise. The study emphasizes the importance of maintaining a balance between the efficiency of technology and the ethical imperatives that define medical translation. Using a mixed-methods approach, including literature review, comparative analysis, and textual evaluation, the study investigates the ethical challenges posed by translation technology. It proposes ethical norms focusing on human oversight, data security, empathy, and accountability. The findings suggest that the role of the translator is evolving, requiring not only linguistic expertise but also competence in navigating the ethical complexities introduced by technology. The research contributes to the development of actionable guidelines that ensure the responsible use of translation technology while preserving the core values of medical ethics.

Recent regulatory developments in EU Medical Device Regulation and their impact on biomaterials translation

AbstractWe envision this work to assist researchers and medical device developers (beside other stakeholders) to better understand biomaterial‐based medical device development and its approval process proposed by the new MDR and IVDR in the European Union, as more complex biomaterials emerge, with the MDR reflecting the progress in biomaterial discoveries. Additionally, insufficient international harmonization in regulatory laws and poor‐quality data reporting contribute to the problem. This review describes the possible reasons for a slowing biomaterials translational trend observed over the past decades, focusing on the European Market, and suggests a feasible approach for biomaterials‐based medical device translation into the clinic. Suitable solutions to upgrade biomaterial translation to the clinic have not yet been provided by the field: no additional hurdles should be imposed for researchers, clinicians, the medical device industry, and insurance companies, which all should collaborate on bringing innovative solutions to patients. The new MDR and IVDR represent a substantial advancement in ensuring patient safety and reflect a major step forward in healthcare. However, they should not constrain innovation in biomaterials‐based medical device development. Incorporating reverse engineering from patient safety and a ‘safe by design’ (SbD) strategy early into medical device development might lead to a smoother and successful approval process. A solid R&D phase, with an emphasis on device safety and performance assessment, is fundamental to ensure an effective transition into the clinic. We offer an overview of the recently implemented regulations on medical devices and in vitro diagnostics across the EU, describing a shifting paradigm in the field of biomaterials discovery. As more complex biomaterials emerge, suitable regulations will be necessary to keep bringing safe and well‐performing medical solutions to patients.

Determining the Medical Spanish Translation Capabilities of Three Artificial Intelligence Translation Models for Mohs Micrographic Surgical Instructions

Determining the Medical Spanish Translation Capabilities of Three Artificial Intelligence Translation Models for Mohs Micrographic Surgical Instructions

Reliable multi-modal medical image-to-image translation independent of pixel-wise aligned data.

The current mainstream multi-modal medical image-to-image translation methods face a contradiction. Supervised methods with outstanding performance rely on pixel-wise aligned training data to constrain the model optimization. However, obtaining pixel-wise aligned multi-modal medical image datasets is challenging. Unsupervised methods can be trained without paired data, but their reliability cannot be guaranteed. At present, there is no ideal multi-modal medical image-to-image translation method that can generate reliable translation results without the need for pixel-wise aligned data. This work aims to develop a novel medical image-to-image translation model that is independent of pixel-wise aligned data (MITIA), enabling reliable multi-modal medical image-to-image translation under the condition of misaligned trainingdata. The proposed MITIA model utilizes a prior extraction network composed of a multi-modal medical image registration module and a multi-modal misalignment error detection module to extract pixel-level prior information from training data with misalignment errors to the largest extent. The extracted prior information is then used to construct a regularization term to constrain the optimization of the unsupervised cycle-consistent Generative Adversarial Network model, restricting its solution space and thereby improving the performance and reliability of the generator. We trained the MITIA model using six datasets containing different misalignment errors and two well-aligned datasets. Subsequently, we conducted quantitative analysis using peak signal-to-noise ratio and structural similarity as metrics. Moreover, we compared the proposed method with six other state-of-the-art image-to-image translationmethods. The results of both quantitative analysis and qualitative visual inspection indicate that MITIA achieves superior performance compared to the competing state-of-the-art methods, both on misaligned data and aligned data. Furthermore, MITIA shows more stability in the presence of misalignment errors in the training data, regardless of their severity or type. The proposed method achieves outstanding performance in multi-modal medical image-to-image translation tasks without aligned training data. Due to the difficulty in obtaining pixel-wise aligned data for medical image translation tasks, MITIA is expected to generate significant application value in this scenario compared to existingmethods.

Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

Un-Stroking the Spanish Translations (and Synonyms) of “Stroke”: A Comparative Analysis

Within the field English-Spanish medical translation we encounter a noteworthy list of difficulties, arising from the fact that medical English oftentimes tends to use either a single term (or a phrase) lexicalizing each concept. Considering the increasing relevance of “stroke” nowadays, we aimed at researching the Spanish renderings (and synonyms) for this issue in order to see whether the English language provides us with more or less terms and phrases. By using institutional and professional sources, in this paper, we first delve into the etymology of “stroke” in English language, as well as the stroke types and subtypes, recording a total of 10 lexical items and phrases. Then, we research into the Spanish counterparts. By referring to computer-aided translation, as well as human translation, later on we carry out the English-Spanish contrast. Overall, the findings unveil how Spanish lexical and phrase items (n=33) triple the English ones available (n=10).

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.

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.

Importance of key stakeholder communication in the implementation of an in-hospital digital medical translation application for heart surgery patients: a qualitative study

Abstract Background/Introduction The scientific and clinical communities are inundated with health applications designed for testing and/or implementation to support and enhance clinical practice. However, there is a deficiency in studies concentrating on vital factors such as stakeholder involvement to ensure the successful implementation of such applications. Purpose The purpose of this study was to investigate the experiences of key stakeholders involved in the process leading to the implementation of a digital medical translation tool designed for in-hospital use with heart surgery patients who speak minority languages. Methods In-depth interviews were conducted using a semi-structured interview guide through digital (i.e., Zoom) interviews in June 2023. The data collected was analyzed using NVivo software, following Braun and Clarke´s six-step thematic analysis process. Results Five stakeholders participated in the interviews, including representatives from the Regional Health Authorities Information Technology partner and the project leader, and representatives from the company responsible for the digital medical translation tool. All with major stakes in such an in-hospital application implementation process. Four main themes with corresponding quotations emerged from the analysis, pointing to the need for: i) Qualified stakeholders: Stakeholders who possessed the knowledge of "what to do and when" played a crucial role in the process. ii) Communication between stakeholders: Effective communication between stakeholders was deemed essential for optimizing the process at the required pace, with an emphasis on collaborative efforts by "working as partners to enhance services for minority-speaking patients". iii) Requirement specification for procurement: The importance of "detailed and clear quality requirements in the procurement process to select the best clinical translation tool", was highlighted. iv) Clinical practice foundation: "Expert nurses from the hospital setting and medical doctor from the company adjusted the translation tool" was pointed to as necessary to suit clinical practice needs. Conclusions Key stakeholders involved in the implementation of a medical translation tool for minority-speaking patients within a hospital setting pointed to four vital aspects for successfully implementing a digital medical translation tool in-hospital care. Effective communication among stakeholders was seen to inspire and improve the working process. Having qualified stakeholders was vital for ensuring quality requirements and establishing a solid clinical foundation during the application implementation.

Target-Guided Diffusion Models for Unpaired Cross-Modality Medical Image Translation.

In a clinical setting, the acquisition of certain medical image modality is often unavailable due to various considerations such as cost, radiation, etc. Therefore, unpaired cross-modality translation techniques, which involve training on the unpaired data and synthesizing the target modality with the guidance of the acquired source modality, are of great interest. Previous methods for synthesizing target medical images are to establish one-shot mapping through generative adversarial networks (GANs). As promising alternatives to GANs, diffusion models have recently received wide interests in generative tasks. In this paper, we propose a target-guided diffusion model (TGDM) for unpaired cross-modality medical image translation. For training, to encourage our diffusion model to learn more visual concepts, we adopted a perception prioritized weight scheme (P2W) to the training objectives. For sampling, a pre-trained classifier is adopted in the reverse process to relieve modality-specific remnants from source data. Experiments on both brain MRI-CT and prostate MRI-US datasets demonstrate that the proposed method achieves a visually realistic result that mimics a vivid anatomical section of the target organ. In addition, we have also conducted a subjective assessment based on the synthesized samples to further validate the clinical value of TGDM.

Time for Medicine and Public Health to Leave Platform X.

For more than 50 years, digital technologies have been employed for the creation and distribution of knowledge in health services. In the last decade, digital social media have been developed for applications in clinical decision support and population health monitoring. Recently, these technologies have also been used for knowledge translation, such as in the process where research findings created in academic settings are established as evidence and distributed for use in clinical practice, policy making, and health self-management. To date, it has been common for medical and public health institutions to have social media accounts for the dissemination of novel research findings and to facilitate conversations about these findings. However, recent events such as the transformation of the microblog Twitter to platform X have brought to light the need for the social media industry to exploit user data to generate revenue. In this viewpoint, it is argued that a redirection of social media use is required in the translation of knowledge to action in the fields of medicine and public health. A new kind of social internet is currently forming, known as the "fediverse," which denotes an ensemble of open social media that can communicate with each other while remaining independent platforms. In several countries, government institutions, universities, and newspapers use open social media to distribute information and enable discussions. These organizations control their own channels while being able to communicate with other platforms through open standards. Examples of medical knowledge translation via such open social media platforms, where users are less exposed to disinformation than in general platforms, are also beginning to appear. The current status of the social media industry calls for a broad discussion about the use of social technologies by health institutions involving researchers and health service practitioners, academic leaders, scientific publishers, social technology providers, policy makers, and the public. This debate should not primarily take place on social media platforms but rather at universities, in scientific journals, at public seminars, and other venues, allowing for the transparent and undisturbed communication and formation of opinions.

Patient Satisfaction With Over-the-Phone Interpretation in an Outpatient Gynecology Clinic [ID 2683580

INTRODUCTION: In Miami, Florida, the majority of households are bilingual or non-English speaking, often necessitating use of translation services in clinical settings. Culturally competent care includes using a language that patients understand, which can lead to improved clinical outcomes. Over-the-phone translation is the only method of interpretation available in our public safety net outpatient gynecology clinic. To our knowledge, no study has explored patient satisfaction and comfort with this mode of interpretation in a gynecology clinic. METHODS: An anonymous 33-item Spanish language survey, adapted from existing studies on medical translation, was used to assess the translation experience for adult patients. Surveys were collected verbally and in-person immediately after a clinic visit in which over-the-phone translation services were used. A Likert-type scale (1, very bad, to 4, very good) was used to assess satisfaction domains. RESULTS: Twenty-nine Spanish-speaking participants completed the survey. The median age was 47.5 years. Over 75% (n=22) were uninsured and close to 50% had not completed high school/GED (n=14). Mean satisfaction with interpretation (3.55) and medical care (4) were high. However, 14% (n=4) felt disrespect by their interpreter, 10% (n=3) expressed dissatisfaction with their understanding of treatment plans, and 10% (n=3) reported issues of privacy with the interpreter. In participants over age 40 (n=19) and with less than a high school education (n=14), there was more discomfort in discussing medical history using the interpreter compared to less than 40 and college-educated respondents. 65% (n=19) of all respondents indicated a preference for an in-person professional interpreter. CONCLUSION: Although further research is needed, this study highlights potential concerns, particularly in older and less-educated patients, with over-the-phone translation services in the outpatient gynecology clinical setting. In settings where patients are likely to be non-English speaking, providing optimal translation services is crucial to both patient satisfaction and culturally competent care.

COVID-19 Study: A New Principle Discovery

Abstract:: The impact and burden of coronavirus outbreak (COVID-19, SARS-CoV-2) still exists worldwide. Different knowledge, opinions, and techniques determine the various outcomes and potency of preventive and therapeutic strategies. COVID-19 infections, long COVID, and the complexity of treatment variability are challenging issues for therapeutic promotion and cost-effective consideration. To speed up global efforts against COVID-19, clinical diagnosis, therapeutic selections, and drug combination studies should be optimized in different ways. The epidemic condition, vaccination, techniques, and therapeutic options are open to new expeditions. To target infectious and therapeutic variability, biomedical mechanisms and pathways should be understood. Facilitating and enhancing new global machinery and roadmap against viral-induced social damages and potential epidemics should be established for biological and pharmaceutical purposes. Thus, in this study, the ways of medical translation for COVID-19 treatment are discussed.

AI-driven translations for kidney transplant equity in Hispanic populations

Health equity and accessing Spanish kidney transplant information continues being a substantial challenge facing the Hispanic community. This study evaluated ChatGPT’s capabilities in translating 54 English kidney transplant frequently asked questions (FAQs) into Spanish using two versions of the AI model, GPT-3.5 and GPT-4.0. The FAQs included 19 from Organ Procurement and Transplantation Network (OPTN), 15 from National Health Service (NHS), and 20 from National Kidney Foundation (NKF). Two native Spanish-speaking nephrologists, both of whom are of Mexican heritage, scored the translations for linguistic accuracy and cultural sensitivity tailored to Hispanics using a 1–5 rubric. The inter-rater reliability of the evaluators, measured by Cohen’s Kappa, was 0.85. Overall linguistic accuracy was 4.89 ± 0.31 for GPT-3.5 versus 4.94 ± 0.23 for GPT-4.0 (non-significant p = 0.23). Both versions scored 4.96 ± 0.19 in cultural sensitivity (p = 1.00). By source, GPT-3.5 linguistic accuracy was 4.84 ± 0.37 (OPTN), 4.93 ± 0.26 (NHS), 4.90 ± 0.31 (NKF). GPT-4.0 scored 4.95 ± 0.23 (OPTN), 4.93 ± 0.26 (NHS), 4.95 ± 0.22 (NKF). For cultural sensitivity, GPT-3.5 scored 4.95 ± 0.23 (OPTN), 4.93 ± 0.26 (NHS), 5.00 ± 0.00 (NKF), while GPT-4.0 scored 5.00 ± 0.00 (OPTN), 5.00 ± 0.00 (NHS), 4.90 ± 0.31 (NKF). These high linguistic and cultural sensitivity scores demonstrate Chat GPT effectively translated the English FAQs into Spanish across systems. The findings suggest Chat GPT’s potential to promote health equity by improving Spanish access to essential kidney transplant information. Additional research should evaluate its medical translation capabilities across diverse contexts/languages. These English-to-Spanish translations may increase access to vital transplant information for underserved Spanish-speaking Hispanic patients.

Difficulties and Suggestions for Medical English Translation: Insights from Experience

Difficulties and Suggestions for Medical English Translation: Insights from Experience

SeRL: Style-embedding representation learning for unsupervised CT images synthesis from unpaired MR images

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, and the fastest-growing cause of cancer deaths in the United States. Computed tomography (CT) and magnetic resonance (MR) imaging are the key image-based examination methods for HCC patients. Patients may alternatively undergo CT and MR exams given a radiation dose of the former and the cost of the latter. Due to organ representation differences between CT and MR, it’s a great challenge to detect the tumors from a time series of data consisting of both CT and MR by a single model. The annotations for these time series data will consume much time and labor for the physicians. Thus, we propose our style-embedding representation learning (SeRL) for unsupervised and unpaired abdomen CT and MR translation. Different from current medical image translation models, the style-representation information from real CT and real MR images has been embedded in the translation process to bypass some local minima during the convergence process and improve the synthesis results. Our patch-corrosion augmentation method enhances the style-embedding representation learning by bringing more diversity to the training data. Combined with the self-attention module, SeRL eliminates the noise caused by low grayscale pixel values during translation. Results on the unpaired HCC patient's CT and MR images show that our proposed SeRL is able to generate high quality CT images from MR ones. Evaluations such as Frechet Inception Distance (FID), Sliced Wasserstein Distance (SWD), and liver segmentation dice score are utilized to demonstrate our advantages over other state-of-the-art unsupervised methods.

Where are we in the implementation of tissue-specific epigenetic clocks?

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense. Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set. Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when-unsurprisingly-the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR. Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.

Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models.

Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequency-guided diffusion model (FGDM) that employs frequency-domain filters to guide the diffusion model for structure-preserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from the target domain, and used directly for source-to-target domain translation without any exposure to the source-domain data during training. We evaluated it on three cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and a cross-institutional MR imaging translation task. FGDM outperformed the state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics of Fréchet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), showing its significant advantages in zero-shot medical image translation.