Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

Speech production skills of bilingual children using cochlear implants.

Evidence is lacking on the impact of bilingualism on the speech skills of children with cochlear implants (CIs). This study described the speech production of children with CIs acquiring French and one or more additional spoken languages. Four groups of children aged 4-11 were included: bilinguals (n = 15) and monolinguals (n = 14) with CIs and bilinguals (n = 14) and monolinguals (n = 20) with typical hearing. Data were collected about the percentage of consonant correct (PCC) and vowel correct (PVC) produced in French and intelligibility in all languages they spoke. Bilingual and monolingual children with CIs had comparable speech accuracy in French, but the pattern differed, impacting PCC for bilinguals and PVC for monolinguals. Most children with CIs had accurate and intelligible speech in French, but few bilingual children with CIs were highly intelligible in their home language. Therefore, bilingualism did not impede the speech production outcomes of bilingual children with CIs in the language of the wider community.

Attention-language interface in Multilingual Assessment instrument for Narratives.

The current study employed the Multilingual Assessment Instrument for Narratives (MAIN) to test comprehension of narrative macrostructure in Russian in a visual world eye-tracking paradigm. The four MAIN visual narratives are structurally similar and question referents' goals and internal states (IS). Previous research revealed that children's MAIN comprehension differed among the four narratives in German, Swedish, Russian, and Turkish, but it is not clear why. We tested whether the difference in comprehension was (a) present, (b) caused by complicated inferences in understanding IS compared with goals, and (c) ameliorated by orienting visual attention to the referents whose IS was critical for accurate comprehension. Our findings confirmed (a) and (b) but found no effect of attentional cues on accuracy for (c). The multidimensional theory of narrative organization of children's knowledge of macrostructure needs to consider the type of inferences necessary for IS that are influenced by subjective interpretation and reasoning.

Machine learning-based classification of Parkinson's disease using acoustic features: Insights from multilingual speech tasks.

This study advances the automation of Parkinson's disease (PD) diagnosis by analyzing speech characteristics, leveraging a comprehensive approach that integrates a voting-based machine learning model. Given the growing prevalence of PD, especially among the elderly population, continuous and efficient diagnosis is of paramount importance. Conventional monitoring methods suffer from limitations related to time, cost, and accessibility, underscoring the need for the development of automated diagnostic tools. In this paper, we present a robust model for classifying speech patterns in Korean PD patients, addressing a significant research gap. Our model employs straightforward preprocessing techniques and a voting-based machine learning approach, demonstrating superior performance, particularly when training data is limited. Furthermore, we emphasize the effectiveness of the eGeMAPSv2 feature set in PD analysis and introduce new features that substantially enhance classification accuracy. The proposed model, achieving an accuracy of 84.73 % and an area under the ROC (AUC) score of 92.18 % on a dataset comprising 100 Korean PD patients and 100 healthy controls, offers a practical solution for automated diagnosis applications, such as smartphone apps. Future research endeavors will concentrate on enhancing the model's performance and delving deeper into the relationship between high-importance features and PD.

Bridging Language Barriers in COVID-19 Research: Descriptive Study of AccesoCovid.com's Reach and User Engagement.

BACKGROUND: The COVID-19 pandemic underscored the challenge of swiftly disseminating research findings to a global audience. Language barriers further exacerbated disparities in access to timely scientific information, particularly for non-English speaking communities. The majority of COVID-19 research was published in English, limiting accessibility for Spanish-speaking populations. OBJECTIVE: This paper aims to assess the reach and effectiveness of AccesoCovid.com, a platform designed to disseminate up-to-date COVID-19 research in both English and Spanish, addressing the language gap in scientific communication. METHODS: AccesoCovid.com was developed through a partnership between the University of California, San Francisco (UCSF) and Universidad Nacional Autónoma de México (UNAM). The website's performance and user engagement were evaluated using Google Analytics over a span of 2 years. Key metrics included user language preference, geographical distribution, and site traffic. The website summarized and translated 1032 articles on various COVID-19 topics, such as "Pharmaceutical Interventions and Vaccines." RESULTS: From February 2021 to February 2023, the platform attracted 57,000 users. Of the 43,000 unique new visitors, 84.2% (n=36,219) hailed from Spanish-speaking regions. The majority accessed the site organically through search engines, with 88.4% (n=38,000) of users arriving this way, while 5000 (11.6%) users accessed the site directly. Most users (n=30,894, 72.1%) preferred the Spanish version of the site. The website's most accessed category was "Pharmaceutical Interventions and Vaccines," followed by "Clinical Presentation and Management" and "Mental Health." Regarding language distribution, 72.1% (n=30,894) of users primarily used Spanish; 21.4% (n=9215) used English; and 6.7% (n=2891) spoke other languages, including Portuguese, Chinese, and German. Geographically, the website attracted visitors from 179 countries, with the highest visitor counts from Mexico (n=12,342, 28.7%), Spain (n=6405, 14.9%), the United States (n=4416, 10.3%), and Peru (n=3821, 8.9%). CONCLUSIONS: AccesoCovid.com successfully bridged a critical language gap in the dissemination of COVID-19 research. Its success underscores the pressing need for multilingual scientific resources. The platform demonstrated significant user engagement and reach, particularly in Spanish-speaking countries. This highlights the potential for similar platforms to ensure equitable access to scientific knowledge across diverse linguistic communities. Future efforts should focus on expanding to other languages and conducting formal evaluations to enhance user satisfaction and impact.

A novel hybrid deep learning IChOA-CNN-LSTM model for modality-enriched and multilingual emotion recognition in social media.

In the rapidly evolving field of artificial intelligence, the importance of multimodal sentiment analysis has never been more evident, especially amid the ongoing COVID-19 pandemic. Our research addresses the critical need to understand public sentiment across various dimensions of this crisis by integrating data from multiple modalities, such as text, images, audio, and videos sourced from platforms like Twitter. Conventional methods, which primarily focus on text analysis, often fall short in capturing the nuanced intricacies of emotional states, necessitating a more comprehensive approach. To tackle this challenge, our proposed framework introduces a novel hybrid model, IChOA-CNN-LSTM, which leverages Convolutional Neural Networks (CNNs) for precise image feature extraction, Long Short-Term Memory (LSTM) networks for sequential data analysis, and an Improved Chimp Optimization Algorithm for effective feature fusion. Remarkably, our model achieves an impressive accuracy rate of 97.8%, outperforming existing approaches in the field. Additionally, by integrating the GeoCoV19 dataset, we facilitate a comprehensive analysis that spans linguistic and geographical boundaries, enriching our understanding of global pandemic discourse and providing critical insights for informed decision-making in public health crises. Through this holistic approach and innovative techniques, our research significantly advances multimodal sentiment analysis, offering a robust framework for deciphering the complex interplay of emotions during unprecedented global challenges like the COVID-19 pandemic.

Examining the Heterogeneous Early Literacy Profiles of First-Grade Students Who Are English Learners.

This study examined the heterogeneity of early literacy profiles of English learners and non-English learners. Utilizing a latent profile analysis, the degree to which distinct learner profiles emerged was examined using code-based and language-based measures administered in the beginning of first grade. Participants included 11,803 English learners and 34,129 non-English learners. Three early literacy profiles emerged for English learners while four profiles emerged for non-English learners. Both sets of profiles can be identified based on the severity of students' difficulties with component skills rather than the specificity of their difficulties. Resulting profiles in both samples were then utilized to predict performance on a measure of broad reading comprehension administered at the end of first and second grade. Results indicated that the profile that was associated with the greatest success on the later measures of reading comprehension for both samples included the strongest performance on measures of both code-related and language-related skills. Results highlight the heterogeneity of early literacy skills within the English learner and non-English learner populations and demonstrate the importance of designing instruction that addresses the severity of a student's skill deficit.

Recruiting people with selected citizenships for the health interview survey GEDA Fokus throughout Germany: evaluation of recruitment efforts and recommendations for future research.

BACKGROUND: Germany is the second most common country of immigration after the US. However, people with own or familial history of migration are not represented proportionately to the population within public health monitoring and reporting. To bridge this data gap and enable differentiated analyses on migration and health, we conducted the health interview survey GEDA Fokus among adults with Croatian, Italian, Polish, Syrian, or Turkish citizenship living throughout Germany. The aim of this paper is to evaluate the effects of recruitment efforts regarding participation and sample composition. METHODS: Data collection for this cross-sectional and multilingual survey took place between 11/2021 and 5/2022 utilizing a sequential mixed-mode design, including self-administered web- and paper-based questionnaires as well as face-to-face and telephone interviews. The gross sample (n = 33436; age range 18-79 years) was randomly drawn from the residents' registers in 120 primary sampling units based on citizenship. Outcome rates according to the American Association for Public Opinion Research, the sample composition throughout the multistage recruitment process, utilization of survey modes, and questionnaire languages are presented. RESULTS: Overall, 6038 persons participated, which corresponded to a response rate of 18.4% (range: 13.8% for Turkish citizenship to 23.9% for Syrian citizenship). Home visits accounted for the largest single increase in response. During recruitment, more female, older, as well as participants with lower levels of education and income took part in the survey. People with physical health problems and less favourable health behaviour more often took part in the survey at a later stage, while participants with symptoms of depression or anxiety more often participated early. Utilization of survey modes and questionnaire languages differed by sociodemographic and migration-related characteristics, e.g. participants aged 50 years and above more often used paper- than web-based questionnaires and those with a shorter duration of residence more often used a translated questionnaire. CONCLUSION: Multiple contact attempts, including home visits and different survey languages, as well as offering different modes of survey administration, increased response rates and most likely reduced non-response bias. In order to adequately represent and include the diversifying population in public health monitoring, national public health institutes should tailor survey designs to meet the needs of different population groups considered hard to survey to enable their survey participation.

Machine Translation Technology in Health: A Scoping Review.

Machine Translation (MT) has emerged as a crucial tool in bridging language barriers. In health settings, MT is increasingly relevant due to the diversity of patient populations, the dominance of English in medical research, and the limited availability of human translation services. Improvements in MT accuracy have prompted a re-evaluation of its suitability in contexts where it was once deemed impractical. This scoping review with meta-analysis delved into the appropriateness and limitations of MT in health, including in medical education, literature translation, and patient-provider communication. A keyword search in PubMed, PubMed Central, and IEEE Xplore produced peer-reviewed literature that focused on MT in a health context, published from 2018 to 2023. Analysis and mapping of full-text articles revealed 33 studies among 2,589 returned abstracts, indicating that MT is still unsuitable for direct use in patient interactions, due to clinical risks linked to insufficient accuracy. However, MT was showing promise further away from patients, for translation of medical articles, terminology, and educational content. Further research in improving MT performance in these contexts, coverage of under-studied languages, and study of the existing usages of MT are recommended.

Developing, validating, and piloting a MultiTeachViews questionnaire on L1 and translation use: Attitudes of Kazakhstani secondary school EFL teachers.

This study developed, validated, and piloted a MultiTeachViews questionnaire to investigate secondary school English as a Foreign Language (EFL) teachers' attitudes towards multilingual teaching practices such as L1 and translation use. Initially, a literature review and focus group interview with six in-service EFL teachers were conducted to capture prevailing attitudes and inform content areas for the questionnaire. Items were then crafted, followed by the adoption of a 5-point Likert scale. Validation involved assessing internal and content validity through a structured checklist and expert evaluation. The pilot phase included think-aloud protocols with two teachers and a reliability test across a broader cohort of 100 teachers. Reliability testing yielded satisfactory Cronbach's Alpha coefficients (α > .70) for all scales, affirming the instrument's internal consistency. Consequently, the instrument is found to be a reliable and valid measure of EFL teachers' attitudes towards L1 and translation use in the classroom, with significant implications for Applied Linguistic and Second Language Acquisition research.•Developed, validated, and piloted a MultiTeachViews questionnaire for investigating attitudes.•Employed mixed methods in the development, validation, and piloting phases.•Found MultiTeachViews to be a reliable and valid measure of EFL teachers' attitudes towards multilingual teaching practices, such as L1 and translation use.

Long COVID impacts: the voices and views of diverse Black and Latinx residents in Massachusetts.

OBJECTIVE: To understand how Long COVID is impacting the health and social conditions of the Black and Latinx communities. BACKGROUND: Emerging research on Long COVID has identified three distinct characteristics, including multi-organ damage, persistent symptoms, and post-hospitalization complications. Given Black and Latinx communities experienced significantly higher COVID rates in the first phase of the pandemic they may be disproportionately impacted by Long COVID. METHODS: Eleven focus groups were conducted in four languages with diverse Black and Latinx individuals (n = 99) experiencing prolonged symptoms of COVID-19 or caring for family members with prolonged COVID-19 symptoms. Data was analyzed thematically. RESULTS: Most participants in non-English language groups reported they were unfamiliar with the diagnosis of long COVID, despite experiencing symptoms. Long COVID impacts spanned financial and housing stability to physical and mental health impacts. Participants reported challenging encounters with health care providers, a lack of support managing symptoms and difficulty performing activities of daily living including work. CONCLUSIONS: There is a need for multilingual, accessible information about Long COVID symptoms, improved outreach and healthcare delivery, and increased ease of enrollment in long-term disability and economic support programs.

Emergent Pragmatic Conventions in Spoken ELF Corpus Data: Micro-Diachronic Analysis of Inclusive vs. Exclusive Multilingual Practices.

This article examines multilingual practices as an example of emergent pragmatic conventions in three Transient International Groups (TIGs) using spoken English as a lingua franca (ELF) from the Vienna-Oxford International Corpus of English (VOICE). The analysis combines principles of corpus linguistics and conversation analysis by adopting a new approach for the micro-diachronic analysis of spoken interaction. Quantitative and qualitative evidence and micro-diachronic visualizations of VOICE transcripts show how the three groups examined interactively develop group-specific multilingual practices. The analysis reveals that the three groups have different preferences in this respect. While two groups develop inclusive multilingual practices in the course of their interaction, one group shows a tendency to use multilingual practices exclusively, primarily in side sequences. In addition to multilingual use, the presence or absence of metalinguistic discussions about language (and languages) plays a role for creation of shared transcultural territory and the formation of group identity. These processes are indicative of how unacquainted multilingual speakers negotiate and develop pragmatic conventions more generally.

Challenges of the Calgary-Cambridge Consultation Guide in Veterinary Multicultural and Multilingual Scenarios and the Role of Veterinary Translators.

The Calgary-Cambridge Guide is a widely recognised framework for teaching communication skills to healthcare professionals that has become a cornerstone of communication training programs in medicine and other healthcare fields. In the context of veterinary medicine, its integration into communication training programs has become an asset improving communication, education, interaction, and quality of service, enhancing the veterinary-client-patient relationship (VCPR). In veterinary medicine, however, a more challenging consultation dynamic involves the veterinarian, the owner, and the animal. The addition of a veterinary assistant that acts as an interpreter or translator is common in Hong Kong where the native language (Cantonese) coexists with English when consultations are led by non-native language speakers. This addition converts this commonly dyadic model into a triadic communication model. The addition of an assistant interpreter influences the way consultations are conducted, how information is conveyed, and how interpersonal cues and empathy are delivered. In this report we depict challenges applying the Calgary-Cambridge Guide in multicultural and multilingual veterinary medical centres in Hong Kong and highlight the role of veterinary supporting staff in these scenarios, specifically veterinary assistant interpreters.

Case Report: Benefits of LSVT LOUD in a multilingual patient with hypokinetic-hyperkinetic dysarthria and suspected progressive supranuclear palsy.

Purpose: This case study measured how well the Lee Silverman Voice Treatment (LSVT) improved vocal features, intelligibility, and communicative effectiveness for a multilingual participant with hypokinetic-hyperkinetic dysarthria secondary to suspected progressive supranuclear palsy. LSVT treatment was chosen for the participant due to the strengths and deficits he presented with prior to treatment, and for the anticipated challenges in treatment that may arise from the presence of multilingualism and impaired cognitive functioning. Methods: A multilingual patient in their 60's (English, Spanish, and French) with hypokinetic-hyperkinetic dysarthria secondary to suspected progressive supranuclear palsy completed the standard treatment sessions for LSVT. Assessment measures were taken at baseline, immediately post-treatment, and three-months post-treatment. Results: Improvements were measured in vocal quality, vocal loudness, intelligibility, and communicative effectiveness immediately post-treatment. Three months post-treatment, improvements in vocal quality and intelligibility were maintained. Conclusion: This case study illustrates that LSVT may be a beneficial treatment for complex clients who are multilingual and present with complex comorbidities and cognitive deficits. LSVT resulted in some meaningful changes in vocal quality, intelligibility, and communicative effectiveness for this individual. Clinicians who work with complex patients may wish to consider the theoretical underpinnings of LSVT, client profile, areas of client need, and ability and desire to complete an intensive treatment program to determine if trialing LSVT is appropriate. The use of LSVT with complex clients may yield positive outcomes.

Annotated dataset for sentiment analysis and sarcasm detection: Bilingual code-mixed English-Malay social media data in the public security domain.

Sentiment analysis in the public security domain involves analysing public sentiment, emotions, opinions, and attitudes toward events, phenomena, and crises. However, the complexity of sarcasm, which tends to alter the intended meaning, combined with the use of bilingual code-mixed content, hampers sentiment analysis systems. Currently, limited datasets are available that focus on these issues. This paper introduces a comprehensive dataset constructed through a systematic data acquisition and annotation process. The acquisition process includes collecting data from social media platforms, starting with keyword searching, querying, and scraping, resulting in an acquired dataset. The subsequent annotation process involves refining and labelling, starting with data merging, selection, and annotation, ending in an annotated dataset. Three expert annotators from different fields were appointed for the labelling tasks, which produced determinations of sentiment and sarcasm in the content. Additionally, an annotator specialized in literature was appointed for language identification of each content. This dataset represents a valuable contribution to the field of natural language processing and machine learning, especially within the public security domain and for multilingual countries in Southeast Asia.

Federated learning for privacy-preserving depression detection with multilingual language models in social media posts.

The incidences of mental health illnesses, such as suicidal ideation and depression, are increasing, which highlights the urgent need for early detection methods. There is a growing interest in using natural language processing (NLP) models to analyze textual data from patients, but accessing patients' data for research purposes can be challenging due to privacy concerns. Federated learning (FL) is a promising approach that can balance the need for centralized learning with data ownership sensitivity. In this study, we examine the effectiveness of FL models in detecting depression by using a simulated multilingual dataset. We analyzed social media posts in five different languages with varying sample sizes. Our findings indicate that FL achieves strong performance in most cases while maintaining clients' privacy for both independent and non-independent client partitioning.

Quebec-based parents' concerns regarding their children's multilingual development.

Many parents express concerns for their children's multilingual development, yet little is known about the nature and strength of these concerns - especially among parents in multilingual societies. This pre-registered, questionnaire-based study addresses this gap by examining the concerns of 821 Quebec-based parents raising infants and toddlers aged 0-4 years with multiple languages in the home. Factor analysis of parents' Likert-scale responses revealed that parents had (1) concerns regarding the effect of children's multilingualism on their cognition, and (2) concerns regarding children's exposure to and attainment of fluency in their languages. Concern strength was moderate to weak, and cognition concerns were weaker than exposure-fluency concerns. Transmission of a heritage language, transmission of three or more languages, presence of developmental issues, and less positive parental attitudes towards childhood multilingualism were associated with stronger concerns. These findings have both theoretical and practical implications: they advance our understanding of parental concerns and facilitate the development of support for multilingual families.

An Overview of the ADReSS-M Signal Processing Grand Challenge on Multilingual Alzheimer's Dementia Recognition Through Spontaneous Speech.

The ADReSS-M Signal Processing Grand Challenge was held at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The challenge targeted difficult automatic prediction problems of great societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD) and the estimation of cognitive test scoress. Participants were invited to create models for the assessment of cognitive function based on spontaneous speech data. Most of these models employed signal processing and machine learning methods. The ADReSS-M challenge was designed to assess the extent to which predictive models built based on speech in one language generalise to another language. The language data compiled and made available for ADReSS-M comprised English, for model training, and Greek, for model testing and validation. To the best of our knowledge no previous shared research task investigated acoustic features of the speech signal or linguistic characteristics in the context of multilingual AD detection. This paper describes the context of the ADReSS-M challenge, its data sets, its predictive tasks, the evaluation methodology we employed, our baseline models and results, and the top five submissions. The paper concludes with a summary discussion of the ADReSS-M results, and our critical assessment of the future outlook in this field.

Naming in a multilingual context: Norms for the ICMR-Manipal colour picture corpus in Kannada from the Indian context.

There have been many published picture corpora. However, more than half of the world's population speaks more than one language and, as language and culture are intertwined, some of the items from a picture corpus designed for a given language in a particular culture may not fit another culture (with the same or different language). There is also an awareness that language research can gain from the study of bi-/multilingual individuals who are immersed in multilingual contexts that foster inter-language interactions. Consequently, we developed a relatively large corpus of pictures (663 nouns, 96 verbs) and collected normative data from multilingual speakers of Kannada (a southern Indian language) on two picture-related measures (name agreement, image agreement) and three word-related measures (familiarity, subjective frequency, age of acquisition), and report objective visual complexity and syllable count of the words. Naming labels were classified into words from the target language (i.e., Kannada), cognates (borrowed from/shared with another language), translation equivalents, and elaborations. The picture corpus had > 85% mean concept agreement with multiple acceptable names (1-7 naming labels) for each concept. The mean percentage name agreement for the modal name was > 70%, with H-statistics of 0.89 for nouns and 0.52 for verbs. We also analyse the variability of responses highlighting the influence of bi-/multilingualism on (picture) naming. The picture corpus is freely accessible to researchers and clinicians. It may be used for future standardization with other languages of similar cultural contexts, and relevant items can be used in languages from different cultures, following suitable standardization.

Cross-cultural application of the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE): Cognitive phenotypes in people with temporal lobe epilepsy in India.

OBJECTIVE: Efforts to understand the global variability in cognitive profiles in patients with epilepsy have been stymied by the lack of a standardized diagnostic system. This study examined the cross-cultural applicability of the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) in a cohort of patients with temporal lobe epilepsy (TLE) in India that was diverse in language, education, and cultural background. METHODS: A cohort of 548 adults with TLE from Mumbai completed a presurgical comprehensive neuropsychological evaluation. The IC-CoDE taxonomy was applied to derive cognitive phenotypes in the sample. Analyses of variance were conducted to examine differences in demographic and clinical characteristics across the phenotypes, and chi-squared tests were used to determine whether the phenotype distribution differed between the Mumbai sample and published data from a multicenter US sample. RESULTS: Using the IC-CoDE criteria, 47% of our cohort showed an intact cognitive profile, 31% a single-domain impairment, 16% a bidomain impairment, and 6% a generalized impairment profile. The distribution of cognitive phenotypes was similar between the Indian and US cohorts for the intact and bidomain phenotypes, but differed for the single and generalized domains. There was a larger proportion of patients with single-domain impairment in the Indian cohort and a larger proportion with generalized impairment in the US cohort. Among patients with single-domain impairment, a greater proportion exhibited memory impairment in the Indian cohort, whereas a greater proportion showed language impairment in the US sample, likely reflecting differences in language administration procedures and sample characteristics including a higher rate of mesial temporal sclerosis in the Indian sample. SIGNIFICANCE: Our results demonstrate the applicability of IC-CoDE in a group of culturally and linguistically diverse patients from India. This approach enhances our understanding of cognitive variability across cultures and enables harmonized and inclusive research into the neuropsychological aspects of epilepsy.