Simple Text Research Articles

Assessing AI simplification of medical texts: Readability and content fidelity

IntroductionThe escalating complexity of medical literature necessitates tools to enhance readability for patients. This study aimed to evaluate the efficacy of ChatGPT-4 in simplifying neurology and neurosurgical abstracts and patient education materials (PEMs) while assessing content preservation using Latent Semantic Analysis (LSA). MethodsA total of 100 abstracts (25 each from Neurosurgery, Journal of Neurosurgery, Lancet Neurology, and JAMA Neurology) and 340 PEMs (66 from the American Association of Neurological Surgeons, 274 from the American Academy of Neurology) were transformed by a GPT-4.0 prompt requesting a 5th grade reading level. Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FKRE) scores were used before/after transformation. Content fidelity was validated via LSA (ranging 0–1, 1 meaning identical topics) and by expert assessment (0–1) for a subset (n = 40). Pearson correlation coefficient compared assessments. ResultsFKGL decreased from 12th to 5th grade for abstracts and 13th to 5th for PEMs (p < 0.001). FKRE scores showed similar improvement (p < 0.001). LSA confirmed high content similarity for abstracts (mean cosine similarity 0.746) and PEMs (mean 0.953). Expert assessment indicated a mean topic similarity of 0.775 for abstracts and 0.715 for PEMs. The Pearson coefficient between LSA and expert assessment of textual similarity was 0.598 for abstracts and −0.167 for PEMs. Segmented analysis of similarity correlations revealed a correlation of 0.48 (p = 0.02) below 450 words and a −0.20 (p = 0.43) correlation above 450 words. ConclusionGPT-4.0 markedly improved the readability of medical texts, predominantly maintaining content integrity as substantiated by LSA and expert evaluations. LSA emerged as a reliable tool for assessing content fidelity within moderate-length texts, but its utility diminished for longer documents, overestimating similarity. These findings support the potential of AI in combating low health literacy, however, the similarity scores indicate expert validation is crucial. Future research must strive to improve transformation precision and develop validation methodologies.

Automatic Simplification of Lithuanian Administrative Texts

Text simplification reduces the complexity of text while preserving essential information, thus making it more accessible to a broad range of readers, including individuals with cognitive disorders, non-native speakers, children, and the general public. In this paper, we present experiments on text simplification for the Lithuanian language, aiming to simplify administrative texts to a Plain Language level. We fine-tuned mT5 and mBART models for this task and evaluated the effectiveness of ChatGPT as well. We assessed simplification results via both quantitative metrics and qualitative evaluation. Our findings indicated that mBART performed the best as it achieved the best scores across all evaluation metrics. The qualitative analysis further supported these findings. ChatGPT experiments showed that it responded quite well to a short and simple prompt to simplify the given text; however, it ignored most of the rules given in a more elaborate prompt. Finally, our analysis revealed that BERTScore and ROUGE aligned moderately well with human evaluations, while BLEU and readability scores indicated lower or even negative correlations

Abstract 4116844: Simplifying Cardiology Research Abstracts: Assessing ChatGPT's Readability and Comprehensibility for Non-Medical Audiences

Background: Artificial Intelligence (AI)-powered chatbots like ChatGPT are increasingly used in academic medical settings to help with tasks such as evidence synthesis and manuscript drafting. They have shown potential in simplifying complex medical texts for non-medical audiences like patients and journalists. However, less is known about whether simplified texts may exclude important information or be of interest to patients or other non-medically-trained people such as journalists. Objective: This study aims to assess ChatGPT's capacity to simplify cardiology research abstracts by eliminating jargon and enhancing universal comprehension. Methods: We analyzed all abstracts and scientific statements published from July to November 2023 in Circulation (n=113). These abstracts were processed through ChatGPT with the prompt: "Please rewrite the following text to be comprehensible at a 5th-grade reading level. Retain all original information and exclude nothing". We assessed the readability of both original and simplified texts using Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) scores. Additionally, a panel of five physicians and five laypeople evaluated these texts for completeness, accuracy, and understandability. Results: ChatGPT transformation or abstracts reduced the required reading level from a college graduate to 8-9th grade by both FKGL (18.3 to 8.6; p&lt;.001) and FKRE (14.6 to 68.5; p&lt;.001). The physician panel generally found that the simplified abstracts retained all essential information without introducing errors. Both physicians and laypeople found the simplified texts more comprehensible. Laypeople appreciated the ease of understanding but did not strongly express a desire for more such simplified texts, and some still worried about the simplified abstracts missing important details. Conclusion: ChatGPT simplifies medical research abstracts to a level understandable by those with less than a high school education without excluding important information. This approach may be useful to communicate new research findings to patients and other non-medically-trained people.

Eye movements in oral reading by primary school students: The relevance of punctuation marks

This study aims to explore the relevance of punctuation marks during oral reading. To achieve this objective, we compared eye movement metrics of two groups of 5th-year students, classified based on the Reading Fluency variable. The fluent reading group (FL) consisted of 27 participants, while 31 participants were in the poor reading fluent group (PF). All participants read three types of text: a simple text with punctuation, a complex text with punctuation and a simple text without punctuation. By analyzing the fixations and saccades, the data suggest that the complexity of the text influences the eye-tracking metrics of the PF group, but not the FL group. On the other hand, the presence of punctuation influence the reading behavior of the FL group, but not the PF group. These findings highlight the influence of punctuation and textual complexity on the dynamics of oral reading and offer valuable insights into how reading fluency is related to text comprehention.

Exploring and enhancing the accessibility of children's oral health resources (called HABIT) for high risk communities.

Within the city of Bradford in West Yorkshire, South Asian and Eastern European communities have an increased risk of childhood tooth decay, especially among families with Limited English Proficiency. Tooth decay is preventable, with national guidelines advocating home-based behaviours (toothbrushing with fluoride toothpaste and reducing sugar intake). In England, Health Visitors have opportunities to undertake oral health conversations during universal visits for children aged 0-24 months. The HABIT (Health visitors delivering Advice in Britain on Infant Toothbrushing) intervention provides structured oral health conversations, underpinned by complex intervention methodology. A feasibility study found HABIT acceptable to parents, feasible to deliver and led to improvements in home-based behaviours. However, the reach of this original study was limited to those proficient in English. This new study focused on exploring and enhancing the accessibility of the HABIT intervention to parents with Limited English Proficiency. Twenty-four parents participated in interviews and focus groups, with 21 requesting support from interpreters. Community centres and WhatsApp were used to maximise inclusivity. Interviews and focus groups, followed a topic guide and the "Think Aloud" technique, were professionally transcribed, managed in NVivo, and thematically analysed. Team discussions facilitated analytical rigour. Recruitment continued until data saturation. Three themes were developed: (1) Navigating linguistic barriers; (2) Engagement through visuals; and (3) Addressing oral health challenges. Parents employed diverse strategies to interpret resources, including Google Translate, as well as family and wider community members. Consequently, the HABIT resources were modified to include simple text, subtitles, and translation tools. Parents highlighted the benefits of shorter oral health messages with clear visuals to help understanding, and this strategy was applied across all resources. Challenges surrounding children's resistance to toothbrushing, high sugar intake within their wider families and communities, and limited dental access were all raised. The HABIT resources were updated to address these challenges. Collaborative community engagement has enhanced the HABIT resources, enabling access for high-risk communities to preventive oral-health programmes thereby promoting health equity.

Efficient indexing and querying of annotations in a pangenome graph.

The current reference genome is the backbone of diverse and rich annotations. Simple text formats, like VCF or BED, have been widely adopted and helped the critical exchange of genomic information. There is a dire need for tools and formats enabling pangenomic annotation to facilitate such enrichment of pangenomic references. The Graph Alignment Format (GAF) is a text format, tab-delimited like BED/VCF files, which was proposed to represent alignments. GAF could also be used to store paths representing annotations in a pangenome graph, but there are no tools to index and query them efficiently. Here, we present extensions to vg and HTSlib that provide efficient sorting, indexing, and querying for GAF files. With this approach, annotations overlapping a subgraph can be extracted quickly. Paths are sorted based on the IDs of traversed nodes, compressed with BGZIP, and indexed with HTSlib/tabix via our extensions for the GAF format. Compared to the binary GAM format, GAF files are easier to edit or inspect because they are plain text, and we show that they are twice as fast to sort and half as large on disk. In addition, we updated vg annotate, which takes BED or GFF3 annotation files relative to linear sequences and projects them into the pangenome. It can now produce GAF files representing these annotations' paths through the pangenome. We showcase these new tools on several applications. We projected annotations for all Human Pangenome Reference Consortium Year 1 haplotypes, including genes, segmental duplications, tandem repeats and repeats annotations, into the Minigraph-Cactus pangenome (GRCh38-based v1.1). We also projected known variants from the GWAS Catalog and expression QTLs from the GTEx project into the pangenome. Finally, we reanalyzed ATAC-seq data from ENCODE to demonstrate what a coverage track could look like in a pangenome graph. These rich annotations can be quickly queried with vg and visualized using existing tools like the Sequence Tube Map or Bandage.

Exploring Large Language Models to generate Easy to Read content

Ensuring text accessibility and understandability are essential goals, particularly for individuals with cognitive impairments and intellectual disabilities, who encounter problems accessing information across various mediums such as web pages, newspapers, online administrative tasks, or health documents. Initiatives like Easy to Read and Plain Language guidelines aim to simplify complex texts; however, standardizing these guidelines remains challenging and often involves manual processes. This work presents an exploratory investigation into leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches to simplify Spanish texts into Easy to Read formats, with a focus on utilizing Large Language Models (LLMs) for creating accessible texts, especially in generating Easy to Read content. The study contributes a parallel corpus of Spanish adapted for Easy To Read format, which serves as a valuable resource for training and testing text simplification systems. Additionally, several text simplification experiments using LLMs and the collected corpus are conducted, involving fine-tuning and testing a Llama2 model to generate Easy to Read content. A qualitative evaluation, guided by an expert in text adaptation for Easy to Read content, is carried out to assess the automatically simplified texts. This research contributes to advancing text accessibility for individuals with cognitive impairments, highlighting promising strategies for leveraging LLMs while responsibly managing energy usage.

Integrating Literacy Programs to Strengthen School Safety Climate: A Research-Based Approach

Objective: This study aims to support the implementation of literacy programs in schools to enhance the school safety climate. The program targets the negative impacts of bullying and violence and helps students develop conflict resolution skills to reduce harmful incidents. Theoretical Framework: Academic literacy is defined as the ability to read and comprehend texts within specific content areas in a school setting. This includes understanding simple texts as well as addressing difficulties with more complex materials often encountered in school. Method: A quantitative approach with path analysis and purposive sampling was used, with the Taro Yamane method applied to select relevant samples. The study examines five dimensions of the school safety climate: Teacher-Student Relationship Quality, Student Character and Behavior, Peer Relationships, School Safety Climate Quality, and Bullying Frequency. Results and Discussion: The findings reveal that Teacher-Student Relationships, Student Behavior, and Peer Relationships all have a direct impact on the quality of the school safety climate. The school safety climate, in turn, significantly influences the overall school environment. Research Implications: The study suggests that integrating literacy programs can significantly improve the school safety climate by enhancing relationships and reducing bullying. Originality/Value: The integration of literacy programs represents a novel approach to fostering a safer and more supportive school environment. These findings offer practical insights for schools aiming to enhance their safety climates and support student development.

Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan.

This study was performed to examine the content of decision-making support and patient responses, as documented in the nursing records of individuals with cancer. These patients had received outpatient treatment at hospitals that met government requirements for providing specialized cancer care. Nursing records from the electronic medical record system (in the subjective, objective, assessment, and plan [SOAP] format), along with data from interviews, were extracted for patients receiving outpatient care at the Department of Internal Medicine and Palliative Care and the Department of Breast Oncology. Data analysis involved simple tabulation and text mining, utilizing KH Coder version 3.beta.07d. The study included 42 patients from palliative care internal medicine and 60 from breast oncology, with mean ages of 70.5 ± 12.2 and 55.8 ± 12.2 years, respectively. Decisions most frequently regarded palliative care unit admission (25 cases) and genetic testing (24 cases). The assessment category covered keywords including (1) "pain," "treatment," "future," "recuperation," and "home," as terms related to palliative care and internal medicine, as well as (2) "treatment," "relief," and "genetics" as terms related to breast oncology. The plan category incorporated keywords such as (1) "treatment," "relaxation," and "visit" and (2) "explanation," "confirmation," and "conveyance." Nurses appear crucial in evaluating patients' symptoms and treatment paths during the decision-making support process, helping them make informed choices about future treatments, care settings, and genetic testing. However, when patients cannot make a decision solely based on the information provided, clinicians must address complex psychological concepts such as disease progression and the potential genetic impact on their children. Further detailed observational studies of nurses' responses to patients' psychological reactions are warranted.

Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage of Simulated Patients Using the Simple Triage and Rapid Treatment (START) Protocol: Gage Repeatability and Reproducibility Study.

The release of ChatGPT (OpenAI) in November 2022 drastically reduced the barrier to using artificial intelligence by allowing a simple web-based text interface to a large language model (LLM). One use case where ChatGPT could be useful is in triaging patients at the site of a disaster using the Simple Triage and Rapid Treatment (START) protocol. However, LLMs experience several common errors including hallucinations (also called confabulations) and prompt dependency. This study addresses the research problem: "Can ChatGPT adequately triage simulated disaster patients using the START protocol?" by measuring three outcomes: repeatability, reproducibility, and accuracy. Nine prompts were developed by 5 disaster medicine physicians. A Python script queried ChatGPT Version 4 for each prompt combined with 391 validated simulated patient vignettes. Ten repetitions of each combination were performed for a total of 35,190 simulated triages. A reference standard START triage code for each simulated case was assigned by 2 disaster medicine specialists (JMF and MV), with a third specialist (LC) added if the first two did not agree. Results were evaluated using a gage repeatability and reproducibility study (gage R and R). Repeatability was defined as variation due to repeated use of the same prompt. Reproducibility was defined as variation due to the use of different prompts on the same patient vignette. Accuracy was defined as agreement with the reference standard. Although 35,102 (99.7%) queries returned a valid START score, there was considerable variability. Repeatability (use of the same prompt repeatedly) was 14% of the overall variation. Reproducibility (use of different prompts) was 4.1% of the overall variation. The accuracy of ChatGPT for START was 63.9% with a 32.9% overtriage rate and a 3.1% undertriage rate. Accuracy varied by prompt with a maximum of 71.8% and a minimum of 46.7%. This study indicates that ChatGPT version 4 is insufficient to triage simulated disaster patients via the START protocol. It demonstrated suboptimal repeatability and reproducibility. The overall accuracy of triage was only 63.9%. Health care professionals are advised to exercise caution while using commercial LLMs for vital medical determinations, given that these tools may commonly produce inaccurate data, colloquially referred to as hallucinations or confabulations. Artificial intelligence-guided tools should undergo rigorous statistical evaluation-using methods such as gage R and R-before implementation into clinical settings.

Transformer-Based Regression Models for Assessing Reading Passage Complexity: A Deep Learning Approach in Natural Language Processing

Natural Language Processing (NLP) is a vital area in deep learning, widely applied in tasks like text classification, virtual assistants, speech recognition, and autocorrect features in digital devices. It allows machines to understand and generate human language, enhancing user interactions with software. This paper presents a deep learning model using the Transformer architecture for a regression task to predict the complexity of reading passages based on text excerpts. By leveraging the Transformer’s capability to identify complex patterns in text, the model achieves a relative error rate of about 10%. The paper also examines how different architectural choices influence model performance, focusing on one-hot encoding and embeddings. While one-hot encoding provides a simple text representation, embeddings offer a richer, more nuanced understanding of word relationships. The findings highlight the significance of model design and data representation in optimizing NLP tasks, providing insights for future advancements in the field.

ZNO-Eval: Benchmarking reasoning capabilities of large language models in Ukrainian

As the usage of large language models for problems outside of simple text understanding or generation increases, assessing their abilities and limitations becomes crucial. While significant progress has been made in this area over the last few years, most research has focused on benchmarking English, leaving other languages underexplored. This makes evaluating the reasoning and robustness level of language models in Ukrainian particularly challenging. The purpose of this work is to establish a comprehensive benchmark for the reasoning capabilities evaluation of large language models in the Ukrainian language. This paper presents the ZNO-Eval benchmark based on real exam tasks from Ukraine's standardized educational testing system: the External Independent Evaluation and the National Multi-subject Test. With single- answer options, multiple-choice, matching, and open-ended questions from diverse subjects, including Ukrainian language, mathematics, history, and geography, this dataset paves the way toward a thorough analysis of reasoning capabilities across different domains and complexities. Evaluation of several well-known language models, such as GPT-3.5-Turbo, GPT-4o, GPT-4-Turbo, Mistral Large, Claude 3 Opus, and Gemini-1.5 Pro on this benchmark demonstrated the superiority of GPT-4o in both common knowledge reasoning and intricate language tasks. At the same time, Gemini Pro and GPT-4 Turbo excelled in the arithmetic domain, leading in single-answer and open-ended math problems. While all models were close to max performance in text-only common knowledge tasks like history and geography, there still is a gap for Ukrainian language and math, thus highlighting the importance of developing specialized language benchmarks for more accurate assessments of model capabilities and limitations across different languages and contexts. This research introduced ZNO-Eval, an effective benchmark for evaluating reasoning capabilities, and thoroughly explored the abilities and limitations of modern solutions in the Ukrainian language. Future research should aim to expand the scope of ZNO-Eval to other modalities like images commonly used for exam problem description.

Health text simplification: An annotated corpus for digestive cancer education and novel strategies for reinforcement learning

Objective:The reading level of health educational materials significantly influences the understandability and accessibility of the information, particularly for minoritized populations. Many patient educational resources surpass widely accepted standards for reading level and complexity. There is a critical need for high-performing text simplification models for health information to enhance dissemination and literacy. This need is particularly acute in cancer education, where effective prevention and screening education can substantially reduce morbidity and mortality. Methods:We introduce Simplified Digestive Cancer (SimpleDC), a parallel corpus of cancer education materials tailored for health text simplification research, comprising educational content from the American Cancer Society, Centers for Disease Control and Prevention, and National Cancer Institute. The corpus includes 31 web pages with the corresponding manually simplified versions. It consists of 1183 annotated sentence pairs (361 train, 294 development, and 528 test). Utilizing SimpleDC and the existing Med-EASi corpus, we explore Large Language Model (LLM)-based simplification methods, including fine-tuning, reinforcement learning (RL), reinforcement learning with human feedback (RLHF), domain adaptation, and prompt-based approaches. Our experimentation encompasses Llama 2, Llama 3, and GPT-4. We introduce a novel RLHF reward function featuring a lightweight model adept at distinguishing between original and simplified texts when enables training on unlabeled data. Results:Fine-tuned Llama models demonstrated high performance across various metrics. Our RLHF reward function outperformed existing RL text simplification reward functions. The results underscore that RL/RLHF can achieve performance comparable to fine-tuning and improve the performance of fine-tuned models. Additionally, these methods effectively adapt out-of-domain text simplification models to a target domain. The best-performing RL-enhanced Llama models outperformed GPT-4 in both automatic metrics and manual evaluation by subject matter experts. Conclusion:The newly developed SimpleDC corpus will serve as a valuable asset to the research community, particularly in patient education simplification. The RL/RLHF methodologies presented herein enable effective training of simplification models on unlabeled text and the utilization of out-of-domain simplification corpora.

FICE: Text-conditioned fashion-image editing with guided GAN inversion

Fashion-image editing is a challenging computer-vision task where the goal is to incorporate selected apparel into a given input image. Most existing techniques, known as Virtual Try-On methods, deal with this task by first selecting an example image of the desired apparel and then transferring the clothing onto the target person. Conversely, in this paper, we consider editing fashion images with text descriptions. Such an approach has several advantages over example-based virtual try-on techniques: (i) it does not require an image of the target fashion item, and (ii) it allows the expression of a wide variety of visual concepts through the use of natural language. Existing image-editing methods that work with language inputs are heavily constrained by their requirement for training sets with rich attribute annotations or they are only able to handle simple text descriptions. We address these constraints by proposing a novel text-conditioned editing model called FICE (Fashion Image CLIP Editing) that is capable of handling a wide variety of diverse text descriptions to guide the editing procedure. Specifically, with FICE, we extend the common GAN-inversion process by including semantic, pose-related, and image-level constraints when generating images. We leverage the capabilities of the CLIP model to enforce the text-provided semantics, due to its impressive image–text association capabilities. We furthermore propose a latent-code regularization technique that provides the means to better control the fidelity of the synthesized images. We validate the FICE through rigorous experiments on a combination of VITON images and Fashion-Gen text descriptions and in comparison with several state-of-the-art, text-conditioned, image-editing approaches. Experimental results demonstrate that the FICE generates very realistic fashion images and leads to better editing than existing, competing approaches. The source code is publicly available from: https://github.com/MartinPernus/FICE.

Designing a Culturally Relevant Digital Skin Cancer Prevention Intervention for Hispanic Individuals: Qualitative Exploration.

In the past 2 decades, melanoma incidence among Hispanic individuals has risen by 20%. The mortality rate of Hispanic individuals is higher than that for non-Hispanic White individuals. Skin cancer can largely be prevented with regular sun protection, and skin cancer outcomes can be improved through early detection, for example, by skin self-examination. Alarmingly, Hispanic individuals are less aware of the symptoms and harms of skin cancers, tend to have misperceptions regarding the risks and benefits of skin cancer prevention behaviors, and engage in less sun protection behaviors than non-Hispanic White individuals. This study aimed to use a community-engaged approach and conduct both group and individual interviews among Hispanic individuals and relevant key stakeholders to explore the potential design of a mobile-based skin cancer prevention intervention for Hispanic individuals. This study used a qualitative design (focus groups and individual interviews). Participants were recruited from local community organizations' social media, local events, and contact lists (eg, email). Zoom interviews were conducted to examine whether Hispanic individuals would be interested in a mobile-based skin cancer intervention and to explore their preferences and suggestions to inform skin cancer prevention intervention design. Five focus groups (2 in Spanish and 3 in English) among self-identified Hispanic individuals (n=34) and 15 semistructured, in-depth individual interviews among key stakeholders (health care providers and community leaders; eg, dermatologist, nurse practitioner, licensed social worker, and church leader) were conducted. The main themes and subthemes emerging from the group discussions and individual interviews were organized into the following categories: intervention platform, delivery frequency and format, message design, engagement plan, and activities. WhatsApp and Facebook were identified as suitable platforms for the intervention. Messages including short videos, visuals (eg, images and photographs), and simple texts messages were preferred. Recommendations for message design included personalized messages, personal stories and narratives, culturally relevant design (eg, incorporating family values), and community-trusted sources. Potential engagement and retention recommendations were also discussed. Additional details and exemplar quotes of each theme and subtheme are described. This study provides important insights and directions for the design of a mobile, digital skin cancer intervention to modify Hispanic individuals' sun protection and skin self-examination behaviors to help improve skin cancer outcomes. Insights gathered from community leaders and health care providers provided valuable additions to the community-derived data. Leveraging popular digital platforms among Hispanic individuals such as WhatsApp or Facebook could be a promising approach to skin cancer prevention. Recommendations from the community included the use of concise videos, illustrative images, clear text messages, tailored communications, narratives featuring personal experiences, designs that reflect cultural significance, and information from sources that are trusted by the community, which provided useful strategies for future intervention design among Hispanic individuals.

Exploration and selection of LLM models for financial text simplification

This research is dedicated to the simplification of Spanish-language financial texts to enhance accessibility for screen readers. We present a qualitative and quantitative analysis of the text simplification process, employing a set of Spanish simplification rules and metrics. Our study evaluates the outcomes resulting from the application of three distinct financial datasets to four pre-trained models. The primary objective is to identify the most effective models for text simplification and determine those warranting further investment through fine-tuning and training. This study contributes to improving the accessibility and comprehensibility of financial documents for individuals with visual impairments.

Deep learning approaches to lexical simplification: A survey

AbstractLexical Simplification (LS) is the task of substituting complex words within a sentence for simpler alternatives while maintaining the sentence’s original meaning. LS is the lexical component of Text Simplification (TS) systems with the aim of improving accessibility to various target populations such as individuals with low literacy or reading disabilities. Prior surveys have been published several years before the introduction of transformers, transformer-based large language models (LLMs), and prompt learning that have drastically changed the field of NLP. The high performance of these models has sparked renewed interest in LS. To reflect these recent advances, we present a comprehensive survey of papers published since 2017 on LS and its sub-tasks focusing on deep learning. Finally, we describe available benchmark datasets for the future development of LS systems.

GCDkit.Mineral: A customizable, platform-independent R-language environment for recalculation, plotting, and classification of electron probe microanalyses of common rock-forming minerals

Abstract GCDkit.Mineral is a platform-independent (Windows/Mac/Linux) freeware for recalculation, plotting, and statistical treatment of mineral data obtained by microbeam techniques, typically an electron microprobe. It is written in R, a language providing a feature-rich environment for statistics and data visualization. This new program imports compositional data in various commonly used file formats or retrieves them from the clipboard. Routines are available for data management, i.e., grouping, searching, and generation of subsets, using regular expressions and Boolean logic. Raw compositional data (wt%) are recalculated to atoms per formula unit (apfu) based on a required number of O equivalents, atoms, or charges, with or without FeII/FeIII estimation by various methods. Analyses may then be recast to structural formulae, i.e., the atoms are distributed into appropriate crystallographic sites. For minerals forming solid solutions, the molar percentages of end-members are computed. All the data may be treated statistically, either by built-in functions for descriptive and multivariate statistics or using the wealth of tools provided by the wide R community. Raw and recalculated mineral data may be plotted on assorted binary and ternary plots and boxplots. Most are defined as internal templates that provide a means to make later changes to the plot (zooming and scaling, adding comments or legend, identifying data points, altering the size or color of the plotting symbols, etc.). The publication-ready graphics may be saved into several vector-(PostScript, PDF, and WMF) and bitmap-based (e.g., PNG, TIF, and JPG) formats, ready to be imported into a professional graphical, presentation, or desktop publishing software. Importantly, the graphical templates are used as a basis for classification. The general classification routine looks for the name of the polygon within the diagram (= graphical template), into which the analysis falls according to its x–y coordinates. The outcome may be either the name of a mineral or a link to another diagram in the case of more complex classification schemes. Following the rules of the International Mineralogical Association (IMA), in some cases, the classification is not done graphically but using prescribed algorithms. The class mechanism in R provides an elegant solution to the computational problems presented by the differing requirements of each mineral group. By assigning each mineral species to a particular class, all algorithms may be implemented as mutually independent but mineral group-specific methods. The default recalculation options for each mineral class are stored externally in a small and simple text file. The program is designed to cater to three potential user groups. For users with no familiarity with R, the program is fully menu-driven and contains embedded default recalculation options for many common rock-forming minerals. More experienced users may easily tweak these parameters, as they are saved in a logically structured plain text file. Seasoned R users may invoke GCDkit.Mineral in command line mode, use batch scripts or Python-driven notebooks (e.g., of project Jupyter), or modify and develop new recalculations or plugins. The lucid, open, and modular design thus makes GCDkit.Mineral a versatile workbench for everyday use, as well as a promising platform for community-driven development. The GCDkit family of R tools, including GCDkit. Mineral, is distributed through the WWW. The current version may be downloaded from http://mineral.gcdkit.org.

Artificial Intelligence in Multilingual Interpretation and Radiology Assessment for Clinical Language Evaluation (AI-MIRACLE).

The AI-MIRACLE Study investigates the efficacy of using ChatGPT 4.0, a large language model (LLM), for translating and simplifying radiology reports into multiple languages, aimed at enhancing patient comprehension. The study assesses the model's performance across the most spoken languages in the U.S., emphasizing the accuracy and clarity of translated and simplified radiology reports for non-medical readers. This study employed ChatGPT 4.0 to translate and simplify selected radiology reports into Vietnamese, Tagalog, Spanish, Mandarin, and Arabic. Hindi was used as a preliminary test language for validation of the questionnaire. Performance was assessed via Google form surveys distributed to bilingual physicians, which assessed the translation accuracy and clarity of simplified texts provided by ChatGPT 4. Responses from 24 participants showed mixed results. The study underscores the model's varying success across different languages, emphasizing both potential applications and limitations. ChatGPT 4.0 shows promise in breaking down language barriers in healthcare settings, enhancing patient comprehension of complex medical information. However, the performance is inconsistent across languages, indicating a need for further refinement and more inclusive training of AI models to handle diverse medical contexts and languages. The study highlights the role of LLMs in improving healthcare communication and patient comprehension, while indicating the need for continued advancements in AI technology, particularly in the translation of low-resource languages.

Does AI Simplification of Authentic Blog Texts Improve Reading Comprehension, Inferencing, and Anxiety? A One-Shot Intervention in Turkish EFL Context

This experimental study investigates the impact of ChatGPT-simplified authentic texts on university students’ reading comprehension, inferencing, and reading anxiety levels. A within-subjects design was employed, and 105 undergraduate English as a foreign language (EFL) students engaged in both original and ChatGPT-simplified text readings, serving as their own controls. The findings reveal a significant improvement in reading comprehension scores and inferencing scores following ChatGPT intervention. However, no significant change in reading anxiety levels was observed. Results suggest that ChatGPT simplification positively influences reading comprehension and inferencing, but its impact on reading anxiety remains inconclusive. This research contributes to literature on the use of artificial intelligence (AI) in education and sheds light on ChatGPT’s potential to influence language learning experiences within higher education contexts. The study highlights the practical application of ChatGPT as a tool for helping students engage in authentic text readings by making text more comprehensible. Based on the findings, several multifaceted implications that extend to various stakeholders in the field of language education are provided.