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A Large Language Model Workflow for Auditable Brain Abscess Risk Stratification and Pre-residency Scholarship: A Technical Report.

Large language models (LLMs) are increasingly available to medical trainees, but transparent and auditable methods for incorporating them into scholarly work and emerging artificial intelligence (AI) literacy curricula remain limited. This technical report describes a mentored educational framework in which a de-identified brain abscess case report was transformed into a reproducible, LLM-supported risk-stratification model to teach rigor, verification, and structured AI use to a fourth-year medical student. A single clinical case was reconstructed using structured variables from history, imag…

Improving sign Language recognition system for assisting deaf and dumb people using pathfinder algorithm with representation learning model.

For many individuals, communication through sign language (SL) is the primary means of interacting with the world, and the potential applications of effective SL Recognition (SLR) systems are vast and far-reaching. SLR is a research area dedicated to the automatic analysis of hand gestures and other visual signs used in communication among individuals with speech or hearing impairments. Despite significant advancements, the automated detection and interpretation of human signs remain a complex and multidisciplinary challenge that is yet to be fully addressed. Recently, various approaches have …

Associations between early motor milestones and speech-language abilities in 4 to 6-year-old children with DLD.

Developmental language disorder (DLD) is a neurodevelopmental disorder that affects speech and language abilities. However, studies have shown that this disorder is widely connected to other aspects of development. We conducted an observational study on the relationship between early motor milestones and speech-language development in 4 to 6-year-old children with DLD. We studied 30 children diagnosed with DLD and 30 typically developed children. To assess early motor milestones, parents provided written responses regarding their children's age at which they reached crawling and walking milest…

Evaluating Large Language Models for Sentiment Analysis and Hesitancy Analysis on Vaccine Posts From Social Media: Qualitative Study.

BACKGROUND: In the digital age, social media has become a crucial platform for public discourse on diverse health-related topics, including vaccines. Efficient sentiment analysis and hesitancy detection are essential for understanding public opinions and concerns. Large language models (LLMs) offer advanced capabilities for processing complex linguistic patterns, potentially providing valuable insights into vaccine-related discourse. OBJECTIVE: This study aims to evaluate the performance of various LLMs in sentiment analysis and hesitancy detection related to vaccine discussions on social med…

Apoprotein E epsilon4 allele ассоциируется с лучшим языком выступления и визуальной памятью в спинномозговой атаксии типа 3.

СПРАВОЧНАЯ ИНФОРМАЦИЯ: Регулирующая роль аполипопротеина E (APOE) 4 аллеля в клинических проявлениях спинномозговой атаксии типа 3 (SCA3) остается неясной. Цель этого исследования заключалась в оценке воздействия аллея APOE 4 на когнитивные и моторные функции пациентов SCA3. В этом исследовании приняли участие 281 пациент, не связанный с системой SCA3, и 182 пациента. Генотипы APOE были проанализированы с использованием PCR амплификации в сочетании с последовательностью Sanger. Кроме того, 96 пациентов SCA3 были набраны для оценки нейропсихологических и моторных функций. Нейропсихологические ф

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BACKGROUND: The regulatory role of the apolipoprotein E (APOE) ε4 allele in the clinical manifestations of spinocerebellar ataxia type 3 (SC … atients compared to the control group. The APOE ε4 allele was associated with better performance in language and visual memory, but also with more severe speech disturbances in SCA3 patients. Furthermore, in SCA3, the expanded CAG repeat length was correlated with poorer language memory performance and slower information processing speed, as well as more s …

Language Lateralization by Passive Auditory fMRI in Presurgical Assessment for Temporal Lobe Epilepsy: A Single-Center Retrospective Study.

Background: In temporal lobe epilepsy (TLE), estimating the potential risk of language dysfunction before surgery is a necessary procedure. Functional MRI (fMRI) is considered the most useful to determine language lateralization noninvasively. However, there are no standardized language fMRI protocols, and several issues remain unresolved. In particular, the language tasks normally used are predominantly active paradigms that require the overt participation of patients, making assessment difficult for pediatric patients or patients with intellectual disabilities. In this study, task-based fMRI…

Natural Language Processing Applied to Spontaneous Recall of Famous Faces Reveals Memory Dysfunction in Temporal Lobe Epilepsy Patients.

OBJECTIVE AND BACKGROUND: Epilepsy patients rank memory problems as their most significant cognitive comorbidity. Current clinical assessments are laborious to administer and score and may not always detect subtle memory decline. The Famous Faces Task (FF) has robustly demonstrated that left temporal lobe epilepsy (LTLE) patients remember fewer names and biographical details compared to right TLE (RTLE) patients and healthy controls (HCs). We adapted the FF task to capture subjects' entire spontaneous spoken recall, then scored responses using manual and natural language processing (NLP) metho…

Thyroid autoimmunity in euthyroid pregnant women is associated with slower productive language acquisition: the Odense child cohort study.

OBJECTIVE: Maternal thyroid autoimmunity and thyroid function in early pregnancy may impact fetal neurodevelopment. We aimed to investigate how thyroid autoimmunity and thyroid function in early pregnancy were associated with language acquisition in offspring at 12-36 months of age. METHODS: This study was embedded in the prospective Odense child cohort. Mother-child dyads were excluded in case of maternal intake of thyroid medication during pregnancy. The parents completed MacArthur-Bates Communicative Development Inventories (MB-CDI) every third month to assess their offspring's productive …

The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.

BACKGROUND: Large language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surgical management of gastroesophageal reflux disease (GERD). METHODS: Nine patient cases were created based on key questions addressed by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) guidelines for the surgical treatment of GERD. ChatGPT-3.5, ChatGPT-4, Copilot, Google Bard, and Perplexity AI were queried on November 16th…

Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study.

BACKGROUND: Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated. OBJECTIVE: This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing. METHODS: The study included 28 participants from the Boston University Alzheimer's Disease Research Center, all of whom engaged…

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study.

BACKGROUND: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs). OBJECTIVE: The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers. METHODS: We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirection…

Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System. METHODS: Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage. To that end, a clinician annotated MM stage within over 5,000…

Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm Development and Validation.

BACKGROUND: Clinical trials are vital for developing new therapies but can also delay drug development. Efficient trial data management, optimized trial protocol, and accurate patient identification are critical for reducing trial timelines. Natural language processing (NLP) has the potential to achieve these objectives. OBJECTIVE: This study aims to assess the feasibility of using data-driven approaches to optimize clinical trial protocol design and identify eligible patients. This involves creating a comprehensive eligibility criteria knowledge base integrated within electronic health recor…

The Correlation between Age at Implant, Consistent Device Use, and Language Outcomes for Children Implanted under 18 Months.

INTRODUCTION: Research suggests deaf children who receive cochlear implants (CIs) at an early age can achieve age-appropriate language. Recent studies suggest age at full-time CI use is a better predictor of language outcomes than age at implant. The aim of this study was to investigate whether there are correlations between age at implantation, establishing consistent device use, and language outcomes in a cohort of young children in Aotearoa New Zealand. METHODS: A retrospective analysis was conducted. The study included 46 participants, implanted by the Southern Cochlear Implant Programme …

Tracking persistent postoperative opioid use: a proof-of-concept study demonstrating a use case for natural language processing.

BACKGROUND: Large language models have been gaining tremendous popularity since the introduction of ChatGPT in late 2022. Perioperative pain providers should leverage natural language processing (NLP) technology and explore pertinent use cases to improve patient care. One example is tracking persistent postoperative opioid use after surgery. Since much of the relevant data may be 'hidden' within unstructured clinical text, NLP models may prove to be advantageous. The primary objective of this proof-of-concept study was to demonstrate the ability of an NLP engine to review clinical notes and ac…

Assessing Retrieval-Augmented Large Language Model Performance in Emergency Department ICD-10-CM Coding Compared to Human Coders.

BACKGROUND: Accurate medical coding is essential for clinical and administrative purposes but complicated, time-consuming, and biased. This study compares Retrieval-Augmented Generation (RAG)-enhanced LLMs to provider-assigned codes in producing ICD-10-CM codes from emergency department (ED) clinical records. METHODS: Retrospective cohort study using 500 ED visits randomly selected from the Mount Sinai Health System between January and April 2024. The RAG system integrated past 1,038,066 ED visits data (2021-2023) into the LLMs' predictions to improve coding accuracy. Nine commercial and open

Oral anticoagulant treatment in atrial fibrillation: the AFIRMA real-world study using natural language processing and machine learning.

INTRODUCTION: Oral anticoagulation (OAC) is key in atrial fibrillation (AF) thromboprophylaxis, but Spain lacks substantial real-world evidence. We aimed to analyze the prevalence, clinical characteristics, and treatment patterns among patients with AF undertaking OAC, using natural language processing (NLP) and machine learning (ML). MATERIALS AND METHODS: This retrospective study included AF patients on OAC from 15 Spanish hospitals (2014-2020). Using EHRead® (including NLP and ML), and SNOMED_CT, we extracted and analyzed patient demographics, comorbidities, and OAC treatment from electron…

Steering veridical large language model analyses by correcting and enriching generated database queries: first steps toward ChatGPT bioinformatics.

Large language models (LLMs) leverage factual knowledge from pretraining. Yet this knowledge remains incomplete and sometimes challenging to retrieve-especially in scientific domains not extensively covered in pretraining datasets and where information is still evolving. Here, we focus on genomics and bioinformatics. We confirm and expand upon issues with plain ChatGPT functioning as a bioinformatics assistant. Poor data retrieval and hallucination lead ChatGPT to err, as do incorrect sequence manipulations. To address this, we propose a system basing LLM outputs on up-to-date, authoritative f…

Leveraging human centered design to enhance clinician communication during pregnancy care: Overcoming language barriers with Spanish-speaking patients.

BACKGROUND: Engaging patients in quality improvement and innovation projects is increasingly important, yet challenges persist with involving patients who speak languages other than English. This article presents design activities our team used to engage Spanish-speaking patients and cultural brokers. OBJECTIVE: To develop a clinician communication tool to enhance patient trust in pregnancy care clinicians, especially among minoritized populations who face language and cultural barriers, using human-centered design (HCD). PATIENT INVOLVEMENT AND INNOVATION: We centered end-user experiences, …