Advances in natural language processing

Patient Voices in Dialysis Care: Sentiment Analysis and Topic Modeling Study of Social Media Discourse

BACKGROUND

Patients with end-stage kidney disease undergoing dialysis face significant physical, psychological, and social challenges that impact their quality of life. Social media platforms such as X (formerly known as Twitter) have become important outlets for these patients to share experiences and exchange information.

OBJECTIVE

This study aimed to uncover key themes, emotions, and challenges expressed by the dialysis community on X from April 2006 to August 2024 by leveraging natural language processing techniques, specifically sentiment analysis and topic modeling.

METHODS

We collected 12,976 publicly available X posts related to dialysis using the platform's application programming interface version 2 and Python's Tweepy library. After rigorous preprocessing, 58.13% (7543/12,976) of the posts were retained for analysis. Sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner (VADER) model, which is a rule-based sentiment analyzer specifically attuned to social media content, classified the emotional tone of posts. VADER uses a human-curated lexicon that maps lexical features to sentiment scores, considering punctuation, capitalization, and modifiers. For topic modeling, posts with <50 tokens were removed, leaving 53.81% (4059/7543) of the posts, which were analyzed using latent Dirichlet allocation with coherence score optimization to identify the optimal number of topics (k=8). The analysis pipeline was implemented using Python's Natural Language Toolkit, Gensim, and scikit-learn libraries, with hyperparameter tuning to maximize model performance.

RESULTS

Sentiment analysis revealed 49.2% (3711/7543) positive, 26.2% (1976/7543) negative, and 24.7% (1863/7543) neutral sentiment posts. Latent Dirichlet allocation topic modeling identified 8 key thematic clusters: medical procedures and outcomes (722/4059, 17.8% prevalence), daily life impact (666/4059, 16.4%), risks and complications (621/4059, 15.3%), patient education and support (544/4059, 13.4%), health care access and costs (499/4059, 12.3%), symptoms and side effects (442/4059, 10.9%), patient experiences and socioeconomic challenges (406/4059, 10%), and diet and fluid management (162/4059, 4%). Cross-analysis of topics and sentiment revealed that negative sentiment was highest for daily life impact (580/666, 87.1%) and socioeconomic challenges (145/406, 35.8%), whereas the education and support topic exhibited more positive sentiment (250/544, 46%). Topic coherence scores ranged from 0.38 to 0.52, with the medical procedures topic showing the highest semantic coherence. Intertopic distance mapping via multidimensional scaling revealed conceptual relationships between identified themes, with lifestyle impact and socioeconomic challenges clustering closely. Our longitudinal analysis demonstrated evolving discourse patterns, with technology-related discussions increasing by 24% in recent years, whereas financial concerns remained consistently prominent.

CONCLUSIONS

This study provides a comprehensive, data-driven understanding of the complex lived experiences of patients undergoing dialysis shared on social media. The findings underscore the need for more holistic, patient-centered care models and policies that address the multidimensional challenges illuminated by patients' voices.

Patient-Centered Research Through Artificial Intelligence to Identify Priorities in Cancer Care

Importance

Patient-centered research is essential for bridging the gap between research and patient care, yet patient perspectives are often inadequately represented in health research.

Objective

To leverage artificial intelligence (AI) and natural language processing (NLP) to analyze a large dataset of patient messages, defining patient concerns and generating relevant research topics, and to quantify the quality of these AI-generated topics.

Design, Setting, and Participants

This case series was conducted using an automated framework involving a 2-staged unsupervised NLP topic model and AI-generated research topic suggestions. The study was based on deidentified patient portal message data from individuals with breast or skin cancer at Stanford Health Care and 22 affiliated centers over July 2013 to April 2024.

Exposures

A widely used large language model (ChatGPT-4o [OpenAI]; April 2024) was used and guided through multiple prompt-engineering strategies to perform multilevel tasks, including knowledge interpretation and summarization (eg, interpreting and summarizing the NLP-defined topics), knowledge generation (eg, generating research ideas corresponding to patients' issues), self-reflection and correction (eg, ensuring and revising the research ideas after searching for scientific articles), and self-reassurance (eg, confirming and finalizing the research ideas).

Main Outcomes and Measures

Three breast oncologists (J.L.C., A.W.K., F.R) and 3 dermatologists (K.Y.S, J.Y.T., E.L.) evaluated the meaningfulness and novelty of the AI-generated research topics using a 5-point Likert scale (1 representing exceptional to 5 representing poor). Mean (SD) scores for meaningfulness and novelty were computed for each topic.

Results

A total of 614 464 patient messages were analyzed from 25 549 individuals, 10 665 with breast cancer (98.6% female) and 14 884 had skin cancer (49.0% female). The overall mean (SD) scores for meaningfulness and novelty were 3.00 (0.50) and 3.29 (0.74), respectively, for breast cancer topics and 2.67 (0.45) and 3.09 (0.68), respectively, for skin cancer topics. One-third of the AI-suggested research topics were highly meaningful and novel when both scores were lower than the average (5 of 15 for breast cancer and 6 of 15 for skin cancer). Notably, two-thirds of the AI-suggested topics were novel (10 of 15 for breast cancer and 11 of 15 for skin cancer).

Conclusions and Relevance

This case series demonstrates that AI/NLP-driven analysis of large volumes of patient messages can generate quality research topics in cancer care that reflect patient perspectives, providing valuable guidance for future patient-centered health research endeavors.

Development and Systematic Evaluation of a Progressive Web Application for Women With Cardiac Pain: Usability Study

BACKGROUND

Cardiac pain has been widely considered to be the primary indicator of coronary artery disease. The presentation of cardiac pain and associated symptoms vary in women, making it challenging to interpret as cardiac, possibly cardiac, or noncardiac. Women prefer to consult with family and friends instead of seeking immediate medical care.

OBJECTIVE

This study aimed to assess the user performance (ie, ease of use, efficiency, and errors) and user satisfaction (System Usability Scale; SUS) of a progressive web application for women with cardiac pain.

METHODS

Following ethics approval, a purposive sample of women aged >18 years with cardiac pain or associated symptoms lasting >3 months and able to speak and read English was recruited to participate in 2 iterative usability testing cycles. The first cycle assessed the performance of and satisfaction with at heart using a web application, and the second cycle assessed the performance of and satisfaction with at heart across various Android and iOS devices. In total, 2 investigators recorded user comments and documented problems. At the end of the testing session, the participants completed the SUS and 4 semistructured interview questions.

RESULTS

In total, 10 eligible women participated in usability testing from March 31, 2020, to April 17, 2020 (cycle 1), and from November 17, 2020, to November 30, 2020 (cycle 2). Women across usability testing cycles had a mean age of 55.6 (SD 7.3) years, and most (9/10, 90%) were well educated. In total, 50% (5/10) were employed full or part time, and 60% (6/10) earned >CAD $70,000 (US $48,881.80) annually. Participants across 2 testing cycles reported the overall usability of the at heart progressive web application as highly acceptable (mean SUS score 81.75, SD 10.41). In total, 90% (9/10) of participants rated the user-friendliness of at heart as good or excellent. All participants (10/10, 100%) thought at heart was easy to use and efficient. Only 2 testing errors were noted as high priority; these were low contrast or small font and clarification that the chatbot was not a real person. User satisfaction was assessed using themes that emerged from the debrief and 4 semistructured interview questions; at heart was engaging, comprehensive, understandable, credible, relevant, affirming, personalized, and innovative.

CONCLUSIONS

This study provides initial support for the at heart progressive web application for women living with cardiac pain and symptoms. Ongoing evaluations in phases 3 and 4 should aim to examine the feasibility and acceptability of and the extent of engagement with the at heart core feature set: Heart Check, Wellness Check, and the library. In addition to assessing effectiveness in the phase-4 effectiveness-implementation hybrid trial (type I), describing and better understanding the context for implementation (eg, race and ethnicity and geography) will be necessary.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2019-033092.

Using Natural Language Processing to Track Negative Emotions in the Daily Lives of Adolescents

Tracking emotion fluctuations in adolescents' daily lives is essential for understanding mood dynamics and identifying early markers of affective disorders. This study examines the potential of text-based approaches for emotion prediction by comparing nomothetic (group-level) and idiographic (individualized) models in predicting adolescents' daily negative affect (NA) from text features. Additionally, we evaluate different Natural Language Processing (NLP) techniques for capturing within-person emotion fluctuations. We analyzed ecological momentary assessment (EMA) text responses from 97 adolescents (ages 14-18, 77.3% female, 22.7% male, NEMA=7,680). Text features were extracted using a dictionary-based approach, topic modeling, and GPT-derived emotion ratings. Random Forest and Elastic Net Regression models predicted NA from these text features, comparing nomothetic and idiographic approaches. All key findings, interactive visualizations, and model comparisons are available via a companion web app: https://emotracknlp.streamlit.app/. Idiographic models combining text features from different NLP approaches exhibited the best performance: they performed comparably to nomothetic models in R2 but yielded lower prediction error (Root Mean Squared Error), improving within-person precision. Importantly, there were substantial between-person differences in model performance and predictive linguistic features. When selecting the best-performing model for each participant, significant correlations between predicted and observed emotion scores were found for 90.7-94.8% of participants. Our findings suggest that while nomothetic models offer initial scalability, idiographic models may provide greater predictive precision with sufficient within-person data. A flexible, personalized approach that selects the optimal model for each individual may enhance emotion monitoring, while leveraging text data to provide contextual insights that could inform appropriate interventions.

Evolutionary Trend of Dental Health Care Information on Chinese Social Media Platforms During 2018-2022: Retrospective Observational Study

BACKGROUND

Social media holds an increasingly significant position in contemporary society, wherein evolving public perspectives are mirrored by changing information. However, there remains a lack of comprehensive analysis regarding the nature and evolution of dental health care information on Chinese social media platforms (SMPs) despite extensive user engagement and voluminous content.

OBJECTIVE

This study aimed to probe into the nature and evolution of dental health care information on Chinese SMPs from 2018 to 2022, providing valuable insights into the evolving digital public perception of dental health for dental practitioners, investigators, and educators.

METHODS

This study was conducted on 3 major Chinese SMPs: Weibo, WeChat, and Zhihu. Data from March 1 to 31 in 2018, 2020, and 2022 were sampled to construct a social media original database (ODB), from which the most popular long-text posts (N=180) were selected to create an analysis database (ADB). Natural language processing (NLP) tools were used to assist tracking topic trends, and word frequencies were analyzed. The DISCERN health information quality assessment questionnaire was used for information quality evaluation.

RESULTS

The number of Weibo posts in the ODB increased approximately fourfold during the observation period, with discussion of orthodontic topics showing the fastest growth, surpassing that of general dentistry after 2020. In the ADB, the engagement of content on Weibo and Zhihu also displayed an upward trend. The overall information quality of long-text posts on the 3 platforms was moderate or low. Of the long-text posts, 143 (79.4%) were written by nonprofessionals, and 105 (58.3%) shared personal medical experiences. On Weibo and WeChat, long-text posts authored by health care professionals had higher DISCERN scores (Weibo P=.04; WeChat P=.02), but there was a negative correlation between engagement and DISCERN scores (Weibo tau-b [τb]=-0.45, P=.01; WeChat τb=-0.30, P=.02).

CONCLUSIONS

There was a significant increase in the dissemination and evolution of public interest in dental health care information on Chinese social media during 2018-2022. However, the quality of the most popular long-text posts was rated as moderate or low, which may mislead patients and the public.

MulAFNet: Integrating Multiple Molecular Representations for Enhanced Property Prediction

In computer-aided drug design, molecular representation plays a crucial role. Most existing multimodal approaches primarily perform simple concatenation of various feature representations, without adequately emphasizing effective integration among these features. To address this issue, this study proposes a network framework that integrates multimodal representations using a multihead attention flow (MulAFNet). MulAFNet utilizes SMILES string representation and two levels of molecular graph representations: atom-level and functional group-level graph structure. Pretraining tasks are established for each of these three representations, which are then fused in downstream tasks to predict molecular properties. The experiments were conducted on six classification data sets and three regression data sets, demonstrating that the use of multiple molecular representations as input has a significant impact on the results. In particular, the excellent performance of our fusion method in molecular property prediction outperforms other state-of-the-art methods, proving its superiority. Additionally, comparative experiments on fusion methods and ablation studies, further validate the effectiveness of MulAFNet. The results demonstrate that multiple molecular feature representations provide a more comprehensive molecular understanding, and appropriate pretraining tasks enhance molecular property prediction.

Throw out an oligopeptide to catch a protein: Deep learning and natural language processing-screened tripeptide PSP promotes Osteolectin-mediated vascularized bone regeneration

Angiogenesis is imperative for bone regeneration, yet the conventional cytokine therapies have been constrained by prohibitive costs and safety apprehensions. It is urgent to develop a safer and more efficient therapeutic alternative. Herein, utilizing the methodologies of Deep Learning (DL) and Natural Language Processing (NLP), we proposed a paradigm algorithm that amalgamates Word2vec with a TF-IDF variant, TF-IIDF, to deftly discern potential pro-angiogenic peptides from intrinsically disordered regions (IDRs) of 262 related proteins, where are fertile grounds for developing safer and highly promising bioactive peptides. After the evaluation of the candidate oligopeptides, one tripeptide, PSP, emerged as particularly notable for its exceptional ability to stimulate the vascularization of endothelial cells (ECs), enhance vascular-osteo communication, and then boost the osteogenic differentiation of bone marrow stem cells (BMSCs), evidenced in mouse critical-sized cranial model. Moreover, we found that PSP serves as a 'priming' agent, activating the body's innate ability to produce Osteolectin (Oln) - prompting ECs to release small extracellular vesicles (sEVs) enriched with Oln to facilitate bone formation. In summary, our study established a precise and efficient composite model of DL and NLP to screen bioactive peptides, opening an avenue for the development of various peptide-based therapeutic strategies applicable to a broader range of diseases.

When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges

Pre-trained large language models (LLMs) exhibit powerful capabilities for generating natural text. Evolutionary algorithms (EAs) can discover diverse solutions to complex real-world problems. Motivated by the common collective and directionality of text generation and evolution, this paper first illustrates the conceptual parallels between LLMs and EAs at a micro level, which includes multiple one-to-one key characteristics: token representation and individual representation, position encoding and fitness shaping, position embedding and selection, Transformers block and reproduction, and model training and parameter adaptation. These parallels highlight potential opportunities for technical advancements in both LLMs and EAs. Subsequently, we analyze existing interdisciplinary research from a macro perspective to uncover critical challenges, with a particular focus on evolutionary fine-tuning and LLM-enhanced EAs. These analyses not only provide insights into the evolutionary mechanisms behind LLMs but also offer potential directions for enhancing the capabilities of artificial agents.

Automated detection of cervical spondylotic myelopathy: harnessing the power of natural language processing

Background

The objective of this study was to develop machine learning (ML) algorithms utilizing natural language processing (NLP) techniques for the automated detection of cervical spondylotic myelopathy (CSM) through the analysis of positive symptoms in free-text admission notes. This approach enables the timely identification and management of CSM, leading to optimal outcomes.

Methods

The dataset consisted of 1,214 patients diagnosed with cervical diseases as their primary condition between June 2013 and June 2020. A random ratio of 7:3 was employed to partition the dataset into training and testing subsets. Two machine learning models, Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short Term Memory Network (LSTM), were developed. The performance of these models was assessed using various metrics, including the Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC), accuracy, precision, recall, and F1 score.

Results

In the testing set, the LSTM achieved an AUC of 0.9025, an accuracy of 0.8740, a recall of 0.9560, an F1 score of 0.9122, and a precision of 0.8723. The LSTM model demonstrated superior clinical applicability compared to the XGBoost model, as evidenced by calibration curves and decision curve analysis.

Conclusions

The timely identification of suspected CSM allows for prompt confirmation of diagnosis and treatment. The utilization of NLP algorithm demonstrated excellent discriminatory capabilities in identifying CSM based on positive symptoms in free-text admission notes complaint data. This study showcases the potential of a pre-diagnosis system in the field of spine.

A comparative analysis of large language models on clinical questions for autoimmune diseases

Background

Artificial intelligence (AI) has made great strides. To explore the potential of Large Language Models (LLMs) in providing medical services to patients and assisting physicians in clinical practice, our study evaluated the performance in delivering clinical questions related to autoimmune diseases.

Methods

46 questions related to autoimmune diseases were input into ChatGPT 3.5, ChatGPT 4.0, and Gemini. The responses were then evaluated by rheumatologists based on five quality dimensions: relevance, correctness, completeness, helpfulness, and safety. Simultaneously, the responses were assessed by laboratory specialists across six medical fields: concept, clinical features, report interpretation, diagnosis, prevention and treatment, and prognosis. Finally, statistical analysis and comparisons were performed on the performance of the three chatbots in the five quality dimensions and six medical fields.

Results

ChatGPT 4.0 outperformed both ChatGPT 3.5 and Gemini across all five quality dimensions, with an average score of 199.8 ± 10.4, significantly higher than ChatGPT 3.5 (175.7 ± 16.6) and Gemini (179.1 ± 11.8) (p = 0.009 and p = 0.001, respectively). The average performance differences between ChatGPT 3.5 and Gemini across these five dimensions were not statistically significant. Specifically, ChatGPT 4.0 demonstrated superior performance in relevance (p < 0.0001, p < 0.0001), completeness (p < 0.0001, p = 0.0006), correctness (p = 0.0001, p = 0.0002), helpfulness (p < 0.0001, p < 0.0001), and safety (p < 0.0001, p = 0.0025) compared to both ChatGPT 3.5 and Gemini. Furthermore, ChatGPT 4.0 scored significantly higher than both ChatGPT 3.5 and Gemini in medical fields such as report interpretation (p < 0.0001, p = 0.0025), prevention and treatment (p < 0.0001, p = 0.0103), prognosis (p = 0.0458, p = 0.0458).

Conclusions

This study demonstrates that ChatGPT 4.0 significantly outperforms ChatGPT 3.5 and Gemini in addressing clinical questions related to autoimmune diseases, showing notable advantages across all five quality dimensions and six clinical domains. These findings further highlight the potential of large language models in enhancing healthcare services.

Applications of Artificial Intelligence in Medical Education: A Systematic Review

Artificial intelligence (AI) models, like Chat Generative Pre-Trained Transformer (OpenAI, San Francisco, CA), have recently gained significant popularity due to their ability to make autonomous decisions and engage in complex interactions. To fully harness the potential of these learning machines, users must understand their strengths and limitations. As AI tools become increasingly prevalent in our daily lives, it is essential to explore how this technology has been used so far in healthcare and medical education, as well as the areas of medicine where it can be applied. This paper systematically reviews the published literature on the PubMed database from its inception up to June 6, 2024, focusing on studies that used AI at some level in medical education, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Several papers identified where AI was used to generate medical exam questions, produce clinical scripts for diseases, improve the diagnostic and clinical skills of students and clinicians, serve as a learning aid, and automate analysis tasks such as screening residency applications. AI shows promise at various levels and in different areas of medical education, and our paper highlights some of these areas. This review also emphasizes the importance of educators and students understanding AI's principles, capabilities, and limitations before integration. In conclusion, AI has potential in medical education, but more research needs to be done to fully explore additional areas of applications, address the current gaps in knowledge, and its future potential in training healthcare professionals.

Moving towards the use of artificial intelligence in pain management

BACKGROUND AND OBJECTIVE

While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.

DATABASES AND DATA TREATMENT

This review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management.

RESULTS

From the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image-guidance for procedural interventions and self-management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems.

CONCLUSION

There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI.

SIGNIFICANCE

This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.

Inspired Spine Smart Universal Resource Identifier (SURI): An Adaptive AI Framework for Transforming Multilingual Speech Into Structured Medical Reports

Medical documentation is a major part of delivering healthcare worldwide and is gaining more importance in developing countries as well. The global spread of multilingual communities in medical documentation poses unique challenges, particularly regarding maintaining accuracy and consistency across diverse languages. Inspired Spine Smart Universal Resource Identifier (SURI), an adaptive artificial intelligence (AI) framework, addresses these challenges by transforming multilingual speech into structured medical reports. Utilizing state-of-the-art automatic speech recognition (ASR) and natural language processing (NLP) technologies, SURI converts doctor-patient dialogues into detailed clinical documentation. This paper presents SURI's development, focusing on its multilingual capabilities, effective report generation, and continuous improvement through real-time feedback. Our evaluation indicates a 60% reduction in documentation errors and a 70% decrease in time spent on medical reporting compared to traditional methods. SURI not only provides a practical solution to a pressing issue in healthcare but also sets a benchmark for integrating AI into medical communication workflows.

Development and feasibility testing of an AI-powered chatbot for early detection of caregiver burden: protocol for a mixed methods feasibility study

Introduction

Caregivers of patients with end-stage kidney disease (ESKD) face significant challenges that contribute to caregiver burden, negatively impacting their physical, psychological, social, and financial well-being. With the growing prevalence of chronic diseases and an aging population, there is an urgent need for accessible and scalable solutions to detect and address caregiver burden. Artificial Intelligence (AI) chatbots using natural language processing (NLP) have shown promise in providing mental health support and monitoring through natural conversations. This study will contribute to research and clinical practice by: (1) validating a novel approach for early detection of caregiver burden through NLP, (2) analyzing the feasibility of AI-powered chatbots for continuous caregiver monitoring, and (3) informing the development of scalable, accessible tools to identify at-risk caregivers.

Methods and analysis

This protocol for the mixed methods aims to evaluate the feasibility, acceptability, and preliminary effectiveness of BOTANIC (Burden Observation and Timely Aid for Navigating Informal Caregiving), an AI-powered chatbot for early detection of caregiver burden. A single-center validation study will be conducted at Alexandra Hospital, Singapore. Twenty primary caregivers of ESKD patients will be recruited to use BOTANIC for 12 weeks. BOTANIC, developed using Python and open-source libraries, will integrate with Telegram and utilize advanced NLP techniques to analyze caregiver conversations and detect signs of burden. The NLP algorithm will analyze conversations to generate burden scores at baseline and at 12 weeks. Participants will also complete baseline and 12-week assessments using validated questionnaires including the Zarit Burden Interview (ZBI), Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder-7 (GAD-7). Primary outcomes include concordance between caregiver burden levels detected by the NLP algorithm and validated assessment scores at both timepoints. Secondary outcomes include user engagement metrics and system satisfaction. Semi-structured interviews will explore participants' experiences with the chatbot. Quantitative data will be analyzed using descriptive statistics and appropriate statistical tests such as paired t-tests or Wilcoxon signed-rank tests, while qualitative data will undergo thematic analysis.

Ethics and dissemination

The study has been approved by the NHG Domain Specific Review Board. Findings will be published in peer-reviewed journals, presented at conferences, and used to inform the development of larger-scale trials of AI-powered caregiver support interventions.

How much can we save by applying artificial intelligence in evidence synthesis? Results from a pragmatic review to quantify workload efficiencies and cost savings

Introduction

Researchers are increasingly exploring the use of artificial intelligence (AI) tools in evidence synthesis, a labor-intensive, time-consuming, and costly effort. This review explored and quantified the potential efficiency benefits of using automated tools as part of core evidence synthesis activities compared with human-led methods.

Methods

We searched the MEDLINE and Embase databases for English-language articles published between 2012 and 14 November 2023, and hand-searched the ISPOR presentations database (2020-2023) for articles presenting quantitative results on workload efficiency in systematic literature reviews (SLR) when AI automation tools were utilized. Data on efficiencies (time- and cost-related) were collected.

Results

We identified 25 eligible studies: 13 used machine learning, 10 used natural language processing, and once each used a systematic review automation tool and a non-specified AI tool. In 17 studies, a >50% time reduction was observed, with 5-to 6-fold decreases in abstract review time. When the number of abstracts reviewed was examined, decreases of 55%-64% were noted. Studies examining work saved over sampling at 95% recall reported 6- to 10-fold decreases in workload with automation. No studies quantified the economic impact associated with automation, although one study found that there was an overall labor reduction of >75% over manual methods during dual-screen reviews.

Discussion

AI can reduce both workload and create time efficiencies when applied to evidence gathering efforts in SLRs. These improvements can facilitate the implementation of novel approaches in decision making that consider the real-life value of health technologies. Further research should quantify the economic impact of automation in SLRs.

Digitalomics: Towards Artificial Intelligence / Machine Learning-Based Precision Cardiovascular Medicine

Recent advances in traditional "-omics" technologies have provided deeper insights into cardiovascular diseases through comprehensive molecular profiling. Accordingly, digitalomics has emerged as a novel transdisciplinary concept that integrates multimodal information with digitized physiological data, medical imaging, environmental data, electronic health records, environmental records, and biometric data from wearables. This digitalomics-driven augmented multiomics approach can provide more precise personalized health risk assessments and optimization when combined with conventional multiomics approaches. Artificial intelligence and machine learning (AI/ML) technologies, alongside statistical methods, serve as key comprehensive analytical tools in realizing this comprehensive framework. This review focuses on two promising AI/ML applications in cardiovascular medicine: digital phonocardiography (PCG) and AI text generators. Digital PCG uses AI/ML models to objectively analyze heart sounds and predict clinical parameters, potentially surpassing traditional auscultation capabilities. In addition, large language models, such as generative pretrained transformer, have demonstrated remarkable performance in assessing medical knowledge, achieving accuracy rates exceeding 80% in medical licensing examinations, although there are issues regarding knowledge accuracy and safety. Current challenges to the implementation of these technologies include maintaining up-to-date medical knowledge and ensuring consistent accuracy of outputs, but ongoing developments in fine-tuning and retrieval-augmented generation show promise in addressing these challenges. Integration of AI/ML technologies in clinical practice, guided by appropriate validation and implementation strategies, may notably advance precision cardiovascular medicine through the digitalomics framework.

What's on the agenda? Examining public health communication about opioids

The way media portray public health problems influences the public's perception of problems and related solutions. Social media allows users to engage with news and to collectively construct meaning. This paper examined news in comparison to user-generated content related to opioids to understand the role of second-level agenda-setting in public health. We analyzed 162,760 tweets about the opioid crisis, and compared the main topics and their sentiments with 2998 opioid stories from The New York Times online. Evidence from this study suggests that second-level agenda setting on social media is different from the news; public communication about opioids on X/Twitter highlights attributes that are different from the ones highlighted in news. The findings suggest that public health communication should strategically utilize social media data, including obtaining consumer insight from personal tweets, listening to diverse views and warning signs from issue tweets, and tuning to the media for policy trends.

Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery

Alzheimer's disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from the traditionally dominant amyloid hypothesis toward a multifactorial understanding of the disease. Emerging evidence suggests that while amyloid-beta (Aβ) accumulation is central to AD, it may not be the primary driver but rather part of a broader pathogenic process. Novel hypotheses have been proposed, including the role of tau protein abnormalities, mitochondrial dysfunction, and chronic neuroinflammation. Additionally, the gut-brain axis and epigenetic modifications have gained attention as potential contributors to AD progression. The limitations of existing therapies underscore the need for innovative strategies. This study explores the integration of machine learning (ML) in drug discovery to accelerate the identification of novel targets and drug candidates. ML offers the ability to navigate AD's complexity, enabling rapid analysis of extensive datasets and optimizing clinical trial design. The synergy between these themes presents a promising future for more effective AD treatments.

Groundbreaking Technologies and the Biocontrol of Fungal Vascular Plant Pathogens

This review delves into innovative technologies to improve the control of vascular fungal plant pathogens. It also briefly summarizes traditional biocontrol approaches to manage them, addressing their limitations and emphasizing the need to develop more sustainable and precise solutions. Powerful tools such as next-generation sequencing, meta-omics, and microbiome engineering allow for the targeted manipulation of microbial communities to enhance pathogen suppression. Microbiome-based approaches include the design of synthetic microbial consortia and the transplant of entire or customized soil/plant microbiomes, potentially offering more resilient and adaptable biocontrol strategies. Nanotechnology has also advanced significantly, providing methods for the targeted delivery of biological control agents (BCAs) or compounds derived from them through different nanoparticles (NPs), including bacteriogenic, mycogenic, phytogenic, phycogenic, and debris-derived ones acting as carriers. The use of biodegradable polymeric and non-polymeric eco-friendly NPs, which enable the controlled release of antifungal agents while minimizing environmental impact, is also explored. Furthermore, artificial intelligence and machine learning can revolutionize crop protection through early disease detection, the prediction of disease outbreaks, and precision in BCA treatments. Other technologies such as genome editing, RNA interference (RNAi), and functional peptides can enhance BCA efficacy against pathogenic fungi. Altogether, these technologies provide a comprehensive framework for sustainable and precise management of fungal vascular diseases, redefining pathogen biocontrol in modern agriculture.

Improving care for amyotrophic lateral sclerosis with artificial intelligence and affective computing

BACKGROUND

Patients with ALS often face difficulties expressing emotions due to impairments in facial expression, speech, body language, and cognitive function. This study aimed to develop non-invasive AI tools to detect and quantify emotional responsiveness in ALS patients, providing objective insights. Improved understanding of emotional responses could enhance patient-provider communication, telemedicine effectiveness, and clinical trial outcome measures.

METHODS

In this preliminary exploratory study, fourteen patients with ALS had audio recordings performed during routine clinic visits while wearing a wireless pulse oximeter. Emotion-triggering questions related to symptom progression, breathing, mobility, feeding tube, and financial burden were randomly asked. The same questions were posed in separate psychiatric evaluations. Natural language processing (NLP) was used to analyze transcriptions, topic classifications, sentiment, and emotional states, combining pulse and speech data. AI-generated reports summarized the findings.

RESULTS

Pulse alterations consistent with emotional arousal were identified, with longer consultations and positive communication reducing pulse fluctuations. Financial concerns triggered the strongest emotional response, while discussions about breathing, mobility, and feeding tube increased anxiety. AI-generated reports prioritized patient concerns and streamlined documentation for providers.

CONCLUSIONS

This study introduces a novel approach to linking pulse and speech analysis to evaluate emotional responses in ALS patients. AI and affective computing provide valuable insights into emotional responses and disease progression, with potential applications for other neurological disorders. This approach could augment clinical trial outcomes by offering a more comprehensive view of patient well-being.

Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges

Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our review highlights the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration. However, key research gaps remain. These include the need for better incorporation of realistic decision-making considerations, expanded exploration of diverse datasets, and further investigation into biological and socio-behavioral mechanisms. Addressing these gaps will unlock the synergistic potential of AI and mechanistic modeling to enhance understanding of disease dynamics and support more effective public health planning and response.

Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries

This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions. Key parameters, including spray penetration, droplet size distribution, and evaporation rates, are modeled and validated against experimental data. The findings reveal that nanoparticle-enhanced fuels, coupled with LSTM-based predictive analytics, lead to superior combustion performance and lower pollutant formation. This interdisciplinary approach provides a robust framework for designing next-generation CI engines with improved efficiency and sustainability. Diesel engine performance and emissions were found to be influenced by variations in combustion chamber geometry, underwent validation through simulation using Diesel-RK. Re-entrant bowl profile in quaternary blend is found to exhibit 31.3% higher BTE and 8.65% lowered BSFC than the conventional HCC bowl at full load condition. Emission wise, re-entrant bowl induced 90.16% lowered CO, 59.95% lowered HC and 15.48% lowered smoke owing to improved spray penetration and faster burning of soot precursors. However, the NOx emissions of DBOPN-TRCC were found to be higher. The simulation outcomes, derived from Diesel-RK, were subsequently compared with empirical data obtained from real-world experiments. These experiments were systematically carried out under identical operating conditions, employing different piston bowl geometries.

Artificial Intelligence Methods in Infection Biology Research

Despite unprecedented achievements, the domain-specific application of artificial intelligence (AI) in the realm of infection biology was still in its infancy just a couple of years ago. This is largely attributable to the proneness of the infection biology community to shirk quantitative techniques. The so-called "sorting machine" paradigm was prevailing at that time, meaning that AI applications were primarily confined to the automation of tedious laboratory tasks. However, fueled by the severe acute respiratory syndrome coronavirus 2 pandemic, AI-driven applications in infection biology made giant leaps beyond mere automation. Instead, increasingly sophisticated tasks were successfully tackled, thereby ushering in the transition to the "Swiss army knife" paradigm. Incentivized by the urgent need to subdue a raging pandemic, AI achieved maturity in infection biology and became a versatile tool. In this chapter, the maturation of AI in the field of infection biology from the "sorting machine" paradigm to the "Swiss army knife" paradigm is outlined. Successful applications are illustrated for the three data modalities in the domain, that is, images, molecular data, and language data, with a particular emphasis on disentangling host-pathogen interactions. Along the way, fundamental terminology mentioned in the same breath as AI is elaborated on, and relationships between the subfields these terms represent are established. Notably, in order to dispel the fears of infection biologists toward quantitative methodologies and lower the initial hurdle, this chapter features a hands-on guide on software installation, virtual environment setup, data preparation, and utilization of pretrained models at its very end.

The Utilization of Natural Language Processing for Analyzing Social Media Data in Nursing Research: A Scoping Review

Aim: This scoping review aimed to identify and synthesize the evidence in existing nursing studies that used natural language processing to analyze social media data, and the relevant procedures, techniques, tools, and ethical issues. Background: Social media has widely integrated into both everyday life and the nursing profession, resulting in the accumulation of extensive nursing-related social media data. The analysis of such data facilitates the generation of evidence thereby aiding in the formation of better policies. Natural language processing has emerged as a promising methodology for analyzing social media data in the field of nursing. However, the extent of natural language processing applications in analyzing nursing-related social media data remains unknown. Evaluation: A scoping review was conducted. PubMed, CINAHL, Web of Science and IEEE Xplore were searched. Studies were screened based on inclusion criteria. Relevant data were extracted and summarized using a descriptive approach. Key Issues: In total, 38 studies were included for the final analysis. Topic modeling and sentiment analysis were the most frequently employed natural language processing techniques. The most used topic modeling algorithm was latent Dirichlet allocation. The dictionary-based approach was the most utilized sentiment analysis approach, and the National Research Council Sentiment and Emotion Lexicons was the most used sentiment dictionary. Natural language processing tools such as Python (NLTK, Jieba, spaCy, and KoNLP library) and R (LDAvis, Jaccard, ldatuning, and SentiWordNet packages) were documented. A significant proportion of the included studies did not obtain ethical approval and did not conduct data anonymization on social media users' information. Conclusion: This scoping review summarized the extent of natural language processing techniques adoption in nursing and relevant procedures and tools, offering valuable resources for researchers who are interested in discovering knowledge from social media data. The study also highlighted that the application of natural language processing for analyzing nursing-related social media data is still emerging, indicating opportunities for future methodological improvements. Implications for Nursing Management: There is a need for a standardized management framework for conducting and reporting studies using natural language processing techniques in the analysis of nursing-related social media data. The findings could inform the development of regulatory policies by nursing authorities.

In-hospital outcomes of older patients with gastric cancer and their risk factors: large comprehensive institution-based study

PURPOSE

Gastric cancer (GC) is mostly a disease of aging, and older patients with GC are generally frailer. This study aimed to describe the characteristics and in-hospital outcomes, both overall and stratified by gender and resection, and to explore factors associated with outcomes of first hospitalization, in older GC patients.

METHODS

Data on GC patients ≥ 65 years hospitalized from January 2016 until December 2020 were retrieved from the electronic medical records of a large tertiary hospital. Patient and tumor characteristics, duration and fee of hospitalization, and in-hospital mortality were described for overall patients and compared by gender and resection. Factors associated with outcomes of first hospitalization were explored using multivariable-adjusted logistic regression.

RESULTS

3238 eligible patients were analyzed, with a mean age of 71 years and a male proportion of 74%. The median duration and fee of first hospitalization were 13 days and 40,000 RMB, respectively, with a median fee of 17,000 RMB not covered by insurance. 16 (< 1%) and 32 (1%) deaths occurred during first and any hospitalization, respectively, with only 4 (< 1%) perioperative deaths. Compared to male patients, female cases had more often signet-ring-cell carcinoma, reduced food intake, resection, and history of major abdominal surgery. Compared to unresected cases, resected patients had higher body-mass-index and Barthel index, less often reduced food intake, weight loss, and risk of malnutrition, and more often common diet, longer hospital stay, and higher fee. Through multivariable-adjusted analysis, longer first hospital-stay was associated with earlier year of diagnosis, older ages, emergency admission, signet-ring-cell carcinoma, resection, history of anticoagulant intake, larger body-mass-index, non-common diet, and non-low-salt and non-diabetes diets; higher fee of first hospitalization was associated with later year of diagnosis, male gender, older ages, emergency admission, signet-ring-cell carcinoma, and resection.

CONCLUSIONS

In this large institution-based study, older GC patients had low in-hospital mortality rates; the insurance coverage needs to be improved. Several characteristics and in-hospital outcomes significantly differed by gender and resection status, and various factors associated with duration and fee of first hospitalization were identified, providing important hints for individualized and stratified geriatric GC care.

Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-Shot Logical Reasoning Over Text

Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering (MCQA). Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: 1) how is the zero-shot capability of the models (train on seen types and test on unseen types)? and 2) how to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes the heuristic input reconstruction and builds a text graph with a global node. Incorporating graph reasoning and contrastive learning, TaCo can improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art (SOTA) methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset.

ViViEchoformer: Deep Video Regressor Predicting Ejection Fraction

Heart disease is the leading cause of death worldwide, and cardiac function as measured by ejection fraction (EF) is an important determinant of outcomes, making accurate measurement a critical parameter in PT evaluation. Echocardiograms are commonly used for measuring EF, but human interpretation has limitations in terms of intra- and inter-observer (or reader) variance. Deep learning (DL) has driven a resurgence in machine learning, leading to advancements in medical applications. We introduce the ViViEchoformer DL approach, which uses a video vision transformer to directly regress the left ventricular function (LVEF) from echocardiogram videos. The study used a dataset of 10,030 apical-4-chamber echocardiography videos from patients at Stanford University Hospital. The model accurately captures spatial information and preserves inter-frame relationships by extracting spatiotemporal tokens from video input, allowing for accurate, fully automatic EF predictions that aid human assessment and analysis. The ViViEchoformer's prediction of ejection fraction has a mean absolute error of 6.14%, a root mean squared error of 8.4%, a mean squared log error of 0.04, and anR 2 of 0.55. ViViEchoformer predicted heart failure with reduced ejection fraction (HFrEF) with an area under the curve of 0.83 and a classification accuracy of 87 using a standard threshold of less than 50% ejection fraction. Our video-based method provides precise left ventricular function quantification, offering a reliable alternative to human evaluation and establishing a fundamental basis for echocardiogram interpretation.

AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges

BACKGROUND

Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care.

OBJECTIVE

This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence-driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness.

METHODS

A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review.

RESULTS

Larger datasets with precise dates of participants' diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone.

CONCLUSIONS

Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user's depression and anxiety is merited.

Machine Learnable Language for the Chemical Space of Nanopores Enables Structure-Property Relationships in Nanoporous 2D Materials

The synthesis of nanoporous two-dimensional (2D) materials has revolutionized fields such as membrane separations, DNA sequencing, and osmotic power harvesting. Nanopores in 2D materials significantly modulate their optoelectronic, magnetic, and barrier properties. However, the large number of possible nanopore isomers makes their study onerous, while the lack of machine-learnable representations stymies progress toward structure-property relationships. Here, we develop a language for nanopores in 2D materials, called STring Representation Of Nanopore Geometry (STRONG), that opens the field of 2D nanopore informatics. We show that STRONGs are naturally suited for machine learning via recurrent neural networks, predicting formation energies/times of arbitrary nanopores and transport barriers for CO2, N2, and O2 gas molecules, enabling structure-property relationships. The machine learning models enable the discovery of specific nanopore topologies to separate CO2/N2, O2/CO2, and O2/N2 gas mixtures with high selectivity ratios. We also enable the rapid enumeration of unique configurations of stable, functionalized nanopores in 2D materials via STRONGs, allowing systematic searching of the vast chemical space of nanopores. Using the STRONGs approach, we find that a mix of hydrogen and quinone functionalization results in the most stable functionalized nanopore configuration in graphene, a discovery made feasible by expedited chemical space exploration. Additionally, we also unravel the STRONGs approach as ∼1000 times faster than graph theory algorithms to distinguish nanopore shapes. These advances in the language-based representation of 2D nanopores will accelerate the tailored design of nanoporous materials.

Incidental pulmonary nodules: Natural language processing analysis of radiology reports

BACKGROUND

Pulmonary nodules are a common incidental finding on chest Computed Tomography scans (CT), most of the time outside of lung cancer screening (LCS). We aimed to evaluate the number of incidental pulmonary nodules (IPN) found in 1 year in our hospital, as well as the follow-up (FUP) rate and the clinical and radiological features associated with FUP.

METHODS

We trained a Natural Language Processing (NLP) tool to identify the transcripts mentioning the presence of a pulmonary nodule, among a large population of patients from a French hospital. We extracted nodule characteristics using keyword analysis. NLP algorithm accuracy was determined through manual reading from a sample of our population. Electronic health database and medical record analysis by clinician allowed us to obtain information about FUP and cancer diagnoses.

RESULTS

In this retrospective observational study, we analyzed 101,703 transcripts corresponding to the entire CTs performed in 2020. We identified 1,991 (2 %) patients with an IPN. NLP accuracy for nodule detection in CT reports was 99 %. Only 41 % received a FUP between January 2020 and December 2021. Patient age, nodule size, and the mention of the nodule in the impression part were positively associated with FUP, while nodules diagnosed in the context of COVID-19 were less followed. 36 (2 %) lung cancers were subsequently diagnosed, with 16 (45 %) at a non-metastatic stage.

CONCLUSIONS

We identified a high prevalence of IPN with a low FUP rate, encouraging the implementation of IPN management program. We also highlighted the potential of NLP for database analysis in clinical research.

Use of AI in Mental Health Care: Community and Mental Health Professionals Survey

Background

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support.

Objective

This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs).

Methods

This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted.

Results

The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback.

Conclusions

Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Artificial intelligence methods available for cancer research

Cancer is a heterogeneous and multifaceted disease with a significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis and selection of effective treatment remains a challenge. With the convenience of large-scale datasets including multiple levels of data, new bioinformatic tools are needed to transform this wealth of information into clinically useful decision-support tools. In this field, artificial intelligence (AI) technologies with their highly diverse applications are rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, and K-nearest neighbors, including neural network models like deep learning, have proven valuable in predictive, prognostic, and diagnostic studies. Researchers have recently employed large language models to tackle new dimensions of problems. However, leveraging the opportunity to utilize AI in clinical settings will require surpassing significant obstacles-a major issue is the lack of use of the available reporting guidelines obstructing the reproducibility of published studies. In this review, we discuss the applications of AI methods and explore their benefits and limitations. We summarize the available guidelines for AI in healthcare and highlight the potential role and impact of AI models on future directions in cancer research.

Speech-Driven Personalized Gesture Synthetics: Harnessing Automatic Fuzzy Feature Inference

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor, a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio. The model combines a fuzzy feature extractor and a non-autoregressive Adaptive Layer Normalization (AdaLN) transformer diffusion architecture (DiTs-based). The fuzzy feature extractor harnesses a fuzzy inference strategy that automatically infers implicit, continuous fuzzy features. These fuzzy features, represented as a unified latent feature, are fed into the AdaLN transformer. The AdaLN transformer introduces a conditional mechanism that applies a uniform function across all tokens, thereby effectively modeling the correlation between the fuzzy features and the gesture sequence. This module ensures a high level of gesture-speech synchronization while preserving naturalness. Finally, we employ the diffusion model to train and infer various gestures. Extensive subjective and objective evaluations on the Trinity, ZEGGS, and BEAT datasets confirm our model's superior performance to the current state-of-the-art approaches. Persona-Gestor improves the system's usability and generalization capabilities, setting a new benchmark in speech-driven gesture synthesis and broadening the horizon for virtual human technology.

The promises and challenges of AI-based chatbots in language education through the lens of learner emotions

The integration of AI-based chatbots in language education has garnered significant attention, yet the interplay between chatbots and positive psychology remains underexplored. Filling this gap through a critical analysis of existing theories, measurement scales, and empirical evidence, this paper evaluates the potential benefits and drawbacks of incorporating AI chatbots in language learning environments and how AI chatbots may positively or negatively impact emotional dimensions of language acquisition. The findings unravel that the primary advantages of the AI chatbots are personalized instruction with rapid feedback, a decrease in anxiety levels and a surge in motivation, greater learner independence and self-directed learning, and the fostering of metacognitive abilities. Conversely, the identified obstacles encompass restricted emotional awareness, a deficiency in genuine human interaction, ethical dilemmas and privacy issues, as well as the potential reinforcement of biases and stereotypes. By highlighting the importance of learner emotions in the language learning process, this conceptual analysis review underscores the need for a nuanced understanding of how AI chatbots can support or hinder emotional engagement and motivation. The paper discusses the impacting factors of AI-based chatbots in language education, and strategies for addressing challenges and optimizing chatbot-learner interactions, such as incorporating affective computing techniques and designing culturally-sensitive chatbots. Finally, the article outlines future research directions, emphasizing the need for validated emotion scales in chatbot assisted language learning contexts, longitudinal studies, mixed-methods research, comparative analyses, and investigations into the role of chatbots in fostering emotional intelligence and intercultural competence.

Exploring public opinion on health effects of prepared dishes in China through social media comments

Introduction

In the 2020s, particularly following 2022, the Chinese government introduced a series of initiatives to foster the development of the prepared dishes sector, accompanied by substantial investments from industrial capital. Consequently, China's prepared dishes industry has experienced rapid growth. Nevertheless, this swift expansion has elicited varied public opinions, particularly concerning the potential health effects of prepared dishes. Therefore, this study aims to gather and analyze comments from social media on prepared dishes using machine learning techniques. The objective is to ascertain the perspectives of the Chinese populace on the health implications of consuming prepared dishes.

Methods

Social media comments, characterized by their broad distribution, objectivity, and timeliness, served as the primary data source for this study. Initially, the data underwent preprocessing to ensure its suitability for analysis. Subsequent steps in this study involved conducting sentiment analysis and employing the BERTopic model for topic clustering. These methods aimed to identify the principal concerns of the public regarding the impact of prepared dishes on health. The final phase of the study involved a comparative analysis of changes in public sentiment and thematic focus across different time frames. This approach provides a dynamic view of evolving public perceptions related to the health implications of prepared dishes.

Results

This study analyzed over 600,000 comments gathered from various social media platforms from mid-July 2022 to the end of March 2024. Following data preprocessing, 200,993 comments were assessed for sentiment, revealing that more than 64% exhibited negative emotions. Subsequent topic clustering using the BERTopic model identified that 11 of the top 50 topics were related to public health concerns. These topics primarily scrutinized the safety of prepared dish production processes, raw materials, packaging materials, and additives. Moreover, significant public's interest was in the right to informed consumption across different contexts. Notably, the most pronounced public opposition emerged regarding introducing prepared dishes into primary and secondary school canteens, with criticisms directed at the negligence of educational authorities and the ethics of manufacturers. Additionally, there were strong recommendations for media organizations to play a more active role in monitoring public opinion and for government agencies to enhance regulatory oversight.

Conclusion

The findings of this study indicate that more than half of the Chinese public maintain a negative perception towards prepared dishes, particularly concerning about health implications. Chinese individuals display considerable sensitivity and intense reactions to news and events related to prepared dishes. Consequently, the study recommends that manufacturers directly address public psychological perceptions, proactively enhance production processes and service quality, and increase transparency in public communications to improve corporate image and people acceptance of prepared dishes. Additionally, supervisory and regulatory efforts must be intensified by media organizations and governmental bodies, fostering the healthy development of the prepared food industry in China.

Natural Language Processing-Driven Artificial Intelligence Models for the Diagnosis of Lumbar Disc Herniation with L5 and S1 Radiculopathy: A Preliminary Evaluation

OBJECTIVE

To develop and validate natural language processing-driven artificial intelligence (AI) models for the diagnosis of lumbar disc herniation (LDH) with L5 and S1 radiculopathy using electronic health records (EHRs).

METHODS

EHRs of patients undergoing single-level percutaneous endoscopic lumbar discectomy for the treatment of LDH at the L4/5 or L5/S1 level between June 1, 2013, and December 31, 2021, were collected. The primary outcome was LDH with L5 and S1 radiculopathy, which was defined as nerve root compression recorded in the operative notes. Datasets were created using the history of present illness text and positive symptom text with radiculopathy (L5 or S1), respectively. The datasets were randomly split into a training set and a testing set in a 7:3 ratio. Two machine learning models, the long short-term memory network and Extreme Gradient Boosting, were developed using the training set. Performance evaluation of the models on the testing set was done using measures such as the receiver operating characteristic curve, area under the curve, accuracy, recall, F1-score, and precision.

RESULTS

The study included a total of 1681 patients, with 590 patients having L5 radiculopathy and 1091 patients having S1 radiculopathy. Among the 4 models developed, the long short-term memory model based on positive symptom text showed the best discrimination in the testing set, with precision (0.9054), recall (0.9405), accuracy (0.8950), F1-score (0.9226), and area under the curve (0.9485).

CONCLUSIONS

This study provides preliminary validation of the concept that natural language processing-driven AI models can be used for the diagnosis of lumbar disease using EHRs. This study could pave the way for future research that may develop more comprehensive and clinically impactful AI-driven diagnostic systems.

Simplifying synthesis of the expanding glioblastoma literature: a topic modeling approach

PURPOSE

Our study aims to discover the leading topics within glioblastoma (GB) research, and to examine if these topics have "hot" or "cold" trends. Additionally, we aim to showcase the potential of natural language processing (NLP) in facilitating research syntheses, offering an efficient strategy to dissect the landscape of academic literature in the realm of GB research.

METHODS

The Scopus database was queried using "glioblastoma" as the search term, in the "TITLE" and "KEY" fields. BERTopic, an NLP-based topic modeling (TM) method, was used for probabilistic TM. We specified a minimum topic size of 300 documents and 5% probability cutoff for outlier detection. We labeled topics based on keywords and representative documents and visualized them with word clouds. Linear regression models were utilized to identify "hot" and "cold" topic trends per decade.

RESULTS

Our TM analysis categorized 43,329 articles into 15 distinct topics. The most common topics were Genomics, Survival, Drug Delivery, and Imaging, while the least common topics were Surgical Resection, MGMT Methylation, and Exosomes. The hottest topics over the 2020s were Viruses and Oncolytic Therapy, Anticancer Compounds, and Exosomes, while the cold topics were Surgical Resection, Angiogenesis, and Tumor Metabolism.

CONCLUSION

Our NLP methodology provided an extensive analysis of GB literature, revealing valuable insights about historical and contemporary patterns difficult to discern with traditional techniques. The outcomes offer guidance for research directions, policy, and identifying emerging trends. Our approach could be applied across research disciplines to summarize and examine scholarly literature, guiding future exploration.

Generative artificial intelligence performs rudimentary structural biology modeling

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.

Hardware-accelerated integrated optoelectronic platform towards real-time high-resolution hyperspectral video understanding

Recent advancements in artificial intelligence have significantly expanded capabilities in processing language and images. However, the challenge of comprehensively understanding video content still needs to be solved. The main problem is the requirement to process real-time multidimensional video information at data rates exceeding 1 Tb/s, a demand that current hardware technologies cannot meet. This work introduces a hardware-accelerated integrated optoelectronic platform specifically designed for the real-time analysis of multidimensional video. By leveraging optical information processing within artificial intelligence hardware and combining it with advanced machine vision networks, the platform achieves data processing speeds of 1.2 Tb/s. This capability supports the analysis of hundreds of frequency bands with megapixel spatial resolution at video frame rates, significantly outperforming existing technologies in speed by three to four orders of magnitude. The platform demonstrates effectiveness for AI-driven tasks, such as video semantic segmentation and object understanding, across indoor and aerial scenarios. By overcoming the current data processing speed limitations, the platform shows promise in real-time AI video understanding, with potential implications for enhancing human-machine interactions and advancing cognitive processing technologies.

Comparative Evaluation of AI Models Such as ChatGPT 3.5, ChatGPT 4.0, and Google Gemini in Neuroradiology Diagnostics

AIMS AND OBJECTIVE

Advances in artificial intelligence (AI), particularly in large language models (LLMs) like ChatGPT (versions 3.5 and 4.0) and Google Gemini, are transforming healthcare. This study explores the performance of these AI models in solving diagnostic quizzes from "Neuroradiology: A Core Review" to evaluate their potential as diagnostic tools in radiology.

MATERIALS AND METHODS

We assessed the accuracy of ChatGPT 3.5, ChatGPT 4.0, and Google Gemini using 262 multiple-choice questions covering brain, head and neck, spine, and non-interpretive skills. Each AI tool provided answers and explanations, which were compared to textbook answers. The analysis followed the STARD (Standards for Reporting of Diagnostic Accuracy Studies) guidelines, and accuracy was calculated for each AI tool and subgroup.

RESULTS

ChatGPT 4.0 achieved the highest overall accuracy at 64.89%, outperforming ChatGPT 3.5 (62.60%) and Google Gemini (55.73%). ChatGPT 4.0 excelled in brain, head, and neck diagnostics, while Google Gemini performed best in head and neck but lagged in other areas. ChatGPT 3.5 showed consistent performance across all subgroups.

CONCLUSION

This study found that advanced AI models, including ChatGPT 4.0 and Google Gemini, vary in diagnostic accuracy, with ChatGPT 4.0 leading at 64.89% overall. While these tools are promising in improving diagnostics and medical education, their effectiveness varies by area, and Google Gemini performs unevenly across different categories. The study underscores the need for ongoing improvements and broader evaluation to address ethical concerns and optimize AI use in patient care.

Appraisal of ChatGPT's Aptitude for Medical Education: Comparative Analysis With Third-Year Medical Students in a Pulmonology Examination

BACKGROUND

The rapid evolution of ChatGPT has generated substantial interest and led to extensive discussions in both public and academic domains, particularly in the context of medical education.

OBJECTIVE

This study aimed to evaluate ChatGPT's performance in a pulmonology examination through a comparative analysis with that of third-year medical students.

METHODS

In this cross-sectional study, we conducted a comparative analysis with 2 distinct groups. The first group comprised 244 third-year medical students who had previously taken our institution's 2020 pulmonology examination, which was conducted in French. The second group involved ChatGPT-3.5 in 2 separate sets of conversations: without contextualization (V1) and with contextualization (V2). In both V1 and V2, ChatGPT received the same set of questions administered to the students.

RESULTS

V1 demonstrated exceptional proficiency in radiology, microbiology, and thoracic surgery, surpassing the majority of medical students in these domains. However, it faced challenges in pathology, pharmacology, and clinical pneumology. In contrast, V2 consistently delivered more accurate responses across various question categories, regardless of the specialization. ChatGPT exhibited suboptimal performance in multiple choice questions compared to medical students. V2 excelled in responding to structured open-ended questions. Both ChatGPT conversations, particularly V2, outperformed students in addressing questions of low and intermediate difficulty. Interestingly, students showcased enhanced proficiency when confronted with highly challenging questions. V1 fell short of passing the examination. Conversely, V2 successfully achieved examination success, outperforming 139 (62.1%) medical students.

CONCLUSIONS

While ChatGPT has access to a comprehensive web-based data set, its performance closely mirrors that of an average medical student. Outcomes are influenced by question format, item complexity, and contextual nuances. The model faces challenges in medical contexts requiring information synthesis, advanced analytical aptitude, and clinical judgment, as well as in non-English language assessments and when confronted with data outside mainstream internet sources.

Improve the efficiency and accuracy of ophthalmologists' clinical decision-making based on AI technology

BACKGROUND

As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform's effectiveness in enhancing doctors' diagnostic efficiency and accuracy.

METHODS

In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors.

RESULTS

The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors.

CONCLUSION

The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review

Social media data are rapidly evolving and accessible, which presents opportunities for research. Data science techniques, such as sentiment or emotion analysis which analyse textual emotion, provide an opportunity to gather insight from social media. This paper describes a systematic scoping review of interdisciplinary evidence to explore how sentiment or emotion analysis methods alongside other data science methods have been used to examine nutrition, food and cooking social media content. A PRISMA search strategy was used to search nine electronic databases in November 2020 and January 2022. Of 7325 studies identified, thirty-six studies were selected from seventeen countries, and content was analysed thematically and summarised in an evidence table. Studies were published between 2014 and 2022 and used data from seven different social media platforms (Twitter, YouTube, Instagram, Reddit, Pinterest, Sina Weibo and mixed platforms). Five themes of research were identified: dietary patterns, cooking and recipes, diet and health, public health and nutrition and food in general. Papers developed a sentiment or emotion analysis tool or used available open-source tools. Accuracy to predict sentiment ranged from 33·33% (open-source engine) to 98·53% (engine developed for the study). The average proportion of sentiment was 38·8% positive, 46·6% neutral and 28·0% negative. Additional data science techniques used included topic modelling and network analysis. Future research requires optimising data extraction processes from social media platforms, the use of interdisciplinary teams to develop suitable and accurate methods for the subject and the use of complementary methods to gather deeper insights into these complex data.

Advances in machine learning with chemical language models in molecular property and reaction outcome predictions

Molecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical nature of the domain of chemical science. With increasing emphasis on sustainable practices, it is desirable that a target set of molecules are synthesized preferably through a fewer empirical attempts instead of a larger library, to realize an active candidate. In this front, predictive endeavors using machine learning (ML) models built on available data acquire high timely significance. Prediction of molecular property and reaction outcome remain one of the burgeoning applications of ML in chemical science. Among several methods of encoding molecular samples for ML models, the ones that employ language like representations are gaining steady popularity. Such representations would additionally help adopt well-developed natural language processing (NLP) models for chemical applications. Given this advantageous background, herein we describe several successful chemical applications of NLP focusing on molecular property and reaction outcome predictions. From relatively simpler recurrent neural networks (RNNs) to complex models like transformers, different network architecture have been leveraged for tasks such as de novo drug design, catalyst generation, forward and retro-synthesis predictions. The chemical language model (CLM) provides promising avenues toward a broad range of applications in a time and cost-effective manner. While we showcase an optimistic outlook of CLMs, attention is also placed on the persisting challenges in reaction domain, which would optimistically be addressed by advanced algorithms tailored to chemical language and with increased availability of high-quality datasets.

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated "biochemical" language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications. SCIENTIFIC CONTRIBUTION: The approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data.

Social media for palliative and end-of-life care research: a systematic review

BACKGROUND

Social media with real-time content and a wide-reaching user network opens up more possibilities for palliative and end-of-life care (PEoLC) researchers who have begun to embrace it as a complementary research tool. This review aims to identify the uses of social media in PEoLC studies and to examine the ethical considerations and data collection approaches raised by this research approach.

METHODS

Nine online databases were searched for PEoLC research using social media published before December 2022. Thematic analysis and narrative synthesis approach were used to categorise social media applications.

RESULTS

21 studies were included. 16 studies used social media to conduct secondary analysis and five studies used social media as a platform for information sharing. Ethical considerations relevant to social media studies varied while 15 studies discussed ethical considerations, only 6 studies obtained ethical approval and 5 studies confirmed participant consent. Among studies that used social media data, most of them manually collected social media data, and other studies relied on Twitter application programming interface or third-party analytical tools. A total of 1 520 329 posts, 325 videos and 33 articles related to PEoLC from 2008 to 2022 were collected and analysed.

CONCLUSIONS

Social media has emerged as a promising complementary research tool with demonstrated feasibility in various applications. However, we identified the absence of standardised ethical handling and data collection approaches which pose an ongoing challenge. We provided practical recommendations to bridge these pressing gaps for researchers wishing to use social media in future PEoLC-related studies.

Machine-Learning-Based Prediction of Client Distress From Session Recordings

Natural language processing (NLP) is a subfield of machine learning that may facilitate the evaluation of therapist-client interactions and provide feedback to therapists on client outcomes on a large scale. However, there have been limited studies applying NLP models to client outcome prediction that have (a) used transcripts of therapist-client interactions as direct predictors of client symptom improvement, (b) accounted for contextual linguistic complexities, and (c) used best practices in classical training and test splits in model development. Using 2,630 session recordings from 795 clients and 56 therapists, we developed NLP models that directly predicted client symptoms of a given session based on session recordings of the previous session (Spearman's rho =0.32, p<.001). Our results highlight the potential for NLP models to be implemented in outcome monitoring systems to improve quality of care. We discuss implications for future research and applications.