Natural language processing: state of the art, current trends and challenges

Generative Pre-trained Transformer for Pediatric Stroke Research: A Pilot Study

BACKGROUND

Pediatric stroke is an important cause of morbidity in children. Although research can be challenging, large amounts of data have been captured through collaborative efforts in the International Pediatric Stroke Study (IPSS). This study explores the use of an advanced artificial intelligence program, the Generative Pre-trained Transformer (GPT), to enter pediatric stroke data into the IPSS.

METHODS

The most recent 50 clinical notes of patients with ischemic stroke or cerebral venous sinus thrombosis at the UTHealth Pediatric Stroke Clinic were deidentified. Domain-specific prompts were engineered for an offline artificial intelligence program (GPT) to answer IPSS questions. Responses from GPT were compared with the human rater. Percent agreement was assessed across 50 patients for each of the 114 queries developed from the IPSS database outcome questionnaire.

RESULTS

GPT demonstrated strong performance on several questions but showed variability overall. In its early iterations it was able to match human judgment occasionally with an accuracy score of 1.00 (n = 20, 17.5%), but it scored as low as 0.26 in some patients. Prompts were adjusted in four subsequent iterations to increase accuracy. In its fourth iteration, agreement was 93.6%, with a maximum agreement of 100% and minimum of 62%. Of 2400 individual items assessed, our model entered 2247 (93.6%) correctly and 153 (6.4%) incorrectly.

CONCLUSIONS

Although our tailored generative model with domain-specific prompt engineering and ontological guidance shows promise for research applications, further refinement is needed to enhance its accuracy. It cannot enter data entirely independently, but it can be employed in tandem with human oversight contributing to a collaborative approach that reduces overall effort.

Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification-A case study on nano-FeCu

Nanoparticle synthesis is complex, influenced by multiple variables including reagent selection. This study introduces a specialized corpus focused on "Fe, Cu, synthesis" to train a domain-specific word embedding model using natural language processing (NLP) in an unsupervised environment. Evaluation metrics included average cosine similarity, visual analysis via t-distributed stochastic neighbor embedding (t-SNE), synonym analysis, and analogy reasoning analysis. Results indicate a strong correlation between learning rate and cosine similarity, with enhanced chemical specificity in the tailored model compared to general models. The framework facilitates rapid identification of potential reagents for nano-FeCu synthesis, enhancing precision in nanomaterial research. This innovative approach offers a data-driven pathway for chemical material synthesis, demonstrating significant interdisciplinary applications.

Potential for advances in data linkage and data science to support injury prevention research

The recent COVID-19 pandemic stimulated unprecedented linkage of datasets worldwide, and while injury is endemic rather than pandemic, there is much to be learned by the injury prevention community from the data science approaches taken to respond to the pandemic to support research into the primary, secondary and tertiary prevention of injuries. The use of routinely collected data to produce real-world evidence, as an alternative to clinical trials, has been gaining in popularity as the availability and quality of digital health platforms grow and the linkage landscape, and the analytics required to make best use of linked and unstructured data, is rapidly evolving. Capitalising on existing data sources, innovative linkage and advanced analytic approaches provides the opportunity to undertake novel injury prevention research and generate new knowledge, while avoiding data waste and additional burden to participants. We provide a tangible, but not exhaustive, list of examples showing the breadth and value of data linkage, along with the emerging capabilities of natural language processing techniques to enhance injury research. To optimise data science approaches to injury prevention, injury researchers in this area need to share methods, code, models and tools to improve consistence and efficiencies in this field. Increased collaboration between injury prevention researchers and data scientists working on population data linkage systems has much to offer this field of research.

Shadows of wisdom: Classifying meta-cognitive and morally grounded narrative content via large language models

We investigated large language models' (LLMs) efficacy in classifying complex psychological constructs like intellectual humility, perspective-taking, open-mindedness, and search for a compromise in narratives of 347 Canadian and American adults reflecting on a workplace conflict. Using state-of-the-art models like GPT-4 across few-shot and zero-shot paradigms and RoB-ELoC (RoBERTa -fine-tuned-on-Emotion-with-Logistic-Regression-Classifier), we compared their performance with expert human coders. Results showed robust classification by LLMs, with over 80% agreement and F1 scores above 0.85, and high human-model reliability (Cohen's κ Md across top models = .80). RoB-ELoC and few-shot GPT-4 were standout classifiers, although somewhat less effective in categorizing intellectual humility. We offer example workflows for easy integration into research. Our proof-of-concept findings indicate the viability of both open-source and commercial LLMs in automating the coding of complex constructs, potentially transforming social science research.

A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology

Artificial Intelligence and Natural Language Processing technology have demonstrated significant promise across several domains within the medical and healthcare sectors. This technique has numerous uses in the field of healthcare. One of the primary challenges in implementing ChatGPT in healthcare is the requirement for precise and up-to-date data. In the case of the involvement of sensitive medical information, it is imperative to carefully address concerns regarding privacy and security when using GPT in the healthcare sector. This paper outlines ChatGPT and its relevance in the healthcare industry. It discusses the important aspects of ChatGPT's workflow and highlights the usual features of ChatGPT specifically designed for the healthcare domain. The present review uses the ChatGPT model within the research domain to investigate disorders associated with the hepatic system. This review demonstrates the possible use of ChatGPT in supporting researchers and clinicians in analyzing and interpreting liver-related data, thereby improving disease diagnosis, prognosis, and patient care.

Mitigating off-target effects of small RNAs: conventional approaches, network theory and artificial intelligence

Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small-molecule drugs. These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off-target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off-target effects we focused on the hybridization-dependent, especially on the miRNA-like off-target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization-dependent off-target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization-dependent off-target effects, this review provides an overview of all major state-of-the-art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating in silico predictions are also presented. These methods could contribute to interpret experimental results, to minimize off-target effects and hopefully to avoid off-target-related adverse events of small RNA therapeutics.

An exploration of available methods and tools to improve the efficiency of systematic review production: a scoping review

BACKGROUND

Systematic reviews (SRs) are time-consuming and labor-intensive to perform. With the growing number of scientific publications, the SR development process becomes even more laborious. This is problematic because timely SR evidence is essential for decision-making in evidence-based healthcare and policymaking. Numerous methods and tools that accelerate SR development have recently emerged. To date, no scoping review has been conducted to provide a comprehensive summary of methods and ready-to-use tools to improve efficiency in SR production.

OBJECTIVE

To present an overview of primary studies that evaluated the use of ready-to-use applications of tools or review methods to improve efficiency in the review process.

METHODS

We conducted a scoping review. An information specialist performed a systematic literature search in four databases, supplemented with citation-based and grey literature searching. We included studies reporting the performance of methods and ready-to-use tools for improving efficiency when producing or updating a SR in the health field. We performed dual, independent title and abstract screening, full-text selection, and data extraction. The results were analyzed descriptively and presented narratively.

RESULTS

We included 103 studies: 51 studies reported on methods, 54 studies on tools, and 2 studies reported on both methods and tools to make SR production more efficient. A total of 72 studies evaluated the validity (n = 69) or usability (n = 3) of one method (n = 33) or tool (n = 39), and 31 studies performed comparative analyses of different methods (n = 15) or tools (n = 16). 20 studies conducted prospective evaluations in real-time workflows. Most studies evaluated methods or tools that aimed at screening titles and abstracts (n = 42) and literature searching (n = 24), while for other steps of the SR process, only a few studies were found. Regarding the outcomes included, most studies reported on validity outcomes (n = 84), while outcomes such as impact on results (n = 23), time-saving (n = 24), usability (n = 13), and cost-saving (n = 3) were less often evaluated.

CONCLUSION

For title and abstract screening and literature searching, various evaluated methods and tools are available that aim at improving the efficiency of SR production. However, only few studies have addressed the influence of these methods and tools in real-world workflows. Few studies exist that evaluate methods or tools supporting the remaining tasks. Additionally, while validity outcomes are frequently reported, there is a lack of evaluation regarding other outcomes.

AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.

LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models

In clinical science and practice, text data, such as clinical letters or procedure reports, is stored in an unstructured way. This type of data is not a quantifiable resource for any kind of quantitative investigations and any manual review or structured information retrieval is time-consuming and costly. The capabilities of Large Language Models (LLMs) mark a paradigm shift in natural language processing and offer new possibilities for structured Information Extraction (IE) from medical free text. This protocol describes a workflow for LLM based information extraction (LLM-AIx), enabling extraction of predefined entities from unstructured text using privacy preserving LLMs. By converting unstructured clinical text into structured data, LLM-AIx addresses a critical barrier in clinical research and practice, where the efficient extraction of information is essential for improving clinical decision-making, enhancing patient outcomes, and facilitating large-scale data analysis. The protocol consists of four main processing steps: 1) Problem definition and data preparation, 2) data preprocessing, 3) LLM-based IE and 4) output evaluation. LLM-AIx allows integration on local hospital hardware without the need of transferring any patient data to external servers. As example tasks, we applied LLM-AIx for the anonymization of fictitious clinical letters from patients with pulmonary embolism. Additionally, we extracted symptoms and laterality of the pulmonary embolism of these fictitious letters. We demonstrate troubleshooting for potential problems within the pipeline with an IE on a real-world dataset, 100 pathology reports from the Cancer Genome Atlas Program (TCGA), for TNM stage extraction. LLM-AIx can be executed without any programming knowledge via an easy-to-use interface and in no more than a few minutes or hours, depending on the LLM model selected.

Comprehensive review of virtual assistants in vascular surgery

Virtual assistants, broadly defined as digital services designed to simulate human conversation and provide personalized responses based on user input, have the potential to improve health care by supporting clinicians and patients in terms of diagnosing and managing disease, performing administrative tasks, and supporting medical research and education. These tasks are particularly helpful in vascular surgery, where the clinical and administrative burden is high due to the rising incidence of vascular disease, the medical complexity of the patients, and the potential for innovation and care advancement. The rapid development of artificial intelligence, machine learning, and natural language processing techniques have facilitated the training of large language models, such as GPT-4 (OpenAI), which can support the development of increasingly powerful virtual assistants. These tools may support holistic, multidisciplinary, and high-quality vascular care delivery throughout the pre-, intra-, and postoperative stages. Importantly, it is critical to consider the design, safety, and challenges related to virtual assistants, including data security, ethical, and equity concerns. By combining the perspectives of patients, clinicians, data scientists, and other stakeholders when developing, implementing, and monitoring virtual assistants, there is potential to harness the power of this technology to care for vascular surgery patients more effectively. In this comprehensive review article, we introduce the concept of virtual assistants, describe potential applications of virtual assistants in vascular surgery for clinicians and patients, highlight the benefits and drawbacks of large language models, such as GPT-4, and discuss considerations around the design, safety, and challenges associated with virtual assistants in vascular surgery.

From Web to RheumaLpack: Creating a Linguistic Corpus for Exploitation and Knowledge Discovery in Rheumatology

This study introduces RheumaLinguisticpack (RheumaLpack), the first specialised linguistic web corpus designed for the field of musculoskeletal disorders. By combining web mining (i.e., web scraping) and natural language processing (NLP) techniques, as well as clinical expertise, RheumaLpack systematically captures and curates structured and unstructured data across a spectrum of web sources including clinical trials registers (i.e., ClinicalTrials.gov), bibliographic databases (i.e., PubMed), medical agencies (i.e. European Medicines Agency), social media (i.e., Reddit), and accredited health websites (i.e., MedlinePlus, Harvard Health Publishing, and Cleveland Clinic). Given the complexity of rheumatic and musculoskeletal diseases (RMDs) and their significant impact on quality of life, this resource can be proposed as a useful tool to train algorithms that could mitigate the diseases' effects. Therefore, the corpus aims to improve the training of artificial intelligence (AI) algorithms and facilitate knowledge discovery in RMDs. The development of RheumaLpack involved a systematic six-step methodology covering data identification, characterisation, selection, collection, processing, and corpus description. The result is a non-annotated, monolingual, and dynamic corpus, featuring almost 3 million records spanning from 2000 to 2023. RheumaLpack represents a pioneering contribution to rheumatology research, providing a useful resource for the development of advanced AI and NLP applications. This corpus highlights the value of web data to address the challenges posed by musculoskeletal diseases, illustrating the corpus's potential to improve research and treatment paradigms in rheumatology. Finally, the methodology shown can be replicated to obtain data from other medical specialities. The code and details on how to build RheumaLpack are also provided to facilitate the dissemination of such resource.

SSTE: Syllable-Specific Temporal Encoding to FORCE-learn audio sequences with an associative memory approach

The circuitry and pathways in the brains of humans and other species have long inspired researchers and system designers to develop accurate and efficient systems capable of solving real-world problems and responding in real-time. We propose the Syllable-Specific Temporal Encoding (SSTE) to learn vocal sequences in a reservoir of Izhikevich neurons, by forming associations between exclusive input activities and their corresponding syllables in the sequence. Our model converts the audio signals to cochleograms using the CAR-FAC model to simulate a brain-like auditory learning and memorization process. The reservoir is trained using a hardware-friendly approach to FORCE learning. Reservoir computing could yield associative memory dynamics with far less computational complexity compared to RNNs. The SSTE-based learning enables competent accuracy and stable recall of spatiotemporal sequences with fewer reservoir inputs compared with existing encodings in the literature for similar purpose, offering resource savings. The encoding points to syllable onsets and allows recalling from a desired point in the sequence, making it particularly suitable for recalling subsets of long vocal sequences. The SSTE demonstrates the capability of learning new signals without forgetting previously memorized sequences and displays robustness against occasional noise, a characteristic of real-world scenarios. The components of this model are configured to improve resource consumption and computational intensity, addressing some of the cost-efficiency issues that might arise in future implementations aiming for compactness and real-time, low-power operation. Overall, this model proposes a brain-inspired pattern generation network for vocal sequences that can be extended with other bio-inspired computations to explore their potentials for brain-like auditory perception. Future designs could inspire from this model to implement embedded devices that learn vocal sequences and recall them as needed in real-time. Such systems could acquire language and speech, operate as artificial assistants, and transcribe text to speech, in the presence of natural noise and corruption on audio data.

Disability Ethics and Education in the Age of Artificial Intelligence: Identifying Ability Bias in ChatGPT and Gemini

OBJECTIVE

To identify and quantify ability bias in generative artificial intelligence large language model chatbots, specifically OpenAI's ChatGPT and Google's Gemini.

DESIGN

Observational study of language usage in generative artificial intelligence models.

SETTING

Investigation-only browser profile restricted to ChatGPT and Gemini.

PARTICIPANTS

Each chatbot generated 60 descriptions of people prompted without specified functional status, 30 descriptions of people with a disability, 30 descriptions of patients with a disability, and 30 descriptions of athletes with a disability (N=300).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Generated descriptions produced by the models were parsed into words that were linguistically analyzed into favorable qualities or limiting qualities.

RESULTS

Both large language models significantly underestimated disability in a population of people, and linguistic analysis showed that descriptions of people, patients, and athletes with a disability were generated as having significantly fewer favorable qualities and significantly more limitations than people without a disability in both ChatGPT and Gemini.

CONCLUSIONS

Generative artificial intelligence chatbots demonstrate quantifiable ability bias and often exclude people with disabilities in their responses. Ethical use of these generative large language model chatbots in medical systems should recognize this limitation, and further consideration should be taken in developing equitable artificial intelligence technologies.

A scoping review of large language model based approaches for information extraction from radiology reports

Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports. We conduct a scoping review that follows the PRISMA-ScR guideline. Queries of five databases were conducted on August 1st 2023. Among the 34 studies that met inclusion criteria, only pre-transformer and encoder-based models are described. External validation shows a general performance decrease, although LLMs might improve generalizability of IE approaches. Reports related to CT and MRI examinations, as well as thoracic reports, prevail. Most common challenges reported are missing validation on external data and augmentation of the described methods. Different reporting granularities affect the comparability and transparency of approaches.

Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research

Microreactor technologies have emerged as versatile platforms with the potential to revolutionize chemistry and materials research, offering sustainable solutions to global challenges in environmental and health domains. This survey paper provides an in-depth review of recent advancements in microreactor technologies, focusing on their role in facilitating accelerated discoveries in chemistry and materials. Specifically, we examine the convergence of microfluidics with machine intelligence and automation, enabling the exploitation of the cyber-physical environment as a highly integrated experimentation platform for rapid scientific discovery and process development. We investigate the applicability and limitations of microreactor-enabled discovery accelerators in various chemistry and materials contexts. Despite their tremendous potential, the integration of machine intelligence and automation into microreactor-based experiments presents challenges in establishing fully integrated, automated, and intelligent systems. These challenges can hinder the broader adoption of microreactor technologies within the research community. To address this, we review emerging technologies that can help lower barriers and facilitate the implementation of microreactor-enabled discovery accelerators. Lastly, we provide our perspective on future research directions for democratizing microreactor technologies, with the aim of accelerating scientific discoveries and promoting widespread adoption of these transformative platforms.

Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease of Diospyros kaki Leaves Using Optical Coherence Tomography

Circular leaf spot (CLS) disease poses a significant threat to persimmon cultivation, leading to substantial harvest reductions. Existing visual and destructive inspection methods suffer from subjectivity, limited accuracy, and considerable time consumption. This study presents an automated pre-identification method of the disease through a deep learning (DL) based pipeline integrated with optical coherence tomography (OCT), thereby addressing the highlighted issues with the existing methods. The investigation yielded promising outcomes by employing transfer learning with pre-trained DL models, specifically DenseNet-121 and VGG-16. The DenseNet-121 model excels in differentiating among three stages of CLS disease (healthy (H), apparently healthy (or healthy-infected (HI)), and infected (I)). The model achieved precision values of 0.7823 for class-H, 0.9005 for class-HI, and 0.7027 for class-I, supported by recall values of 0.8953 for class-HI and 0.8387 for class-I. Moreover, the performance of CLS detection was enhanced by a supplemental quality inspection model utilizing VGG-16, which attained an accuracy of 98.99% in discriminating between low-detail and high-detail images. Moreover, this study employed a combination of LAMP and A-scan for the dataset labeling process, significantly enhancing the accuracy of the models. Overall, this study underscores the potential of DL techniques integrated with OCT to enhance disease identification processes in agricultural settings, particularly in persimmon cultivation, by offering efficient and objective pre-identification of CLS and enabling early intervention and management strategies.

Natural language processing with transformers: a review

Natural language processing (NLP) tasks can be addressed with several deep learning architectures, and many different approaches have proven to be efficient. This study aims to briefly summarize the use cases for NLP tasks along with the main architectures. This research presents transformer-based solutions for NLP tasks such as Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-Training (GPT) architectures. To achieve that, we conducted a step-by-step process in the review strategy: identify the recent studies that include Transformers, apply filters to extract the most consistent studies, identify and define inclusion and exclusion criteria, assess the strategy proposed in each study, and finally discuss the methods and architectures presented in the resulting articles. These steps facilitated the systematic summarization and comparative analysis of NLP applications based on Transformer architectures. The primary focus is the current state of the NLP domain, particularly regarding its applications, language models, and data set types. The results provide insights into the challenges encountered in this research domain.

A Comparative Analysis of ChatGPT and Medical Faculty Graduates in Medical Specialization Exams: Uncovering the Potential of Artificial Intelligence in Medical Education

Background This study aims to evaluate the performance of ChatGPT in the medical specialization exam (MSE) that medical graduates take when choosing their postgraduate specialization and to reveal how artificial intelligence-supported education can increase the quality and academic success of medical education. The research aims to explore the potential applications and advantages of artificial intelligence in medical education and examine ways in which this technology can contribute to student learning and exam preparation. Methodology A total of 240 MSE questions were posed to ChatGPT, 120 of which were basic medical sciences questions and 120 were clinical medical sciences questions. A total of 18,481 people participated in the exam. The performance of medical school graduates was compared with ChatGPT-3.5 in terms of answering these questions correctly. The average score for ChatGPT-3.5 was calculated by averaging the minimum and maximum scores. Calculations were done using the R.4.0.2 environment. Results The general average score of graduates was a minimum of 7.51 in basic sciences and a maximum of 81.46, while in clinical sciences, the average was a minimum of 12.51 and a maximum of 80.78. ChatGPT, on the other hand, had an average of at least 60.00 in basic sciences and a maximum of 72.00, with an average of at least 66.25 and a maximum of 77.00 in clinical sciences. The rate of correct answers in basic medical sciences for graduates was 43.03%, while for ChatGPT was 60.00%. In clinical medical sciences, the rate of correct answers for graduates was 53.29%, while for ChatGPT was 64.16%. ChatGPT performed best with a 91.66% correct answer rate in Obstetrics and Gynecology and an 86.36% correct answer rate in Medical Microbiology. The least successful area for ChatGPT was Anatomy, with a 28.00% correct answer rate, a subfield of basic medical sciences. Graduates outperformed ChatGPT in the Anatomy and Physiology subfields. Significant differences were found in all comparisons between ChatGPT and graduates. Conclusions This study shows that artificial intelligence models such as ChatGPT can provide significant advantages to graduates, as they score higher than medical school graduates. In terms of these benefits, recommended applications include interactive support, private lessons, learning material production, personalized learning plans, self-assessment, motivation boosting, and 24/7 access, among a variety of benefits. As a result, artificial intelligence-supported education can play an important role in improving the quality of medical education and increasing student success.

In the heart of Swahili: An exploration of data collection methods and corpus curation for natural language processing

Swahili corpus is a dataset generated by collecting written Kiswahili sentences from different sectors that deals with Kiswahili documents. Corpus of intended language is needed in Natural Language Processing (NLP) task to fit algorithm in order to understand that language before training the model. Swahili corpus dataset generated contained 1,693,228 sentences with 39,639,824 words and 871,452 unique words. Corpus exported in text file format with storage size of 168 MB. These sentences collected from different sources in different categories as follows: - Health (AFYA), Business and Industries (BIASHARA), Parliament (BUNGE), Religion (DINI), Education (ELIMU), News (HABARI), Agriculture (KILIMO), Social Media (MITANDAO), Non-Governmental Organizations (MASHIRIKA YA KIRAIA), Government (SERIKALI), Laws (SHERIA) and Politics (SIASA). This abstract outlines the systematic data collection process employed for the creation of a Swahili corpus derived from multiple public websites and reports. The compilation of this corpus involves a meticulous and comprehensive approach to ensure the representation of diverse linguistic contexts and topics relevant to the Swahili language. The data collection process commenced with the identification of suitable sources across various domains, including news articles, health publications, online forums, and Governmental public reports. Websites and platforms with publicly available Swahili content were systematically crawled and archived to capture a broad spectrum of linguistic expressions. Furthermore, special attention was given to reputable sources to maintain the authenticity of the corpus and linguistic richness. The inclusion of diverse sources ensures that the corpus reflects the linguistic nuances inherent in different contexts and registers within the Swahili language. Additionally, efforts were made to incorporate variations in domain dialects, acknowledging the linguistic diversity present in Swahili. The potential for reusing this Swahili corpus is vast. Researchers, linguists, and language enthusiasts can leverage the diverse and extensive dataset for a multitude of applications, including NLP tasks such as sentiment analysis, textual data clustering, classifications tasks and machine translation. The Corpus can serve as training data for developing and evaluating NLP algorithms, including part-of-speech tagging, and named entity recognition. Also, text mining techniques can be applied to corpus and enable researchers to extract valuable insights, identify patterns, and discover knowledge from large textual datasets.

UmamiPreDL: Deep learning model for umami taste prediction of peptides using BERT and CNN

Taste is crucial in driving food choice and preference. Umami is one of the basic tastes defined by characteristic deliciousness and mouthfulness that it imparts to foods. Identification of ingredients to enhance umami taste is of significant value to food industry. Various models have been shown to predict umami taste using feature encodings derived from traditional molecular descriptors such as amphiphilic pseudo-amino acid composition, dipeptide composition, and composition-transition-distribution. Highest reported accuracy of 90.5 % was recently achieved through novel model architecture. Here, we propose use of biological sequence transformers such as ProtBert and ESM2, trained on the Uniref databases, as the feature encoders block. With combination of 2 encoders and 2 classifiers, 4 model architectures were developed. Among the 4 models, ProtBert-CNN model outperformed other models with accuracy of 95 % on 5-fold cross validation data and 94 % on independent data.

ChatGPT in surgery: a revolutionary innovation?

ChatGPT has brought about a new era of digital health, as this model has become prominent and been rapidly developing since its release. ChatGPT may be able to facilitate improvements in surgery as well; however, the influence of ChatGPT on surgery is largely unknown at present. Therefore, the present study reports on the current applications of ChatGPT in the field of surgery, evaluating its workflow, practical implementations, limitations, and future perspectives. A literature search was performed using the PubMed and Embase databases. The initial search was performed from its inception until July 2023. This study revealed that ChatGPT has promising capabilities in areas of surgical research, education, training, and practice. In daily practice, surgeons and surgical residents can be aided in performing logistics and administrative tasks, and patients can be more efficiently informed about the details of their condition. However, priority should be given to establishing proper policies and protocols to ensure the safe and reliable use of this model.

De novo synthetic antimicrobial peptide design with a recurrent neural network

Antibiotic resistance is recognized as an imminent and growing global health threat. New antimicrobial drugs are urgently needed due to the decreasing effectiveness of conventional small-molecule antibiotics. Antimicrobial peptides (AMPs), a class of host defense peptides, are emerging as promising candidates to address this need. The potential sequence space of amino acids is combinatorially vast, making it possible to extend the current arsenal of antimicrobial agents with a practically infinite number of new peptide-based candidates. However, mining naturally occurring AMPs, whether directly by wet lab screening methods or aided by bioinformatics prediction tools, has its theoretical limit regarding the number of samples or genomic/transcriptomic resources researchers have access to. Further, manually designing novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods are gaining interest as a high-throughput solution to the problem. Here, we introduce AMPd-Up, a recurrent neural network based tool for de novo AMP design, and demonstrate its utility over existing methods. Validation of candidates designed by AMPd-Up through antimicrobial susceptibility testing revealed that 40 of the 58 generated sequences possessed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. These results illustrate that AMPd-Up can be used to design novel synthetic AMPs with potent activities.

Evaluation of ChatGPT as a diagnostic tool for medical learners and clinicians

BACKGROUND

ChatGPT is a large language model (LLM) trained on over 400 billion words from books, articles, and websites. Its extensive training draws from a large database of information, making it valuable as a diagnostic aid. Moreover, its capacity to comprehend and generate human language allows medical trainees to interact with it, enhancing its appeal as an educational resource. This study aims to investigate ChatGPT's diagnostic accuracy and utility in medical education.

METHODS

150 Medscape case challenges (September 2021 to January 2023) were inputted into ChatGPT. The primary outcome was the number (%) of cases for which the answer given was correct. Secondary outcomes included diagnostic accuracy, cognitive load, and quality of medical information. A qualitative content analysis was also conducted to assess its responses.

RESULTS

ChatGPT answered 49% (74/150) cases correctly. It had an overall accuracy of 74%, a precision of 48.67%, sensitivity of 48.67%, specificity of 82.89%, and an AUC of 0.66. Most answers were considered low cognitive load 51% (77/150) and most answers were complete and relevant 52% (78/150).

DISCUSSION

ChatGPT in its current form is not accurate as a diagnostic tool. ChatGPT does not necessarily give factual correctness, despite the vast amount of information it was trained on. Based on our qualitative analysis, ChatGPT struggles with the interpretation of laboratory values, imaging results, and may overlook key information relevant to the diagnosis. However, it still offers utility as an educational tool. ChatGPT was generally correct in ruling out a specific differential diagnosis and providing reasonable next diagnostic steps. Additionally, answers were easy to understand, showcasing a potential benefit in simplifying complex concepts for medical learners. Our results should guide future research into harnessing ChatGPT's potential educational benefits, such as simplifying medical concepts and offering guidance on differential diagnoses and next steps.

Role of artificial intelligence in critical care nutrition support and research

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

Rapid review methods series: Guidance on the use of supportive software

This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RRs) use modified systematic review methods to accelerate the review process while maintaining systematic, transparent and reproducible methods. This paper guides how to use supportive software for RRs.We strongly encourage the use of supportive software throughout RR production. Specifically, we recommend (1) using collaborative online platforms that enable working in parallel, allow for real-time project management and centralise review details; (2) using automation software to support, but not entirely replace a human reviewer and human judgement and (3) being transparent in reporting the methodology and potential risk for bias due to the use of supportive software.

Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges

(1) Background: Approximately 1% of the global population is affected by schizophrenia, a disorder marked by cognitive deficits, delusions, hallucinations, and language issues. It is associated with genetic, neurological, and environmental factors, and linked to dopaminergic hyperactivity and neurotransmitter imbalances. Recent research reveals that patients exhibit significant language impairments, such as reduced verbal output and fluency. Advances in machine learning and natural language processing show potential for early diagnosis and personalized treatments, but additional research is required for the practical application and interpretation of such technology. The objective of this study is to explore the applications of natural language processing in patients diagnosed with schizophrenia. (2) Methods: A scoping review was conducted across multiple electronic databases, including Medline, PubMed, Embase, and PsycInfo. The search strategy utilized a combination of text words and subject headings, focusing on schizophrenia and natural language processing. Systematically extracted information included authors, population, primary uses of the natural language processing algorithms, main outcomes, and limitations. The quality of the identified studies was assessed. (3) Results: A total of 516 eligible articles were identified, from which 478 studies were excluded based on the first analysis of titles and abstracts. Of the remaining 38 studies, 18 were selected as part of this scoping review. The following six main uses of natural language processing were identified: diagnostic and predictive modeling, followed by specific linguistic phenomena, speech and communication analysis, social media and online content analysis, clinical and cognitive assessment, and linguistic feature analysis. (4) Conclusions: This review highlights the main uses of natural language processing in the field of schizophrenia and the need for more studies to validate the effectiveness of natural language processing in diagnosing and treating schizophrenia.

Artificial intelligence in perinatal mental health research: A scoping review

The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment

PURPOSE OF REVIEW

Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.

RECENT FINDINGS

We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis

ABSTRACT

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study's best performing model that evaluated for pulmonary embolism and/or deep vein thrombosis. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with confidence interval (CI) were calculated by DerSimonian and Laird method using a random-effects model. Study quality was assessed using an adapted TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) tool. Thirteen studies were included in the systematic review and 8 had data available for meta-analysis. Pooled sensitivity was 0.931 (95% CI, 0.881-0.962), specificity 0.984 (95% CI, 0.967-0.992), PPV 0.910 (95% CI, 0.865-0.941) and NPV 0.985 (95% CI, 0.977-0.990). All studies met at least 13 of the 21 NLP-modified TRIPOD items, demonstrating fair quality. The highest performing models used vectorization rather than bag-of-words and deep-learning techniques such as convolutional neural networks. There was significant heterogeneity in the studies, and only 4 validated their model on an external data set. Further standardization of ML studies can help progress this novel technology toward real-world implementation.

Semantic similarity is not enough: A novel NLP-based semantic similarity measure in geospatial context

In this study, we addressed two primary challenges: firstly, the issue of domain shift, which pertains to changes in data characteristics or context that can impact model performance, and secondly, the discrepancy between semantic similarity and geographical distance. We employed topic modeling in conjunction with the BERT architecture. Our model was crafted to enhance similarity computations applied to geospatial text, aiming to integrate both semantic similarity and geographical proximity. We tested the model on two datasets, Persian Wikipedia articles and rental property advertisements. The findings demonstrate that the model effectively improved the correlation between semantic similarity and geographical distance. Furthermore, evaluation by real-world users within a recommender system context revealed a notable increase in user satisfaction by approximately 22% for Wikipedia articles and 56% for advertisements.

ChatGPT: perspectives from human-computer interaction and psychology

The release of GPT-4 has garnered widespread attention across various fields, signaling the impending widespread adoption and application of Large Language Models (LLMs). However, previous research has predominantly focused on the technical principles of ChatGPT and its social impact, overlooking its effects on human-computer interaction and user psychology. This paper explores the multifaceted impacts of ChatGPT on human-computer interaction, psychology, and society through a literature review. The author investigates ChatGPT's technical foundation, including its Transformer architecture and RLHF (Reinforcement Learning from Human Feedback) process, enabling it to generate human-like responses. In terms of human-computer interaction, the author studies the significant improvements GPT models bring to conversational interfaces. The analysis extends to psychological impacts, weighing the potential of ChatGPT to mimic human empathy and support learning against the risks of reduced interpersonal connections. In the commercial and social domains, the paper discusses the applications of ChatGPT in customer service and social services, highlighting the improvements in efficiency and challenges such as privacy issues. Finally, the author offers predictions and recommendations for ChatGPT's future development directions and its impact on social relationships.

Unlocking the Black Box? A Comprehensive Exploration of Large Language Models in Rehabilitation

ABSTRACT

Rehabilitation is a vital component of health care, aiming to restore function and improve the well-being of individuals with disabilities or injuries. Nevertheless, the rehabilitation process is often likened to a " black box ," with complexities that pose challenges for comprehensive analysis and optimization. The emergence of large language models offers promising solutions to better understand this " black box ." Large language models excel at comprehending and generating human-like text, making them valuable in the healthcare sector. In rehabilitation, healthcare professionals must integrate a wide range of data to create effective treatment plans, akin to selecting the best ingredients for the " black box. " Large language models enhance data integration, communication, assessment, and prediction.This article delves into the ground-breaking use of large language models as a tool to further understand the rehabilitation process. Large language models address current rehabilitation issues, including data bias, contextual comprehension, and ethical concerns. Collaboration with healthcare experts and rigorous validation is crucial when deploying large language models. Integrating large language models into rehabilitation yields insights into this intricate process, enhancing data-driven decision making, refining clinical practices, and predicting rehabilitation outcomes. Although challenges persist, large language models represent a significant stride in rehabilitation, underscoring the importance of ethical use and collaboration.

Generative artificial intelligence in surgery

Generative artificial intelligence is able to collect, extract, digest, and generate information in an understandable way for humans. As the first surgical applications of generative artificial intelligence are applied, this perspective paper aims to provide a comprehensive overview of current applications and future perspectives for the application of generative artificial intelligence in surgery, from preoperative planning to training. Generative artificial intelligence can be used before surgery for planning and decision support by extracting patient information and providing patients with information and simulation regarding the procedure. Intraoperatively, generative artificial intelligence can document data that is normally not captured as intraoperative adverse events or provide information to help decision-making. Postoperatively, GAIs can help with patient discharge and follow-up. The ability to provide real-time feedback and store it for later review is an important capability of GAIs. GAI applications are emerging as highly specialized, task-specific tools for tasks such as data extraction, synthesis, presentation, and communication within the realm of surgery. GAIs have the potential to play a pivotal role in facilitating interaction between surgeons and artificial intelligence.

Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models

BACKGROUND

Natural language processing (NLP) tools including recently developed large language models (LLMs) have myriad potential applications in medical care and research, including the efficient labeling and classification of unstructured text such as electronic health record (EHR) notes. This opens the door to large-scale projects that rely on variables that are not typically recorded in a structured form, such as patient signs and symptoms.

OBJECTIVES

This study is designed to acquaint the emergency medicine research community with the foundational elements of NLP, highlighting essential terminology, annotation methodologies, and the intricacies involved in training and evaluating NLP models. Symptom characterization is critical to urinary tract infection (UTI) diagnosis, but identification of symptoms from the EHR has historically been challenging, limiting large-scale research, public health surveillance, and EHR-based clinical decision support. We therefore developed and compared two NLP models to identify UTI symptoms from unstructured emergency department (ED) notes.

METHODS

The study population consisted of patients aged ≥ 18 who presented to an ED in a northeastern U.S. health system between June 2013 and August 2021 and had a urinalysis performed. We annotated a random subset of 1250 ED clinician notes from these visits for a list of 17 UTI symptoms. We then developed two task-specific LLMs to perform the task of named entity recognition: a convolutional neural network-based model (SpaCy) and a transformer-based model designed to process longer documents (Clinical Longformer). Models were trained on 1000 notes and tested on a holdout set of 250 notes. We compared model performance (precision, recall, F1 measure) at identifying the presence or absence of UTI symptoms at the note level.

RESULTS

A total of 8135 entities were identified in 1250 notes; 83.6% of notes included at least one entity. Overall F1 measure for note-level symptom identification weighted by entity frequency was 0.84 for the SpaCy model and 0.88 for the Longformer model. F1 measure for identifying presence or absence of any UTI symptom in a clinical note was 0.96 (232/250 correctly classified) for the SpaCy model and 0.98 (240/250 correctly classified) for the Longformer model.

CONCLUSIONS

The study demonstrated the utility of LLMs and transformer-based models in particular for extracting UTI symptoms from unstructured ED clinical notes; models were highly accurate for detecting the presence or absence of any UTI symptom on the note level, with variable performance for individual symptoms.

Evaluation of AI-generated responses by different artificial intelligence chatbots to the clinical decision-making case-based questions in oral and maxillofacial surgery

OBJECTIVES

This study aims to evaluate the correctness of the generated answers by Google Bard, GPT-3.5, GPT-4, Claude-Instant, and Bing chatbots to decision-making clinical questions in the oral and maxillofacial surgery (OMFS) area.

STUDY DESIGN

A group of 3 board-certified oral and maxillofacial surgeons designed a questionnaire with 50 case-based questions in multiple-choice and open-ended formats. Answers of chatbots to multiple-choice questions were examined against the chosen option by 3 referees. The chatbots' answers to the open-ended questions were evaluated based on the modified global quality scale. A P-value under .05 was considered significant.

RESULTS

Bard, GPT-3.5, GPT-4, Claude-Instant, and Bing answered 34%, 36%, 38%, 38%, and 26% of the questions correctly, respectively. In open-ended questions, GPT-4 scored the most answers evaluated as grades "4" or "5," and Bing scored the most answers evaluated as grades "1" or "2." There were no statistically significant differences between the 5 chatbots in responding to the open-ended (P = .275) and multiple-choice (P = .699) questions.

CONCLUSION

Considering the major inaccuracies in the responses of chatbots, despite their relatively good performance in answering open-ended questions, this technology yet cannot be trusted as a consultant for clinicians in decision-making situations.

Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria

Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial.

Artificial intelligence in drug repurposing for rare diseases: a mini-review

Drug repurposing, the process of identifying new uses for existing drugs beyond their original indications, offers significant advantages in terms of reduced development time and costs, particularly in addressing unmet medical needs in rare diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, and by leveraging AI technologies, researchers aim to overcome some of the challenges associated with rare diseases. This review presents concrete case studies, as well as pre-existing platforms, initiatives, and companies that demonstrate the application of AI for drug repurposing in rare diseases. Despite representing a modest part of the literature compared to other diseases such as COVID-19 or cancer, the growing interest, and investment in AI for drug repurposing in rare diseases underscore its potential to accelerate treatment availability for patients with unmet medical needs.

Applications of natural language processing tools in the surgical journey

Background

Natural language processing tools are becoming increasingly adopted in multiple industries worldwide. They have shown promising results however their use in the field of surgery is under-recognised. Many trials have assessed these benefits in small settings with promising results before large scale adoption can be considered in surgery. This study aims to review the current research and insights into the potential for implementation of natural language processing tools into surgery.

Methods

A narrative review was conducted following a computer-assisted literature search on Medline, EMBASE and Google Scholar databases. Papers related to natural language processing tools and consideration into their use for surgery were considered.

Results

Current applications of natural language processing tools within surgery are limited. From the literature, there is evidence of potential improvement in surgical capability and service delivery, such as through the use of these technologies to streamline processes including surgical triaging, data collection and auditing, surgical communication and documentation. Additionally, there is potential to extend these capabilities to surgical academia to improve processes in surgical research and allow innovation in the development of educational resources. Despite these outcomes, the evidence to support these findings are challenged by small sample sizes with limited applicability to broader settings.

Conclusion

With the increasing adoption of natural language processing technology, such as in popular forms like ChatGPT, there has been increasing research in the use of these tools within surgery to improve surgical workflow and efficiency. This review highlights multifaceted applications of natural language processing within surgery, albeit with clear limitations due to the infancy of the infrastructure available to leverage these technologies. There remains room for more rigorous research into broader capability of natural language processing technology within the field of surgery and the need for cross-sectoral collaboration to understand the ways in which these algorithms can best be integrated.

The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling

ChatGPT has demonstrated impressive abilities and impacted various aspects of human society since its creation, gaining widespread attention from different social spheres. This study aims to comprehensively assess public perception of ChatGPT on Reddit. The dataset was collected via Reddit, a social media platform, and includes 23,733 posts and comments related to ChatGPT. Firstly, to examine public attitudes, this study conducts content analysis utilizing topic modeling with the Latent Dirichlet Allocation (LDA) algorithm to extract pertinent topics. Furthermore, sentiment analysis categorizes user posts and comments as positive, negative, or neutral using Textblob and Vader in natural language processing. The result of topic modeling shows that seven topics regarding ChatGPT are identified, which can be grouped into three themes: user perception, technical methods, and impacts on society. Results from the sentiment analysis show that 61.6% of the posts and comments hold favorable opinions on ChatGPT. They emphasize ChatGPT's ability to prompt and engage in natural conversations with users, without relying on complex natural language processing. It provides suggestions for ChatGPT developers to enhance its usability design and functionality. Meanwhile, stakeholders, including users, should comprehend the advantages and disadvantages of ChatGPT in human society to promote ethical and regulated implementation of the system.

Introduction to Large Language Models (LLMs) for dementia care and research

Introduction

Dementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement. In light of the recent advent of Large Language Models (LLMs) such as ChatGPT, this paper aims to thoroughly analyse their potential applications and usefulness in dementia care and research.

Method

To this end, we offer an introduction into LLMs, outlining the key features, capabilities, limitations, potential risks, and practical considerations for deployment as easy-to-use software (e.g., smartphone apps). We then explore various domains related to dementia, identifying opportunities for LLMs to enhance understanding, diagnostics, and treatment, with a broader emphasis on improving patient care. For each domain, the specific contributions of LLMs are examined, such as their ability to engage users in meaningful conversations, deliver personalized support, and offer cognitive enrichment. Potential benefits encompass improved social interaction, enhanced cognitive functioning, increased emotional well-being, and reduced caregiver burden. The deployment of LLMs in caregiving frameworks also raises a number of concerns and considerations. These include privacy and safety concerns, the need for empirical validation, user-centered design, adaptation to the user's unique needs, and the integration of multimodal inputs to create more immersive and personalized experiences. Additionally, ethical guidelines and privacy protocols must be established to ensure responsible and ethical deployment of LLMs.

Results

We report the results on a questionnaire filled in by people with dementia (PwD) and their supporters wherein we surveyed the usefulness of different application scenarios of LLMs as well as the features that LLM-powered apps should have. Both PwD and supporters were largely positive regarding the prospect of LLMs in care, although concerns were raised regarding bias, data privacy and transparency.

Discussion

Overall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers. The findings underscore the importance of further research and development in this field to fully harness the benefits of LLMs and maximize their potential for improving the lives of individuals living with dementia.

Exploring hidden pathways to sustainable manufacturing for cyber-physical production systems

Future manufacturing scenarios will likely be built around cyber-physical production systems. To succeed, this new manufacturing paradigm will also have to comply with the golden rule of sustainability. However, the concept of sustainability as defined in a number of high-level policy documents and recommendations requires disambiguation. The paper introduces HITECS, a novel, context-independent text analytics methodology for hidden correlation analysis in documents. HITECS is based on the assumption that there is a strong link between a concept and the words implicitly chosen to explain it. The analysis is based on the combination of bare words frequency and cosine similarity, excluding trivial, first-level terms (titles, keywords, and definitions). Processing a corpus of generally accepted documents related to various definitions and requirements of sustainability unfolded their hidden correlations and some common key concepts. These results indicate that terms like access, inclusion, global, change, together with others like resource, share, and integration, are among leading concepts in the high-level documents discussing the requirements of sustainability. A similar analysis in the domain of cyber-physical production systems shows strong conceptual overlaps but also gaps indicating pathways for future research and actions.

Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient-doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes.

Pilot Study of a Mobile Phone Chatbot for Medication Adherence and Toxicity Management Among Patients With GI Cancers on Capecitabine

PURPOSE

Capecitabine is an oral chemotherapy used to treat many gastrointestinal cancers. Its complex dosing and narrow therapeutic index make medication adherence and toxicity management crucial for quality care.

METHODS

We conducted a pilot study of PENNY-GI, a mobile phone text messaging-based chatbot that leverages algorithmic surveys and natural language processing to promote medication adherence and toxicity management among patients with gastrointestinal cancers on capecitabine. Eligibility initially included all capecitabine-containing regimens but was subsequently restricted to capecitabine monotherapy because of challenges in integrating PENNY-GI with radiation and intravenous chemotherapy schedules. We used design thinking principles and real-time data on safety, accuracy, and usefulness to make iterative refinements to PENNY-GI with the goal of minimizing the proportion of text messaging exchanges with incorrect medication or symptom management recommendations. All patients were invited to participate in structured exit interviews to provide feedback on PENNY-GI.

RESULTS

We enrolled 40 patients (median age 64.5 years, 52.5% male, 62.5% White, 55.0% with colorectal cancer, 50.0% on capecitabine monotherapy). We identified 284 of 3,895 (7.3%) medication-related and 13 of 527 (2.5%) symptom-related text messaging exchanges with incorrect recommendations. In exit interviews with 24 patients, participants reported finding the medication reminders reliable and user-friendly, but the symptom management tool was too simplistic to be helpful.

CONCLUSION

Although PENNY-GI provided accurate recommendations in >90% of text messaging exchanges, we identified multiple limitations with respect to the intervention's generalizability, usefulness, and scalability. Lessons from this pilot study should inform future efforts to develop and implement digital health interventions in oncology.

[Significance of natural language processing and chat-based generative language models]

BACKGROUND

Natural language processing (NLP) has experienced significant growth in recent years and shows potential for broad impacts in scientific research and clinical practice.

OBJECTIVE

This study comprises an exploration of the role of NLP in scientific research and its subsequent effects on traditional publication practices, as well as an evaluation of the opportunities and challenges offered by large language models (LLM) and a reflection on necessary paradigm shifts in research culture.

MATERIALS AND METHODS

Current LLMs, such as ChatGPT, and their potential applications were compared and assessed. An analysis of the literature and case studies on the integration of LLMs into scientific and clinical practice was conducted.

RESULTS AND CONCLUSION

LLMs provide enhanced access to and processing capabilities of text-based information and represent a vast potential for (medical) research as well as daily clinical practice. Chat-based LLMs enable efficient completion of often time-consuming tasks, but due to their tendency for hallucinations, have a significant limitation. Current developments require critical examination and a paradigm shift to fully exploit the benefits of LLMs and minimize potential risks.

Reality monitoring and metacognitive judgments in a false-memory paradigm

How well do we distinguish between different memory sources when the information from imagination and perception is similar? And how do metacognitive (confidence) judgments differ across different sources of experiences? To study these questions, we developed a reality monitoring task using semantically related words from the Deese-Roediger-McDermott (DRM) paradigm of false memories. In an orientation phase, participants either perceived word pairs or had to voluntarily imagine the second word of a word pair. In a test phase, participants viewed words and had to judge whether the paired word was previously perceived, imagined, or new. Results revealed an interaction between memory source and judgment type on both response rates and confidence judgments: reality monitoring was better for new and perceived (compared to imagined) sources, and participants often incorrectly reported imagined experiences to be perceived. Individuals exhibited similar confidence between correct imagined source judgments and incorrect imagined sources reported to be perceived. Modeling results indicated that the observed judgments were likely due to an externalizing bias (i.e., a bias to judge the memory source as perceived). Additionally, we found that overall metacognitive ability was best in the perceived source. Together, these results reveal a source-dependent effect on response rates and confidence ratings, and provide evidence that observers are surprisingly prone to externalizing biases when monitoring their own memories.

Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

Deep learning in structural bioinformatics: current applications and future perspectives

In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.

Dual-Path Graph Neural Network with Adaptive Auxiliary Module for Link Prediction

Link prediction, which has important applications in many fields, predicts the possibility of the link between two nodes in a graph. Link prediction based on Graph Neural Network (GNN) obtains node representation and graph structure through GNN, which has attracted a growing amount of attention recently. However, the existing GNN-based link prediction approaches possess some shortcomings. On the one hand, because a graph contains different types of nodes, it leads to a great challenge for aggregating information and learning node representation from its neighbor nodes. On the other hand, the attention mechanism has been an effect instrument for enhancing the link prediction performance. However, the traditional attention mechanism is always monotonic for query nodes, which limits its influence on link prediction. To address these two problems, a Dual-Path Graph Neural Network (DPGNN) for link prediction is proposed in this study. First, we propose a novel Local Random Features Augmentation for Graph Convolution Network as a baseline of one path. Meanwhile, Graph Attention Network version 2 based on dynamic attention mechanism is adopted as a baseline of the other path. And then, we capture more meaningful node representation and more accurate link features by concatenating the information of these two paths. In addition, we propose an adaptive auxiliary module for better balancing the weight of auxiliary tasks, which brings more benefit to link prediction. Finally, extensive experiments verify the effectiveness and superiority of our proposed DPGNN for link prediction.

Detecting anomalous proteins using deep representations

Many advances in biomedicine can be attributed to identifying unusual proteins and genes. Many of these proteins' unique properties were discovered by manual inspection, which is becoming infeasible at the scale of modern protein datasets. Here, we propose to tackle this challenge using anomaly detection methods that automatically identify unexpected properties. We adopt a state-of-the-art anomaly detection paradigm from computer vision, to highlight unusual proteins. We generate meaningful representations without labeled inputs, using pretrained deep neural network models. We apply these protein language models (pLM) to detect anomalies in function, phylogenetic families, and segmentation tasks. We compute protein anomaly scores to highlight human prion-like proteins, distinguish viral proteins from their host proteome, and mark non-classical ion/metal binding proteins and enzymes. Other tasks concern segmentation of protein sequences into folded and unstructured regions. We provide candidates for rare functionality (e.g. prion proteins). Additionally, we show the anomaly score is useful in 3D folding-related segmentation. Our novel method shows improved performance over strong baselines and has objectively high performance across a variety of tasks. We conclude that the combination of pLM and anomaly detection techniques is a valid method for discovering a range of global and local protein characteristics.