Development Of Natural Language Processing Research Articles

Challenges of Natural Language Processing from a Linguistic Perspective

As the foundation of artificial intelligence, natural language processing is a research field with great development prospects, closely related to linguistics. Linguistic research has realized the initial natural language processing, and the development of natural language processing has raised higher requirements for linguistic research. Although natural language processing technology has achieved some results, there is still a distance from the most natural human-machine interaction. Language is the expression form of human thinking, and the fundamental purpose of artificial intelligence is to simulate human thinking with computers. To achieve this goal, it is necessary to start with simulating human natural language. This paper mainly analyzes the current challenges of natural language processing from a linguistic perspective, aiming to further improve the level of natural language processing.

Personal Data in Artificial Intelligence Systems: Natural Language Processing Technology

Objective: to conceptualize, from the viewpoint of personal data protection legislation, the development of natural language processing technology, identifying possible legal barriers to such development and directions for further research of the issue.Methods: the research is based on general scientific methods of cognition, along with which formal-legal and comparative-legal methods were applied, as well as the method of theoretical modeling.Results: it was found that the observance of personal data regime natural language processing in the development of natural language processing technology leads technology, to a conflict between private-legal and public-legal interests, which, personal data in turn, creates obstacles for further development of this technology. The shortcomings of the existing legal order are shown, namely, the insufficient correspondence to the technical features of technology development. This may lead to the risks of excessive regulation, or, on the contrary, to the risks of neglecting critical areas that require protection. Problems in qualifying the data involved in the technology development are outlined. An attempt is made to define the limits of ensuring the lawfulness of personal data processing within the natural language processing technology. The material, temporal and territorial effect of the legal regulation in this field is identified as the limits of ensuring the legality. The author touches upon the possibility of using personal data as a consideration, which is important for the development of natural language processing technology and for the improvement of the information and communication technology industry.Scientific novelty: the paper supplements the scientific discussion on the legal regulation of personal data processing by artificial intelligence systems with an analysis of natural language processing technology. The latter is insufficiently studied, making it relevant to research information law, namely, the legal relations arising around artificial intelligence systems, and to assess the impact of a personal data regime on the development of natural language processing technology.Practical relevance: the applied aspects of the problems researched and the results obtained can be used to improve the legal regulation of public relations in the field of creation and development of artificial intelligence, as well as to identify and assess the legal risks arising in the personal data processing by developers of digital products based on natural language processing technology.

Knowledge-enhanced model with dual-graph interaction for confusing legal charge prediction

The rapid development of natural language processing (NLP) technologies has enabled the emergence of legal intelligence assistance systems, with legal charge prediction (LCP) being a critical technology. The automatic LCP aims to determine the final charges based on fact descriptions of criminal cases. LCP assists human judges in managing workloads and improving efficiency, provides accessible legal guidance for individuals, and supports enterprises in litigation financing and compliance monitoring. However, distinguishing between confusing charges in real-world judicial practice remains a significant challenge. Most exist works cannot effectively capture complex relationships and discern subtle differences in fact descriptions while ignoring the legal schematic knowledge. In order to improve confusing LCP performance, we propose a novel knowledge-aware model for legal charge prediction that leverages Graph Neural Networks (GNNs) to capture complex relationships within criminal case descriptions. Specifically, the model constructs structural and semantic graphs from fact descriptions and integrates information from both through a dual-graph interaction process. A legal knowledge transformer generates key knowledge representations at schema and charge levels, while a knowledge matching network incorporates hierarchical charge knowledge into facts. Besides, we also propose two real-world datasets namely Criminal-All and Criminal-Confusing, containing 203 different charges and 86 confusing charges, respectively. To the best of our knowledge, these datasets are the first well-organized datasets for confusing LCP task. Experimental results demonstrate that the proposed model outperforms baselines and significantly improves the distinction of confusing charges, providing valuable support for intelligent legal judgment systems.

Assessing the utility of natural language processing for detecting postoperative complications from free medical text.

Postoperative complication rates are often assessed through administrative data, although this method has proven to be imprecise. Recently, new developments in natural language processing have shown promise in detecting specific phenotypes from free medical text. Using the clinical challenge of extracting four specific and frequently undercoded postoperative complications (pneumonia, urinary tract infection, sepsis, and septic shock), it was hypothesized that natural language processing would capture postoperative complications on a par with human-level curation from electronic health record free medical text. Electronic health record data were extracted for surgical cases (across 11 surgical sub-specialties) from 18 hospitals in the Capital and Zealand regions of Denmark that were performed between May 2016 and November 2021. The data set was split into training/validation/test sets (30.0%/48.0%/22.0%). Model performance was compared with administrative data and manual extraction of the test data set. Data were obtained for 17 486 surgical cases. Natural language processing achieved a receiver operating characteristic area under the curve of 0.989 for urinary tract infection, 0.993 for pneumonia, 0.992 for sepsis, and 0.998 for septic shock, whereas administrative data achieved a receiver operating characteristic area under the curve of 0.595 for urinary tract infection, 0.624 for pneumonia, 0.571 for sepsis, and 0.625 for septic shock. The natural language processing approach was able to capture complications with acceptable performance, which was superior to administrative data. In addition, the model performance approached that of manual curation and thereby offers a potential pathway for complete real-time coverage of postoperative complications across surgical procedures based on natural language processing assessment of electronic health record free medical text.

Rule-Based Text Classification of Dental Diagnosis.

Unstructured medical records boast an abundance of information that could greatly facilitate medical decision-making and improve patient care. With the development of Natural Language Processing (NLP) methodology, the free-text medical data starts to attract more and more research attention. Most existing studies try to leverage the power of such unstructured data using Machine Learning algorithms, which would usually require a relatively large training set, and high computational capacity. However, when faced with a smaller-scale project, opting for an alternative approach may be more effective and practical. This project proposes an efficient and light-weight rule-based approach to categorize dental diagnosis data. It not only fills the void of dental records in the medical free-text processing area, but also demonstrates that with expertly designed research structure and proper implementation, simple method could achieve our study goal very competently.

MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction

BackgroundEpi-transcriptome regulation through post-transcriptional RNA modifications is essential for all RNA types. Precise recognition of RNA modifications is critical for understanding their functions and regulatory mechanisms. However, wet experimental methods are often costly and time-consuming, limiting their wide range of applications. Therefore, recent research has focused on developing computational methods, particularly deep learning (DL). Bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and the transformer have demonstrated achievements in modification site prediction. However, BiLSTM cannot achieve parallel computation, leading to a long training time, CNN cannot learn the dependencies of the long distance of the sequence, and the Transformer lacks information interaction with sequences at different scales. This insight underscores the necessity for continued research and development in natural language processing (NLP) and DL to devise an enhanced prediction framework that can effectively address the challenges presented.ResultsThis study presents a multi-scale self- and cross-attention network (MSCAN) to identify the RNA methylation site using an NLP and DL way. Experiment results on twelve RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) reveal that the area under the receiver operating characteristic of MSCAN obtains respectively 98.34%, 85.41%, 97.29%, 96.74%, 99.04%, 79.94%, 76.22%, 65.69%, 92.92%, 92.03%, 95.77%, 89.66%, which is better than the state-of-the-art prediction model. This indicates that the model has strong generalization capabilities. Furthermore, MSCAN reveals a strong association among different types of RNA modifications from an experimental perspective. A user-friendly web server for predicting twelve widely occurring human RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) is available at http://47.242.23.141/MSCAN/index.php.ConclusionsA predictor framework has been developed through binary classification to predict RNA methylation sites.

Aircraft Behavior Recognition on Trajectory Data with a Multimodal Approach

Moving traces are essential data for target detection and associated behavior recognition. Previous studies have used time–location sequences, route maps, or tracking videos to establish mathematical recognition models for behavior recognition. The multimodal approach has seldom been considered because of the limited modality of sensing data. With the rapid development of natural language processing and computer vision, the multimodal model has become a possible choice to process multisource data. In this study, we have proposed a mathematical model for aircraft behavior recognition with joint data manners. The feature abstraction, cross-modal fusion, and classification layers are included in the proposed model for obtaining multiscale features and analyzing multimanner information. Attention has been placed on providing self- and cross-relation assessments on the spatiotemporal and geographic data related to a moving object. We have adopted both a feedforward network and a softmax function to form the classifier. Moreover, we have enabled a modality-increasing phase, combining longitude and latitude sequences with related geographic maps to avoid monotonous data. We have collected an aircraft trajectory dataset of longitude and latitude sequences for experimental validation. We have demonstrated the excellent behavior recognition performance of the proposed model joint with the modality-increasing phase. As a result, our proposed methodology reached the highest accuracy of 95.8% among all the adopted methods, demonstrating the effectiveness and feasibility of trajectory-based behavior recognition.

Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.

Information regarding opioid use disorder (OUD) status and severity is important for patient care. Clinical notes provide valuable information for detecting and characterizing problematic opioid use, necessitating development of natural language processing (NLP) tools, which in turn requires reliably labeled OUD-relevant text and understanding of documentation patterns. To inform automated NLP methods, we aimed to develop and evaluate an annotation schema for characterizing OUD and its severity, and to document patterns of OUD-relevant information within clinical notes of heterogeneous patient cohorts. We developed an annotation schema to characterize OUD severity based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. In total, 2 annotators reviewed clinical notes from key encounters of 100 adult patients with varied evidence of OUD, including patients with and those without chronic pain, with and without medication treatment for OUD, and a control group. We completed annotations at the sentence level. We calculated severity scores based on annotation of note text with 18 classes aligned with criteria for OUD severity and determined positive predictive values for OUD severity. The annotation schema contained 27 classes. We annotated 1436 sentences from 82 patients; notes of 18 patients (11 of whom were controls) contained no relevant information. Interannotator agreement was above 70% for 11 of 15 batches of reviewed notes. Severity scores for control group patients were all 0. Among noncontrol patients, the mean severity score was 5.1 (SD 3.2), indicating moderate OUD, and the positive predictive value for detecting moderate or severe OUD was 0.71. Progress notes and notes from emergency department and outpatient settings contained the most and greatest diversity of information. Substance misuse and psychiatric classes were most prevalent and highly correlated across note types with high co-occurrence across patients. Implementation of the annotation schema demonstrated strong potential for inferring OUD severity based on key information in a small set of clinical notes and highlighting where such information is documented. These advancements will facilitate NLP tool development to improve OUD prevention, diagnosis, and treatment.

Graph global attention network with memory: A deep learning approach for fake news detection

With the proliferation of social media, the detection of fake news has become a critical issue that poses a significant threat to society. The dissemination of fake information can lead to social harm and damage the credibility of information. To address this issue, deep learning has emerged as a promising approach, especially with the development of Natural Language Processing (NLP). This study introduces a novel approach called Graph Global Attention Network with Memory (GANM) for detecting fake news. This approach leverages NLP techniques to encode nodes with news context and user content. It employs three graph convolutional networks to extract informative features from the news propagation network and aggregates endogenous and exogenous user information. This methodology aims to address the challenge of identifying fake news within the context of social media. Innovatively, the GANM combines two strategies. First, a novel global attention mechanism with memory is employed in the GANM to learn the structural homogeneity of news propagation networks, which is the attention mechanism of a single graph with a history of all graphs. Second, we design a module for partial key information learning aggregation to emphasize the acquisition of partial key information in the graph and merge node-level embeddings with graph-level embeddings into fine-grained joint information. Our proposed method provides a new direction in news detection research with a combination of global and partial information and achieves promising performance on real-world datasets.

Advances in the Automatic Lemmatization of Old English: Class IV Strong Verbs (L-Y)

The morphological features of an inflectional language like Old English (OE), which also presents generalized spelling inconsistencies, limit the use of lemmatizing and tagging tools that can be applied to natural languages. Consequently, the development of Natural Language Processing (NLP) models, which crucially depend on lemmatized corpora, is slowed down. Against this background, this article develops a lemmatizer within the framework of Morphological Generation that allows for the type-based automatic lemmatization of OE class IV strong verbs (L–Y). The lemmatizer incorporates a set of algorithms to account for features of inflectional, derivational, morphophonological and diatopic variation. The generated forms are automatically compared with Taylor et al. (2003) and Healey et al. (2004) to confirm their attestation and are assigned a lemma. Overall, the research proves successful in setting up form-lemma associations, while highlighting areas of ambiguity and mismatches. The main conclusion of the article is that taking the route of automatic lemmatization with this methodological framework will contribute to the field of OE lexicography by both lemmatizing attested inflectional forms and by identifying areas for manual revision.

AraPathogen2.0: An Improved Prediction of Plant-Pathogen Protein-Protein Interactions Empowered by the Natural Language Processing Technique.

Plant-pathogen protein-protein interactions (PPIs) play crucial roles in the arm race between plants and pathogens. Therefore, the identification of these interspecies PPIs is very important for the mechanistic understanding of pathogen infection and plant immunity. Computational prediction methods can complement experimental efforts, but their predictive performance still needs to be improved. Motivated by the rapid development of natural language processing and its successful applications in the field of protein bioinformatics, here we present an improved XGBoost-based plant-pathogen PPI predictor (i.e., AraPathogen2.0), in which sequence encodings from the pretrained protein language model ESM2 and Arabidopsis PPI network-related node representations from the graph embedding technique struc2vec are used as input. Stringent benchmark experiments showed that AraPathogen2.0 could achieve a better performance than its precedent version, especially for processing the test data set with novel proteins unseen in the training data.

Revealing the technology development of natural language processing: A Scientific entity-centric perspective

Most studies on technology development have been conducted from a thematic perspective, but the topics are coarse-grained and insufficient to accurately represent technology. The development of automatic entity recognition techniques makes it possible to extract technology-related entities on a large scale. Thus, we perform a more accurate analysis of technology development from an entity-centric perspective. To begin with, we extract technology-related entities such as methods, datasets, metrics, and tools in articles on Natural Language Processing (NLP), and we apply a semi-automatic approach to normalize the entities. Subsequently, we calculate the z-scores of entities based on their co-occurrence networks to measure their impact. We then analyze the development trends of new technologies in the NLP domain since the beginning of the 21st century. The findings of this paper include three aspects: Firstly, the continued increase in the average number of entities per paper implies a growing burden on researchers to acquire relevant technical background knowledge. However, the emergence of pre-trained language models has injected new vitality into the technological innovation of the NLP domain. Secondly, Methods dominate among the 179 high-impact entities. An analysis of the z-score trend about the top 10 entities reveals that pre-trained language models, exemplified by BERT and Transformer, have become mainstream in recent years. Unlike the trend of the other eight method entities, the impact of Wikipedia dataset and BLEU metric has continued to rise in the long term. Thirdly, in recent years, there has been a remarkable surge in popularity for new high-impact technologies than ever before, and their acceptance by researchers has accelerated at an unprecedented speed. Our study provides a new perspective on analyzing technology development in a specific domain. This work can help researchers grasp the current hot technologies in the field of NLP and contribute to the future development of NLP technologies. The source code and dataset for this paper can be accessed at https://github.com/ZH-heng/technology_development.

Low-Resource Language Processing Using Improved Deep Learning with Hunter–Prey Optimization Algorithm

Low-resource language (LRL) processing refers to the development of natural language processing (NLP) techniques and tools for languages with limited linguistic resources and data. These languages often lack well-annotated datasets and pre-training methods, making traditional approaches less effective. Sentiment analysis (SA), which involves identifying the emotional tone or sentiment expressed in text, poses unique challenges for LRLs due to the scarcity of labelled sentiment data and linguistic intricacies. NLP tasks like SA, powered by machine learning (ML) techniques, can generalize effectively when trained on suitable datasets. Recent advancements in computational power and parallelized graphical processing units have significantly increased the popularity of deep learning (DL) approaches built on artificial neural network (ANN) architectures. With this in mind, this manuscript describes the design of an LRL Processing technique that makes use of Improved Deep Learning with Hunter–Prey Optimization (LRLP-IDLHPO). The LRLP-IDLHPO technique enables the detection and classification of different kinds of sentiments present in LRL data. To accomplish this, the presented LRLP-IDLHPO technique initially pre-processes these data to improve their usability. Subsequently, the LRLP-IDLHPO approach applies the SentiBERT approach for word embedding purposes. For the sentiment classification process, the Element-Wise–Attention GRU network (EWAG-GRU) algorithm is used, which is an enhanced version of the recurrent neural network. The EWAG-GRU model is capable of processing temporal features and includes an attention strategy. Finally, the performance of the EWAG-GRU model can be boosted by adding the HPO algorithm for use in the hyperparameter tuning process. A widespread simulation analysis was performed to validate the superior results derived from using the LRLP-IDLHPO approach. The extensive results indicate the significant superiority of the performance of the LRLP-IDLHPO technique compared to the state-of-the-art approaches described in the literature.

Research on Keywords Variations in Linguistics Based on TF-IDF and N-gram

The rapid development of natural language processing (NLP) holds great promise for bridging the divide among languages. One of its main innovative applications is to use broad data to explore the historical trend of a subject. However, since Saussure pioneered modern linguistics, there is relatively inadequate research work done in the linguistic research on the field's variations to comprehensively reveal the linguistic trends. To trace the changes in linguistic research hotspots, we use a dataset of more than 30,000 linguistics-related literature with their titles from the Web of Science and apply NLP techniques to the data consisting of their keywords and publication years. It is found that the co-occurrence relationship between keywords, NGRAM, and their relationship with years can effectively present changes in linguistic research themes. This research is supposed to provide further insights and new methods that can be applied in the field of linguistics and related disciplines.

A workflow to create trait databases from collections of textual taxonomic descriptions

There is a wealth of information about the characteristics (traits) of organisms within collections of taxonomic descriptions of plants and animals called a ‘Flora’ or ‘Fauna’ of a region. However, such knowledge is usually encoded as text paragraphs, and is thus unavailable for immediate analysis. In order to make use of the knowledge embedded in taxon descriptions, text must be organised into standardised, queryable datasets. Despite the recent development of natural language processing (NPL) tools to analyse taxonomic descriptions to extract trait values, the complexity and specificity of these methods currently limits broad application. Accessible and flexible methods for extracting traits across large numbers of taxonomic descriptions are therefore needed. Here we present such an R-based workflow, which can be adapted for use on any organismal group using a language familiar to researchers in the biological sciences. We document a way to (1) assemble tens of thousands of taxonomic descriptions into a standardised format, (2) split the taxon descriptions into different topics, (3) extract trait values as defined by the user, and (4) assign traits described at the genus and family level to lower level taxa to maximise trait coverage. As a case study, we apply the workflow to a collection of taxonomic descriptions drawn from Australia's state and national floras and describe useful techniques for creating workflows and thereby research-grade trait datasets. Using this method, we were able to extract 615,812 trait values from 38 different plant traits. Trait data collated using this method are freely available as part of the AusTraits trait database and have already contributed to analyses in several scientific publications.

The current status and prospects of transformer in multimodality

At present, the attention mechanism represented by transformer has greatly promoted the development of natural language processing (NLP) and image processing (CV). However, in the multimodal field, the application of attention mechanism still mainly focuses on extracting the features of different types of data, and then fusing these features (such as text and image). With the increasing scale of the model and the instability of the Internet data, feature fusion has been difficult to solve the growing variety of multimodal problems for us, and the multimodal field has always lacked a model that can uniformly handle all types of data. In this paper, we first take the CV and NLP fields as examples to review various derived models of transformer. Then, based on the mechanism of word embedding and image embedding, we discuss how embedding with different granularity is handled uniformly under the attention mechanism in multimodal scenes. Further, we reveal that this mechanism will not only be limited to CV and NLP, but the real unified model will be able to handle tasks across data types through pre-training and fine tuning. Finally, on the specific implementation of the unified model, this paper lists several cases, and analyzes the valuable research directions in related fields.

EnML: Multi-label Ensemble Learning for Urdu Text Classification

Exponential growth of electronic data requires advanced multi-label classification approaches for the development of natural language processing (NLP) applications such as recommendation systems, drug reaction detection, hate speech detection, and opinion recognition/mining. To date, several machine and deep learning–based multi-label classification methodologies have been proposed for English, French, German, Chinese, Arabic, and other developed languages. Urdu is the 11th largest language in the world and has no computer-aided multi-label textual news classification approach. Unlike other languages, Urdu is lacking multi-label text classification datasets that can be used to benchmark the performance of existing machine and deep learning methodologies. With an aim to accelerate and expedite research for the development of Urdu multi-label text classification–based applications, this article provides multiple contributions as follows: First, it provides a manually annotated multi-label textual news classification dataset for the Urdu language. Second, it benchmarks the performance of traditional machine learning approaches particularly by adapting three data transformation approaches along with three top-performing machine learning classifiers and four algorithm adaptation-based approaches. Third, it benchmarks performance of 16 existing deep learning approaches and the four most widely used language models. Finally, it provides an ensemble approach that reaps the benefits of three different deep learning architectures to precisely predict different classes associated with a particular Urdu textual document. Experimental results reveal that proposed ensemble approach performance values (87% accuracy, 92% F1-score, and 8% hamming loss) are significantly higher than adapted machine and deep learning–based approaches.

Impact of Chat GPT on Scientific Research: Opportunities, Risks, Limitations, and Ethical Issues

The creation of chatbots, such as Generative Pre-trained Transformer (GPT), is a result of recent developments in natural language processing (NLP). Even though Chat GPT has demonstrated enormous promise in a number of areas, including scientific research, this impact is still developing. This paper attempts to investigate the possibilities, threats, limits, and ethical issues surrounding Chat GPT in scientific research. The assessment of the literature on Chat GPT and scientific research is followed by the presentation of case examples that demonstrate the potential advantages and difficulties of Chat GPT use in scientific research. Finally, we conclude by pointing about the ethical issues that need to be tackled before Chat GPT can be completely utilized in scientific research.

Generative Artificial Intelligence GPT-4 Accelerates Knowledge Mining and Machine Learning for Synthetic Biology.

Knowledge mining from synthetic biology journal articles for machine learning (ML) applications is a labor-intensive process. The development of natural language processing (NLP) tools, such as GPT-4, can accelerate the extraction of published information related to microbial performance under complex strain engineering and bioreactor conditions. As a proof of concept, we proposed prompt engineering for a GPT-4 workflow pipeline to extract knowledge from 176 publications on two oleaginous yeasts (Yarrowia lipolytica and Rhodosporidium toruloides). After human intervention, the pipeline obtained a total of 2037 data instances. The structured data sets and feature selections enabled ML approaches (e.g., a random forest model) to predict Yarrowia fermentation titers with decent accuracy (R2 of 0.86 for unseen test data). Via transfer learning, the trained model could assess the production potential of the engineered nonconventional yeast, R. toruloides, for which there are fewer published reports. This work demonstrated the potential of generative artificial intelligence to streamline information extraction from research articles, thereby facilitating fermentation predictions and biomanufacturing development.

Healthcare: A Growing Role for Large Language Models and Generative AI

Abstract: Large language models and generative artificial intelligence (GAI) have recently demonstrated significant promise for revolutionizing a range of industries, including healthcare. The paper investigates how these cutting-edge AI developments are transforming healthcare applications. We focus on how big language models, like GPT-3 (generative pretrained transformer), Visual ChatGPT and generative AI, such Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can be applied to solve important problems in the healthcare sector. Medical text analysis is one of the main uses of massive language models in the visual ChatGPT healthcare industry. These models have impressive natural language processing abilities that make it possible to effectively extract important information from electronic health records (EHRs), biomedical text data from large biobanks, scholarly articles and patient notes. The Biomedical Transformer Model represents a ground-breaking development in natural language processing for the biomedical field, exhibiting outstanding performance in comprehending and producing textual data. It opens up new avenues for biomedical research, diagnosis, and personalized therapy when combined with Multimodal Biomedical AI, which makes use of numerous data sources, including pictures, genomes, and clinical records. On the other side, generative AI has made great progress in medical picture analysis such as MRI scans and X-rays. The outstanding performance of GANs in medical picture synthesis and augmentation has helped to increase the precision and accuracy of diagnosis. Due to the issues with small and uneven medical datasets, VAEs have proven crucial in producing realistic medical images for training and research reasons. In addition to describing the various generative AI tools used in healthcare, this paper also provides an overview of multimodal medical LLMs and the biomedical transformer LLMs in the healthcare industry. Large language models and generative AI have great potential, but ethical issues and data privacy are still major problems in healthcare applications. Further, we investigate the potential role of multimodal medical LLMs as the foundation for novel assistive technologies in the fields of professional medicine, medical research, and consumer applications in the healthcare industry