ABSTRACT
Background: The emergence of artificial intelligence (AI) tools such as ChatGPT and Bard is disrupting a broad swathe of fields, including medicine. In pediatric medicine, AI is also increasingly being used across multiple subspecialties. However, the practical application of AI still faces a number of key challenges. Consequently, there is a requirement for a concise overview of the roles of AI across the multiple domains of pediatric medicine, which the current study seeks to address. Aim: To systematically assess the challenges, opportunities, and explainability of AI in pediatric medicine. Methodology: A systematic search was carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, and grey literature using search terms related to machine learning (ML) and AI for the years 2016 to 2022 in the English language. A total of 210 articles were retrieved that were screened with PRISMA for abstract, year, language, context, and proximal relevance to research aims. A thematic analysis was carried out to extract findings from the included studies. Results: Twenty articles were selected for data abstraction and analysis, with three consistent themes emerging from these articles. In particular, eleven articles address the current state-of-the-art application of AI in diagnosing and predicting health conditions such as behavioral and mental health, cancer, syndromic and metabolic diseases. Five articles highlight the specific challenges of AI deployment in pediatric medicines: data security, handling, authentication, and validation. Four articles set out future opportunities for AI to be adapted: the incorporation of Big Data, cloud computing, precision medicine, and clinical decision support systems. These studies collectively critically evaluate the potential of AI in overcoming current barriers to adoption. Conclusion: AI is proving disruptive within pediatric medicine and is presently associated with challenges, opportunities, and the need for explainability. AI should be viewed as a tool to enhance and support clinical decision-making rather than a substitute for human judgement and expertise. Future research should consequently focus on obtaining comprehensive data to ensure the generalizability of research findings.
ABSTRACT
ChatGPT, as a high-profile generative large language model (LLM) of artificial intelligence(AI), brings people immersive learning experience and a unique interactive platform; meanwhile, it provides an innovative tool and new opportunities for the development in many fields. With the increasing importance of radiation medicine in disease diagnosis and treatment, manned spaceflight, and nuclear energy and nuclear technology, it can be foreseen that AI LLMs like ChatGPT will play an important role in the development of radiation medicine. This article reviews the application prospects and challenges of ChatGPT in radiation medicine, aiming to promote the application research of AI LLMs in radiation medicine.
ABSTRACT
Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.
Subject(s)
Anti-Bacterial Agents/chemistry , Amino Acid Sequence , Antimicrobial Cationic Peptides/chemistry , Antimicrobial Peptides , Natural Language ProcessingABSTRACT
Previous researches about the brain mechanism of aphasia mainly focused on the correspondence between cortical brain regions and language function. Now, more and more researches have found that the connection of white matter tracts in the brain plays an important role in language function. Dual-stream language model hypothesizes that the process of language can be considered as two parallel pathways, dorsal and ventral. The white matter fibers of dorsal stream include arcuate fasciculus and superior longitudinal fasciculi, which are mainly involved in the production of language. The white matter fibers of ventral stream include the inferior frontal-occipital fasciculus, inferior longitudinal fasciculus and uncinate fasciculus, which are mainly responsible for language understanding. The conception of connection mode and specific functional role of these fibers in the language network of patients with aphasia is helpful for the assessment of the features and severity of the patient's language dysfunction and outcome, to guide clinical precision rehabilitation. More researches are needed to elaborate the interaction between the dorsal and ventral streams to well know the brain's language network processing mechanism.
ABSTRACT
The cognitive neuroscience researches about post-stroke aphasia provide the interpretation of all aspects of linguistics. The word-picture research paradigm can be applied to assess different types of aphasia, in various ways of stimulation modes and models. It is more helpful combining functional magenetic resonance imaging to research the mechanism of brain damage and recovery objectively. The interactive application of language task and imaging has also become a new direction in the mechanism study of aphasia.