Innovating Healthcare: The Role of ChatGPT in Streamlining Hospital Workflow in the Future

Large multimodal models assist in psychiatry disorders prevention and diagnosis of students

Students are considered one of the groups most affected by psychological problems. Given the highly dangerous nature of mental illnesses and the increasingly serious state of global mental health, it is imperative for us to explore new methods and approaches concerning the prevention and treatment of mental illnesses. Large multimodal models (LMMs), as the most advanced artificial intelligence models (i.e. ChatGPT-4), have brought new hope to the accurate prevention, diagnosis, and treatment of psychiatric disorders. The assistance of these models in the promotion of mental health is critical, as the latter necessitates a strong foundation of medical knowledge and professional skills, emotional support, stigma mitigation, the encouragement of more honest patient self-disclosure, reduced health care costs, improved medical efficiency, and greater mental health service coverage. However, these models must address challenges related to health, safety, hallucinations, and ethics simultaneously. In the future, we should address these challenges by developing relevant usage manuals, accountability rules, and legal regulations; implementing a human-centered approach; and intelligently upgrading LMMs through the deep optimization of such models, their algorithms, and other means. This effort will thus substantially contribute not only to the maintenance of students’ health but also to the achievement of global sustainable development goals.

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.

Perforator Selection with Computed Tomography Angiography for Unilateral Breast Reconstruction: A Clinical Multicentre Analysis

Background and Objectives: Despite CTAs being critical for preoperative planning in autologous breast reconstruction, experienced plastic surgeons may have differing preferences for which side of the abdomen to use for unilateral breast reconstruction. Large language models (LLMs) have the potential to assist medical imaging interpretation. This study compares the perforator selection preferences of experienced plastic surgeons with four popular LLMs based on CTA images for breast reconstruction. Materials and Methods: Six experienced plastic surgeons from Australia, the US, Italy, Denmark, and Argentina reviewed ten CTA images, indicated their preferred side of the abdomen for unilateral breast reconstruction and recommended the type of autologous reconstruction. The LLMs were prompted to do the same. The average decisions were calculated, recorded in suitable tables, and compared. Results: The six consultants predominantly recommend the DIEP procedure (83%). This suggests experienced surgeons feel more comfortable raising DIEP than TRAM flaps, which they recommended only 3% of the time. They also favoured MS TRAM and SIEA less frequently (11% and 2%, respectively). Three LLMs-ChatGPT-4o, ChatGPT-4, and Bing CoPilot-exclusively recommended DIEP (100%), while Claude suggested DIEP 90% and MS TRAM 10%. Despite minor variations in side recommendations, consultants and AI models clearly preferred DIEP. Conclusions: Consultants and LLMs consistently preferred DIEP procedures, indicating strong confidence among experienced surgeons, though LLMs occasionally deviated in recommendations, highlighting limitations in their image interpretation capabilities. This emphasises the need for ongoing refinement of AI-assisted decision support systems to ensure they align more closely with expert clinical judgment and enhance their reliability in clinical practice.

Role of ChatGPT-4 for Medical Researchers

The robustness of artificial intelligence (AI) in medical research has gotten a lot of attention in the current era. In particular, ChatGPT-4 is emerging as a notable AI language model. Basically, ChatGPT-4 is a state-of-the-art language model that mimics human responses to natural language inputs using AI-based deep learning methods and was created by OpenAI. ChatGPT-4 allows medical researchers to instantly share and receive up-to-date information on best practices and guidelines. Moreover, practitioners can explore this technology in various fields such as recognizing patterns, predicting outcomes, and providing important insights by entering patient data, medical reports, and other text-based information. No doubt, ChatGPT-4 is in the trending era but still need to know more about this tool. Therefore, this letter highlights the contributions, benefits, and challenges of the ChatGPT-4 tool in the medical field that may help medical professionals or researchers in their future work.

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

PURPOSE OF REVIEW

ChatGPT has emerged as a potentially useful tool for healthcare. Its role in urology is in its infancy and has much potential for research, clinical practice and for patient assistance. With this narrative review, we want to draw a picture of what is known about ChatGPT's integration in urology, alongside future promises and challenges.

RECENT FINDINGS

The use of ChatGPT can ease the administrative work, helping urologists with note-taking and clinical documentation such as discharge summaries and clinical notes. It can improve patient engagement through increasing awareness and facilitating communication, as it has especially been investigated for uro-oncological diseases. Its ability to understand human emotions makes ChatGPT an empathic and thoughtful interactive tool or source for urological patients and their relatives. Currently, its role in clinical diagnosis and treatment decisions is uncertain, as concerns have been raised about misinterpretation, hallucination and out-of-date information. Moreover, a mandatory regulatory process for ChatGPT in urology is yet to be established.

SUMMARY

ChatGPT has the potential to contribute to precision medicine and tailored practice by its quick, structured responses. However, this will depend on how well information can be obtained by seeking appropriate responses and asking the pertinent questions. The key lies in being able to validate the responses, regulating the information shared and avoiding misuse of the same to protect the data and patient privacy. Its successful integration into mainstream urology needs educational bodies to provide guidelines or best practice recommendations for the same.

Implementation of Chatbot Technology in Health Care: Protocol for a Bibliometric Analysis

BACKGROUND

Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings.

OBJECTIVE

This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health.

METHODS

In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks.

RESULTS

The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024.

CONCLUSIONS

Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54349.

Popular large language model chatbots' accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries

In light of growing interest in using emerging large language models (LLMs) for self-diagnosis, we systematically assessed the performance of ChatGPT-3.5, ChatGPT-4.0, and Google Bard in delivering proficient responses to 37 common inquiries regarding ocular symptoms. Responses were masked, randomly shuffled, and then graded by three consultant-level ophthalmologists for accuracy (poor, borderline, good) and comprehensiveness. Additionally, we evaluated the self-awareness capabilities (ability to self-check and self-correct) of the LLM-Chatbots. 89.2% of ChatGPT-4.0 responses were 'good'-rated, outperforming ChatGPT-3.5 (59.5%) and Google Bard (40.5%) significantly (all p < 0.001). All three LLM-Chatbots showed optimal mean comprehensiveness scores as well (ranging from 4.6 to 4.7 out of 5). However, they exhibited subpar to moderate self-awareness capabilities. Our study underscores the potential of ChatGPT-4.0 in delivering accurate and comprehensive responses to ocular symptom inquiries. Future rigorous validation of their performance is crucial to ensure their reliability and appropriateness for actual clinical use.