A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurotransmitters using predictive modeling, closed-loop neuromodulation, and precision drug design. The fusion of AI with applications such as machine learning, deep-learning, and even computational modeling allows for the real-time tracking and enhancement of biological processes within the body. An exemplary application of AI is the use of DeepMind's AlphaFold to design new GABA reuptake inhibitors for epilepsy and anxiety. Likewise, Benevolent AI and IBM Watson have fast-tracked drug repositioning for neurodegenerative conditions. Furthermore, we identified new serotonin reuptake inhibitors for depression through AI screening. In addition, the application of Deep Brain Stimulation (DBS) settings using AI for patients with Parkinson's disease and for patients with major depressive disorder (MDD) using reinforcement learning-based transcranial magnetic stimulation (TMS) leads to better treatment. This review highlights other challenges including algorithm bias, ethical concerns, and limited clinical validation. Their proposal to incorporate AI with optogenetics, CRISPR, neuroprosthesis, and other advanced technologies fosters further exploration and refinement of precision neurotherapeutic approaches. By bridging computational neuroscience with clinical applications, AI has the potential to revolutionize neuropharmacology and improve patient-specific treatment strategies. We addressed critical challenges, such as algorithmic bias and ethical concerns, by proposing bias auditing, diverse datasets, explainable AI, and regulatory frameworks as practical solutions to ensure equitable and transparent AI applications in neurotransmitter modulation.

Evaluating a customised large language model (DELSTAR) and its ability to address medication-related questions associated with delirium: a quantitative exploratory study

BACKGROUND

A customised large language model (LLM) could serve as a next-generation clinical pharmacy research assistant to prevent medication-associated delirium. Comprehensive evaluation strategies are still missing.

AIM

This quantitative exploratory study aimed to develop an approach to comprehensively assess the domain-specific customised delirium LLM (DELSTAR) ability, quality and performance to accurately address complex clinical and practice research questions on delirium that typically require extensive literature searches and meta-analyses.

METHOD

DELSTAR, focused on delirium-associated medications, was implemented as a 'Custom GPT' for quality assessment and as a Python-based software pipeline for performance testing on closed and leading open models. Quality metrics included statement accuracy and data credibility; performance metrics covered F1-Score, sensitivity/specificity, precision, AUC, and AUC-ROC curves.

RESULTS

DELSTAR demonstrated more accurate and comprehensive information compared to information retrieved by traditional systematic literature reviews (SLRs) (p < 0.05) and accessed Application Programmer Interfaces (API), private databases, and high-quality sources despite mainly relying on less reliable internet sources. GPT-3.5 and GPT-4o emerged as the most reliable foundation models. In Dataset 2, GPT-4o (F1-Score: 0.687) and Llama3-70b (F1-Score: 0.655) performed best, while in Dataset 3, GPT-3.5 (F1-Score: 0.708) and GPT-4o (F1-Score: 0.665) led. None consistently met desired threshold values across all metrics.

CONCLUSION

DELSTAR demonstrated potential as a clinical pharmacy research assistant, surpassing traditional SLRs in quality. Improvements are needed in high-quality data use, citation, and performance optimisation. GPT-4o, GPT-3.5, and Llama3-70b were the most suitable foundation models, but fine-tuning DELSTAR is essential to enhance sensitivity, especially critical in pharmaceutical contexts.

Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease

Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI's applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns-like data privacy and algorithmic bias-must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care.

Assessing the performance of an artificial intelligence based chatbot in the differential diagnosis of oral mucosal lesions: clinical validation study

OBJECTIVES

Artificial intelligence (AI) is becoming more popular in medicine. The current study aims to investigate, primarily, if an AI-based chatbot, such as ChatGPT, could be a valid tool for assisting in establishing a differential diagnosis of oral mucosal lesions.

METHODS

Data was gathered from patients who were referred to our clinic for an oral mucosal biopsy by one oral medicine specialist. Clinical description, differential diagnoses, and final histopathologic diagnoses were retrospectively extracted from patient records. The lesion description was inputted into ChatGPT version 4.0 under a uniform script to generate three differential diagnoses. ChatGPT and an oral medicine specialist's differential diagnosis were compared to the final histopathologic diagnosis.

RESULTS

100 oral soft tissue lesions were evaluated. A statistically significant correlation was found between the ability of the Chatbot and the Specialist to accurately diagnose the cases (P < 0.001). ChatGPT demonstrated remarkable sensitivity for diagnosing urgent cases, as none of the malignant lesions were missed by the chatbot. At the same time, the specificity of the specialist was higher in cases of malignant lesion diagnosis (p < 0.05). The chatbot performance was reliable in two different events (p < 0.01).

CONCLUSION

ChatGPT-4 has shown the ability to pinpoint suspicious malignant lesions and suggest an adequate differential diagnosis for soft tissue lesions, in a consistent and repetitive manner.

CLINICAL RELEVANCE

This study serves as a primary insight into the role of AI chatbots, as assisting tools in oral medicine and assesses their clinical capabilities.

Comparing ChatGPT 3.5 and 4.0 in Low Back Pain Patient Education: Addressing Strengths, Limitations, and Psychosocial Challenges

BACKGROUND

Artificial intelligence tools like ChatGPT have gained attention for their potential to support patient education by providing accessible, evidence-based information. This study compares the performance of ChatGPT 3.5 and ChatGPT 4.0 in answering common patient questions about low back pain, focusing on response quality, readability, and adherence to clinical guidelines, while also addressing the models' limitations in managing psychosocial concerns.

METHODS

Thirty frequently asked patient questions about low back pain were categorized into 4 groups: Diagnosis, Treatment, Psychosocial Factors, and Management Approaches. Responses generated by ChatGPT 3.5 and 4.0 were evaluated on 3 key metrics: 1) response quality: rated on a scale of 1 (excellent) to 4 (unsatisfactory); 2) DISCERN criteria: evaluating reliability and adherence to clinical guidelines, with scores ranging from 1 (low reliability) to 5 (high reliability; and 3) readability: assessed using 7 readability formulas, including Flesch-Kincaid and Gunning Fog Index.

RESULTS

ChatGPT 4.0 significantly outperformed ChatGPT 3.5 in response quality across all categories, with a mean score of 1.03 compared to 2.07 for ChatGPT 3.5 (P < 0.001). ChatGPT 4.0 also demonstrated higher DISCERN scores (4.93 vs. 4.00, P < 0.001). However, both versions struggled with psychosocial factor questions, where responses were rated lower than for Diagnosis, Treatment, and Management questions (P = 0.04).

CONCLUSIONS

ChatGPT 3.5 and 4.0 limitations in addressing psychosocial concerns highlight the need for clinician oversight, particularly for emotionally sensitive issues. Enhancing artificial intelligence's capability in managing psychosocial aspects of patient care should be a priority in future iterations.

ChatGPT's Performance on Portuguese Medical Examination Questions: Comparative Analysis of ChatGPT-3.5 Turbo and ChatGPT-4o Mini

Background

Advancements in ChatGPT are transforming medical education by providing new tools for assessment and learning, potentially enhancing evaluations for doctors and improving instructional effectiveness.

Objective

This study evaluates the performance and consistency of ChatGPT-3.5 Turbo and ChatGPT-4o mini in solving European Portuguese medical examination questions (2023 National Examination for Access to Specialized Training; Prova Nacional de Acesso à Formação Especializada [PNA]) and compares their performance to human candidates.

Methods

ChatGPT-3.5 Turbo was tested on the first part of the examination (74 questions) on July 18, 2024, and ChatGPT-4o mini on the second part (74 questions) on July 19, 2024. Each model generated an answer using its natural language processing capabilities. To test consistency, each model was asked, "Are you sure?" after providing an answer. Differences between the first and second responses of each model were analyzed using the McNemar test with continuity correction. A single-parameter t test compared the models' performance to human candidates. Frequencies and percentages were used for categorical variables, and means and CIs for numerical variables. Statistical significance was set at P<.05.

Results

ChatGPT-4o mini achieved an accuracy rate of 65% (48/74) on the 2023 PNA examination, surpassing ChatGPT-3.5 Turbo. ChatGPT-4o mini outperformed medical candidates, while ChatGPT-3.5 Turbo had a more moderate performance.

Conclusions

This study highlights the advancements and potential of ChatGPT models in medical education, emphasizing the need for careful implementation with teacher oversight and further research.

Unlocking the potential: Responsibly embracing artificial intelligence to advance the use of health data and analytics at the Canadian Institute for Health Information

Canadian Institute for Health Information (CIHI) is looking to modernize and adopt new ways of working. This incudes the use of new technology, including the application of Artificial Intelligence (AI). To begin in a purposeful manner, the organization developed an AI strategy which was informed through feedback from key stakeholders and partners, from its staff and from a review of international research. The research informed several ways AI could add value to CIHI's internal operations and to the external role CIHI could play in advancing responsible AI adoption in health systems across Canada. This article describes the strategy development process and the areas of focus within the strategy.

Patients' attitudes toward artificial intelligence (AI) in cancer care: A scoping review protocol

BACKGROUND

Artificial intelligence broadly refers to computer systems that simulate intelligent behaviour with minimal human intervention. Emphasizing patient-centered care, research has explored patients' perspectives on artificial intelligence in medical care, indicating general acceptance of the technology but also concerns about supervision. However, these views have not been systematically examined from the perspective of patients with cancer, whose opinions may differ given the distinct psychosocial toll of the disease.

OBJECTIVES

This protocol describes a scoping review aimed at summarizing the existing literature on the attitudes of patients with cancer toward the use of artificial intelligence in their medical care. The primary goal is to identify knowledge gaps and highlight opportunities for future research.

METHODS

This scoping review protocol will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA-ScR). The electronic databases MEDLINE (OVID), EMBASE, PsycINFO, and CINAHL will be searched for peer-reviewed primary research articles published in academic journals. We will have two independent reviewers screen the articles retrieved from the literature search and select relevant studies based on our inclusion criteria, with a third reviewer resolving any disagreements. We will then compile the data from the included articles into a narrative summary and discuss the implications for clinical practice and future research.

DISCUSSION

To our knowledge, this will be the first scoping review to map the existing literature on the attitudes of patients with cancer regarding artificial intelligence in their medical care.

Healthcare professionals and the public sentiment analysis of ChatGPT in clinical practice

To explore the attitudes of healthcare professionals and the public on applying ChatGPT in clinical practice. The successful application of ChatGPT in clinical practice depends on technical performance and critically on the attitudes and perceptions of non-healthcare and healthcare. This study has a qualitative design based on artificial intelligence. This study was divided into five steps: data collection, data cleaning, validation of relevance, sentiment analysis, and content analysis using the K-means algorithm. This study comprised 3130 comments amounting to 1,593,650 words. The dictionary method showed positive and negative emotions such as anger, disgust, fear, sadness, surprise, good, and happy emotions. Healthcare professionals prioritized ChatGPT's efficiency but raised ethical and accountability concerns, while the public valued its accessibility and emotional support but expressed worries about privacy and misinformation. Bridging these perspectives by improving reliability, safeguarding privacy, and clearly defining ChatGPT's role is essential for its practical and ethical integration into clinical practice.

Transforming dental diagnostics with artificial intelligence: advanced integration of ChatGPT and large language models for patient care

Artificial intelligence has dramatically reshaped our interaction with digital technologies, ushering in an era where advancements in AI algorithms and Large Language Models (LLMs) have natural language processing (NLP) systems like ChatGPT. This study delves into the impact of cutting-edge LLMs, notably OpenAI's ChatGPT, on medical diagnostics, with a keen focus on the dental sector. Leveraging publicly accessible datasets, these models augment the diagnostic capabilities of medical professionals, streamline communication between patients and healthcare providers, and enhance the efficiency of clinical procedures. The advent of ChatGPT-4 is poised to make substantial inroads into dental practices, especially in the realm of oral surgery. This paper sheds light on the current landscape and explores potential future research directions in the burgeoning field of LLMs, offering valuable insights for both practitioners and developers. Furthermore, it critically assesses the broad implications and challenges within various sectors, including academia and healthcare, thus mapping out an overview of AI's role in transforming dental diagnostics for enhanced patient care.

Decoding the NCCN Guidelines With AI: A Comparative Evaluation of ChatGPT-4.0 and Llama 2 in the Management of Thyroid Carcinoma

INTRODUCTION

Artificial Intelligence (AI) has emerged as a promising tool in the delivery of health care. ChatGPT-4.0 (OpenAI, San Francisco, California) and Llama 2 (Meta, Menlo Park, CA) have each gained attention for their use in various medical applications.

OBJECTIVE

This study aims to evaluate and compare the effectiveness of ChatGPT-4.0 and Llama 2 in assisting with complex clinical decision making in the diagnosis and treatment of thyroid carcinoma.

PARTICIPANTS

We reviewed the National Comprehensive Cancer Network® (NCCN) Clinical Practice Guidelines for the management of thyroid carcinoma and formulated up to 3 complex clinical questions for each decision-making page. ChatGPT-4.0 and Llama 2 were queried in a reproducible manner. The answers were scored on a Likert scale: 5) Correct; 4) correct, with missing information requiring clarification; 3) correct, but unable to complete answer; 2) partially incorrect; 1) absolutely incorrect. Score frequencies were compared, and subgroup analysis was conducted on Correctness (defined as scores 1-2 vs 3-5) and Accuracy (scores 1-3 vs 4-5).

RESULTS

In total, 58 pages of the NCCN Guidelines® were analyzed, generating 167 unique questions. There was no statistically significant difference between ChatGPT-4.0 and Llama 2 in terms of overall score (Mann-Whitney U-test; Mean Rank = 160.53 vs 174.47, P = 0.123), Correctness (P = 0.177), or Accuracy (P = 0.891).[Formula: see text].

CONCLUSION

ChatGPT-4.0 and Llama 2 demonstrate a limited but substantial capacity to assist with complex clinical decision making relating to the management of thyroid carcinoma, with no significant difference in their effectiveness.

A Comparative Analysis of Artificial Intelligence Platforms: ChatGPT-4o and Google Gemini in Answering Questions About Birth Control Methods

Background Birth control methods (BCMs) are often underutilized or misunderstood, especially among young individuals entering their reproductive years. With the growing reliance on artificial intelligence (AI) platforms for health-related information, this study evaluates the performance of ChatGPT-4o and Google Gemini in addressing commonly asked questions about BCMs. Methods Thirty questions, derived from the American College of Obstetrics and Gynecologists (ACOG) website, were posed to both AI platforms. Questions spanned four categories: general contraception, specific contraceptive types, emergency contraception, and other topics. Responses were evaluated using a five-point rubric assessing Relevance, Completeness, and Lack of False Information (RCL). Overall scores were calculated by averaging the rubric scores. Statistical analysis, including the Wilcoxon Signed-Rank test, Friedman test, and Kruskal-Wallis test, was performed to compare metrics. Results ChatGPT-4o and Google Gemini provided high-quality responses to birth control-related queries, with overall scores averaging 4.38 ± 0.58 and 4.37 ± 0.52, respectively, both categorized as "very good" to "excellent." ChatGPT-4o demonstrated higher scores in the lack of false information, based on descriptive statistics (4.70 ± 0.60 vs. 4.47 ± 0.73), while Google Gemini outperformed in relevance, with a statistically significant difference (4.53 ± 0.57 vs. 4.30 ± 0.70, p = 0.035, large effect size). Completeness scores were comparable (p = 0.655). Statistical analyses revealed no significant differences in overall performance (p = 0.548), though Google Gemini demonstrated a potential trend of stronger performance in the "Other Topics" category. Within-model variability showed ChatGPT-4o had more pronounced differences among metrics (moderate effect size, Kendall's W = 0.357), while Google Gemini exhibited smaller variability (Kendall's W = 0.165). These findings suggest that both platforms offer reliable and complementary tools for addressing knowledge gaps in contraception, with nuanced strengths that warrant further exploration. Conclusions ChatGPT-4o and Google Gemini provided reliable and accurate responses to BCM-related queries, with slight differences in strengths. These findings underscore the potential of AI tools, in addressing public health information needs, particularly for young individuals seeking guidance on contraception. Further studies with larger datasets may elucidate nuanced differences between AI platforms.

Revolutionizing Radiology with Natural Language Processing and Chatbot Technologies: A Narrative Umbrella Review on Current Trends and Future Directions

The application of chatbots and NLP in radiology is an emerging field, currently characterized by a growing body of research. An umbrella review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This review explores the early developments and potential future impact of these technologies in radiology. The current literature, comprising 15 systematic reviews, highlights potentialities, opportunities, areas needing improvements, and recommendations. This umbrella review offers a comprehensive overview of the current landscape of natural language processing (NLP) and natural language models (NLMs), including chatbots, in healthcare. These technologies show potential for improving clinical decision-making, patient engagement, and communication across various medical fields. However, significant challenges remain, particularly the lack of standardized protocols, which raises concerns about the reliability and consistency of these tools in different clinical contexts. Without uniform guidelines, variability in outcomes may hinder the broader adoption of NLP/NLM technologies by healthcare providers. Moreover, the limited research on how these technologies intersect with medical devices (MDs) is a notable gap in the literature. Future research must address these challenges to fully realize the potential of NLP/NLM applications in healthcare. Key future research directions include the development of standardized protocols to ensure the consistent and safe deployment of NLP/NLM tools, particularly in high-stake areas like radiology. Investigating the integration of these technologies with MD workflows will be crucial to enhance clinical decision-making and patient care. Ethical concerns, such as data privacy, informed consent, and algorithmic bias, must also be explored to ensure responsible use in clinical settings. Longitudinal studies are needed to evaluate the long-term impact of these technologies on patient outcomes, while interdisciplinary collaboration between healthcare professionals, data scientists, and ethicists is essential for driving innovation in an ethically sound manner. Addressing these areas will advance the application of NLP/NLM technologies and improve patient care in this emerging field.

Revolutionizing Cytology and Cytopathology with Natural Language Processing and Chatbot Technologies: A Narrative Review on Current Trends and Future Directions

The application of chatbots and Natural Language Processing (NLP) in cytology and cytopathology is an emerging field, which is currently characterized by a limited but growing body of research. Here, a narrative review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This narrative review explores the early developments and potential future impact of these technologies in medical diagnostics. The current literature, comprising 11 studies (after excluding comments, letters, and editorials) suggests that chatbots and NLP offer significant opportunities to enhance diagnostic accuracy, streamline clinical workflows, and improve patient engagement. By automating the extraction and classification of medical information, these technologies can reduce human error and increase precision. They also promise to make patient information more accessible and facilitate complex decision-making processes, thereby fostering greater patient involvement in healthcare. Despite these promising prospects, several challenges need to be addressed for the full potential of these technologies to be realized. These include the need for data standardization, mitigation of biases in Artificial Intelligence (AI) systems, and comprehensive clinical validation. Furthermore, ethical, privacy, and legal considerations must be navigated carefully to ensure responsible AI deployment. Compared to the more established fields of histology, histopathology, and especially radiology, the integration of digital tools in cytology and cytopathology is still in its infancy. Building on the advancements in related fields, especially radiology's experience with digital integration, where these technologies already offer promising solutions in mentoring, second opinions, and education, we can leverage this knowledge to further develop chatbots and natural language processing in cytology and cytopathology. Overall, this review underscores the transformative potential of these technologies while outlining the critical areas for future research and development.

Being diagnosed with a rhabdomyosarcoma in the era of artificial intelligence: Whom can we trust?

In the era of big data, young patients may be overwhelmed by artificial intelligence-based tools, like chatbots. Five clinical experts were asked to evaluate the performance of the most currently used chatbots in providing information on a rare cancer affecting young people, like rhabdomyosarcoma. Generally speaking, despite their high performance in giving general information about the disease, these chatbots were considered by the experts to be inadequate in providing suggestions on cancer treatments and specialized centers, and also lacking in "sensitivity." Efforts are planned by the pediatric oncology community to improve the quality of data used to train these tools.

Use of Artificial Intelligence tools in supporting decision-making in hospital management

BACKGROUND

The use of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state of decision-making in hospital management, explores the potential benefits of AI integration, and examines hospital managers' perceptions of AI as a decision-support tool.

METHODS

A descriptive and exploratory study was conducted using a qualitative approach. Data were collected through semi-structured interviews with 15 hospital managers from various departments and institutions. The interviews were transcribed, anonymized, and analyzed using thematic coding to identify key themes and patterns in the responses.

RESULTS

Hospital managers highlighted the current inefficiencies in decision-making processes, often characterized by poor communication, isolated decision-making, and limited data access. The use of traditional tools like spreadsheet applications and business intelligence systems remains prevalent, but there is a clear need for more advanced, integrated solutions. Managers expressed both optimism and skepticism about AI, acknowledging its potential to improve efficiency and decision-making while raising concerns about data privacy, ethical issues, and the loss of human empathy. The study identified key challenges, including the variability in technical skills, data fragmentation, and resistance to change. Managers emphasized the importance of robust data infrastructure and adequate training to ensure successful AI integration.

CONCLUSIONS

The study reveals a complex landscape where the potential benefits of AI in hospital management are balanced with significant challenges and concerns. Effective integration of AI requires addressing technical, ethical, and cultural issues, with a focus on maintaining human elements in decision-making. AI is seen as a powerful tool to support, not replace, human judgment in hospital management, promising improvements in efficiency, data accessibility, and analytical capacity. Preparing healthcare institutions with the necessary infrastructure and providing specialized training for managers are crucial for maximizing the benefits of AI while mitigating associated risks.

Holomics and Artificial Intelligence-Driven Precision Oncology for Medullary Thyroid Carcinoma: Addressing Challenges of a Rare and Aggressive Disease

Background/Objective: Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer comprising a disproportionate share of thyroid cancer-related mortalities, despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, which are designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for patients with MTC. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, which is an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. Methods: This article reviews how AI-driven precision oncology can enhance the diagnostic workup, staging, risk stratification, management, and follow-up care of patients with MTC by processing vast amounts of complex data quickly and accurately. Articles published in English language and indexed in Pubmed were searched. Results: AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in the management of MTC enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. Conclusions: The integration of holomics and AI in the management of patients with MTC represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of treatment and care of patients with MTC. By leveraging AI and holomics, we can strive toward making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being at all ages.

Artificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome

BACKGROUND

Recently, there has been a growing interest in exploring AI-driven chatbots, such as ChatGPT, as a resource for disease management and education.

OBJECTIVE

The study aims to evaluate ChatGPT's accuracy and quality/clarity in providing nutritional management for Type 2 Diabetes (T2DM), the Metabolic syndrome (MetS) and its components, in accordance with the Academy of Nutrition and Dietetics' guidelines.

METHODS

Three nutrition management-related domains were considered: (1) Dietary management, (2) Nutrition care process (NCP) and (3) Menu planning (1500 kcal). A total of 63 prompts were used. Two experienced dietitians evaluated the chatbot output's concordance with the guidelines.

RESULTS

Both dietitians provided similar assessments for most conditions examined in the study. Gaps in the ChatGPT-derived outputs were identified and included weight loss recommendations, energy deficit, anthropometric assessment, specific nutrients of concern and the adoption of specific dietary interventions. Gaps in physical activity recommendations were also observed, highlighting ChatGPT's limitations in providing holistic lifestyle interventions. Within the NCP, the generated output provided incomplete examples of diagnostic documentation statements and had significant gaps in the monitoring and evaluation step. In the 1500 kcal one-day menus, the amounts of carbohydrates, fat, vitamin D and calcium were discordant with dietary recommendations. Regarding clarity, dietitians rated the output as either good or excellent.

CONCLUSION

Although ChatGPT is an increasingly available resource for practitioners, users are encouraged to consider the gaps identified in this study in the dietary management of T2DM and the MetS.

ChatGPT revisited: Using ChatGPT-4 for finding references and editing language in medical scientific articles

The attainment of academic superiority relies heavily upon the accessibility of scholarly resources and the expression of research findings through faultless language usage. Although modern tools, such as the Publish or Perish software program, are proficient in sourcing academic papers based on specific keywords, they often fall short of extracting comprehensive content, including crucial references. The challenge of linguistic precision remains a prominent issue, particularly for research papers composed by non-native English speakers who may encounter word usage errors. This manuscript serves a twofold purpose: firstly, it reassesses the effectiveness of ChatGPT-4 in the context of retrieving pertinent references tailored to specific research topics. Secondly, it introduces a suite of language editing services that are skilled in rectifying word usage errors, ensuring the refined presentation of research outcomes. The article also provides practical guidelines for formulating precise queries to mitigate the risks of erroneous language usage and the inclusion of spurious references. In the ever-evolving realm of academic discourse, leveraging the potential of advanced AI, such as ChatGPT-4, can significantly enhance the quality and impact of scientific publications.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

Background

With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption.

Objectives

This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings.

Methods

We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail.

Results

Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare.

Conclusions

The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Effectiveness of ChatGPT 4.0 in Telemedicine-Based Management of Metastatic Prostate Carcinoma

The recent rise in telemedicine, notably during the COVID-19 pandemic, highlights the potential of integrating artificial intelligence tools in healthcare. This study assessed the effectiveness of ChatGPT versus medical oncologists in the telemedicine-based management of metastatic prostate cancer. In this retrospective study, 102 patients who met inclusion criteria were analyzed to compare the competencies of ChatGPT and oncologists in telemedicine consultations. ChatGPT's role in pre-charting and determining the need for in-person consultations was evaluated. The primary outcome was the concordance between ChatGPT and oncologists in treatment decisions. Results showed a moderate concordance (Cohen's Kappa = 0.43, p < 0.001). The number of diagnoses made by both parties was not significantly different (median number of diagnoses: 5 vs. 5, p = 0.12). In conclusion, ChatGPT exhibited moderate agreement with oncologists in management via telemedicine, indicating the need for further research to explore its healthcare applications.

Large Language Model Prompting Techniques for Advancement in Clinical Medicine

Large Language Models (LLMs have the potential to revolutionize clinical medicine by enhancing healthcare access, diagnosis, surgical planning, and education. However, their utilization requires careful, prompt engineering to mitigate challenges like hallucinations and biases. Proper utilization of LLMs involves understanding foundational concepts such as tokenization, embeddings, and attention mechanisms, alongside strategic prompting techniques to ensure accurate outputs. For innovative healthcare solutions, it is essential to maintain ongoing collaboration between AI technology and medical professionals. Ethical considerations, including data security and bias mitigation, are critical to their application. By leveraging LLMs as supplementary resources in research and education, we can enhance learning and support knowledge-based inquiries, ultimately advancing the quality and accessibility of medical care. Continued research and development are necessary to fully realize the potential of LLMs in transforming healthcare.

PyCaret for Predicting Type 2 Diabetes: A Phenotype- and Gender-Based Approach with the "Nurses' Health Study" and the "Health Professionals' Follow-Up Study" Datasets

Predicting type 2 diabetes mellitus (T2DM) by using phenotypic data with machine learning (ML) techniques has received significant attention in recent years. PyCaret, a low-code automated ML tool that enables the simultaneous application of 16 different algorithms, was used to predict T2DM by using phenotypic variables from the "Nurses' Health Study" and "Health Professionals' Follow-up Study" datasets. Ridge Classifier, Linear Discriminant Analysis, and Logistic Regression (LR) were the best-performing models for the male-only data subset. For the female-only data subset, LR, Gradient Boosting Classifier, and CatBoost Classifier were the strongest models. The AUC, accuracy, and precision were approximately 0.77, 0.70, and 0.70 for males and 0.79, 0.70, and 0.71 for females, respectively. The feature importance plot showed that family history of diabetes (famdb), never having smoked, and high blood pressure (hbp) were the most influential features in females, while famdb, hbp, and currently being a smoker were the major variables in males. In conclusion, PyCaret was used successfully for the prediction of T2DM by simplifying complex ML tasks. Gender differences are important to consider for T2DM prediction. Despite this comprehensive ML tool, phenotypic variables alone may not be sufficient for early T2DM prediction; genotypic variables could also be used in combination for future studies.

Decoding children dental health risks: a machine learning approach to identifying key influencing factors

Introduction and objectives

This study investigates key factors influencing dental caries risk in children aged 7 and under using machine learning techniques. By addressing dental caries' prevalence, it aims to enhance early identification and preventative strategies for high-risk individuals.

Methods

Data from clinical examinations of 356 children were analyzed using Logistic Regression, Decision Trees, and Random Forests models. These models assessed the influence of dietary habits, fluoride exposure, and socio-economic status on caries risk, emphasizing accuracy, precision, recall, F1 score, and AUC metrics.

Results

Poor oral hygiene, high sugary diet, and low fluoride exposure were identified as significant caries risk factors. The Random Forest model demonstrated superior performance, illustrating the potential of machine learning in complex health data analysis. Our SHAP analysis identified poor oral hygiene, high sugary diet, and low fluoride exposure as significant caries risk factors.

Conclusion

Machine learning effectively identifies and quantifies dental caries risk factors in children. This approach supports targeted interventions and preventive measures, improving pediatric dental health outcomes.

Clinical significance

By leveraging machine learning to pinpoint crucial caries risk factors, this research lays the groundwork for data-driven preventive strategies, potentially reducing caries prevalence and promoting better dental health in children.

Embracing the future: Integrating ChatGPT into China's nursing education system

This article delves into the role of ChatGPT within the rapidly evolving field of artificial intelligence, especially highlighting its significant potential in nursing education. Initially, the paper presents the notable advancements ChatGPT has achieved in facilitating interactive learning and providing real-time feedback, along with the academic community's growing interest in this technology. Subsequently, summarizing the research outcomes of ChatGPT's applications in nursing education, including various clinical disciplines and scenarios, showcases the enormous potential for multidisciplinary education and addressing clinical issues. Comparing the performance of several Large Language Models (LLMs) on China's National Nursing Licensure Examination, we observed that ChatGPT demonstrated a higher accuracy rate than its counterparts, providing a solid theoretical foundation for its application in Chinese nursing education and clinical settings. Educational institutions should establish a targeted and effective regulatory framework to leverage ChatGPT in localized nursing education while assuming corresponding responsibilities. Through standardized training for users and adjustments to existing educational assessment methods aimed at preventing potential misuse and abuse, the full potential of ChatGPT as an innovative auxiliary tool in China's nursing education system can be realized, aligning with the developmental needs of modern teaching methodologies.

Analysis of approved dental teaching projects in the teaching practice research program in 8 dental schools of Taiwan from 2018 to 2023

Background/purpose

The teaching practice research program was initiated by Taiwan's Ministry of Education in 2018 to improve medical teaching quality. This study analyzed dental teaching projects conducted under this program from 2018 to 2023.

Materials and methods

Data of submitted and approved medical (including dental) teaching projects from 2018 to 2023 were obtained from the annual reports released by the program committee. The annual passing rates were calculated by dividing the number of approved dental teaching projects by the total number of approved medical teaching projects in the category of medical and healthcare sciences in a particular year. The 24 approved dental teaching projects were reviewed, classified into different topics in the dental field, and then reported.

Results

There were 24 approved dental teaching projects out of a total of 822 approved medical teaching projects from 2018 to 2023. The annual passing rates increased gradually from 2018 (1.4 %) to 2022 (3.9 %) and 2023 (3.8 %) with an overall mean passing rate of 2.9 % over a period of 6 years. Of the 24 approved dental teaching projects, digital dentistry was the most common teaching research topic (9 projects), followed by new teaching models (7 projects), 3D technology (3 projects), endodontics (3 projects), dental histology (one project), and evidence-based method (one project).

Conclusion

Digital dentistry and new teaching models were the two predominant dental teaching research topics, suggesting that both are the modern trends in the dental education. However, the dental teaching research projects are still very limited in 8 Taiwanese dental schools.

"I Wonder if my Years of Training and Expertise Will be Devalued by Machines": Concerns About the Replacement of Medical Professionals by Artificial Intelligence

Background

The rapid integration of artificial intelligence (AI) into healthcare has raised concerns among healthcare professionals about the potential displacement of human medical professionals by AI technologies. However, the apprehensions and perspectives of healthcare workers regarding the potential substitution of them with AI are unknown.

Objective

This qualitative research aimed to investigate healthcare workers' concerns about artificial intelligence replacing medical professionals.

Methods

A descriptive and exploratory research design was employed, drawing upon the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory, and Sociotechnical Systems Theory as theoretical frameworks. Participants were purposively sampled from various healthcare settings, representing a diverse range of roles and backgrounds. Data were collected through individual interviews and focus group discussions, followed by thematic analysis.

Results

The analysis revealed seven key themes reflecting healthcare workers' concerns, including job security and economic concerns; trust and acceptance of AI; ethical and moral dilemmas; quality of patient care; workforce role redefinition and training; patient-provider relationships; healthcare policy and regulation.

Conclusions

This research underscores the multifaceted concerns of healthcare workers regarding the increasing role of AI in healthcare. Addressing job security, fostering trust, addressing ethical dilemmas, and redefining workforce roles are crucial factors to consider in the successful integration of AI into healthcare. Healthcare policy and regulation must be developed to guide this transformation while maintaining the quality of patient care and preserving patient-provider relationships. The study findings offer insights for policymakers and healthcare institutions to navigate the evolving landscape of AI in healthcare while addressing the concerns of healthcare professionals.