The role of artificial intelligence in healthcare: a structured literature review

Role of artificial intelligence in colorectal cancer

The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.

Artificial intelligence in multiple sclerosis management: Challenges in a new era

Multiple sclerosis poses diagnostic and therapeutic challenges for healthcare professionals, with a high risk of misdiagnosis and difficulties in assessing therapeutic effectiveness. Artificial intelligence, particularly machine learning and deep neural networks, emerges as a promising tool to address these challenges. These technologies have the capability to analyze a wide range of data, from magnetic resonance imaging to genetic information, to provide more accurate diagnoses, classify multiple sclerosis subtypes, and predict disease progression and treatment response with extraordinary precision. However, their implementation raises ethical dilemmas, such as accountability in case of errors and the risk of excessive reliance on healthcare personnel. That said, this manuscript aims to urge healthcare professionals dedicated to the care and research of multiple sclerosis patients to recognize artificial intelligence as a valuable and complementary resource in their clinical practice. It also seeks to emphasize the importance of integrating this type of technology safely and responsibly, thereby ensuring the ethics and welfare of patients.

Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks

BACKGROUND OF STUDY

Over the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan.

MATERIAL AND METHODS

A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression.

RESULTS

Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187).

CONCLUSION

Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.

Revolutionizing elderly care: Building a healthier aging society through innovative long-term care systems and assessing the long-term care acceptance model

AIM

With a growing elderly population, the demand for caregivers is increasing in Khon Kaen, Thailand, with approximately 17 000 elderly residents. This growing number of older people and a shortage of caregivers could overload the healthcare system.

METHODS

The present study involved 129 healthcare volunteers (caregivers for questionnaires study) and the collection of health data from 290 elderly residents from northeastern Thailand. After training, the volunteers assessed its usefulness through questionnaires. Tool reliability and statistical hypotheses were tested using stratified regression analysis (hierarchical regression) and multiple regression.

RESULTS

The relative mean scores of perceived usefulness, perceived ease of use, attitude toward usage and behavioral intention to use technology were 4.51, 4.29, 4.44 and 4.41, respectively. In addition, perceived usefulness and user attitudes positively affected volunteers' willingness to use the system.

CONCLUSION

The study was developed from the awareness of enhancing community quality and ecosystem through a long-term care system application. Analyzing external factors can enhance technology's future effectiveness. Geriatr Gerontol Int 2024; 24: 477-485.

Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review

Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.

Leveraging Artificial Intelligence for Expediting Implementation Efforts

Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice.

Brave new world: expanding home care in stem cell transplantation and advanced therapies with new technologies

Hematopoietic stem cell transplantation and cell therapies like CAR-T are costly, complex therapeutic procedures. Outpatient models, including at-home transplantation, have been developed, resulting in similar survival results, reduced costs, and increased patient satisfaction. The complexity and safety of the process can be addressed with various emerging technologies (artificial intelligence, wearable sensors, point-of-care analytical devices, drones, virtual assistants) that allow continuous patient monitoring and improved decision-making processes. Patients, caregivers, and staff can also benefit from improved training with simulation or virtual reality. However, many technical, operational, and above all, ethical concerns need to be addressed. Finally, outpatient or at-home hematopoietic transplantation or CAR-T therapy creates a different, integrated operative system that must be planned, designed, and carefully adapted to the patient's characteristics and distance from the hospital. Patients, clinicians, and their clinical environments can benefit from technically improved at-home transplantation.

Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis

PURPOSE

Fracture detection is one of the most commonly used and studied aspects of artificial intelligence (AI) in medicine. In this systematic review and meta-analysis, we aimed to summarize available literature and data regarding AI performance in fracture detection on plain radiographs and various factors affecting it.

METHODS

We systematically reviewed studies evaluating AI algorithms in detecting bone fractures in plain radiographs, combined their performance using meta-analysis (a bivariate regression approach), and compared it with that of clinicians. We also analyzed the factors potentially affecting algorithm performance using meta-regression.

RESULTS

Our analysis included 100 studies. In 83 studies with confusion matrices, AI algorithms showed a sensitivity of 91.43% and a specificity of 92.12% (Area under the summary receiver operator curve = 0.968). After adjustment and false discovery rate correction, tibia/fibula (excluding ankle) fractures were associated with higher (7.0%, p=0.004) AI sensitivity, while more recent publications (5.5%, p=0.003) and Xception architecture (6.6%, p<0.001) were associated with higher specificity. Clinicians and AI showed similar specificity in fracture identification, although AI leaned to higher sensitivity (7.6%, p=0.07). Radiologists, on the other hand, were more specific than AI overall and in several subgroups, and more sensitive to hip fractures before FDR correction.

CONCLUSIONS

Currently available AI aids could result in a significant improvement in care where radiologists are not readily available. Moreover, identifying factors affecting algorithm performance could guide AI development teams in their process of optimizing their products.

Artificial Intelligence Assistance for the Measurement of Full Alignment Parameters in Whole-Spine Lateral Radiographs

BACKGROUND

Measuring spinal alignment with radiological parameters is essential in patients with spinal conditions likely to be treated surgically. These evaluations are not usually included in the radiological report. As a result, spinal surgeons commonly perform the measurement, which is time-consuming and subject to errors. We aim to develop a fully automated artificial intelligence (AI) tool to assist in measuring alignment parameters in whole-spine lateral radiograph (WSL X-rays).

METHODS

We developed a tool called Vertebrai that automatically calculates the global spinal parameters (GSPs): Pelvic incidence, sacral slope, pelvic tilt, L1-L4 angle, L4-S1 lumbo-pelvic angle, T1 pelvic angle, sagittal vertical axis, cervical lordosis, C1-C2 lordosis, lumbar lordosis, mid-thoracic kyphosis, proximal thoracic kyphosis, global thoracic kyphosis, T1 slope, C2-C7 plummet, spino-sacral angle, C7 tilt, global tilt, spinopelvic tilt, and hip odontoid axis. We assessed human-AI interaction instead of AI performance alone. We compared the time to measure GSP and inter-rater agreement with and without AI assistance. Two institutional datasets were created with 2267 multilabel images for classification and 784 WSL X-rays with reference standard landmark labeled by spinal surgeons.

RESULTS

Vertebrai significantly reduced the measurement time comparing spine surgeons with AI assistance and the AI algorithm alone, without human intervention (3 minutes vs. 0.26 minutes; P < 0.05). Vertebrai achieved an average accuracy of 83% in detecting abnormal alignment values, with the sacral slope parameter exhibiting the lowest accuracy at 61.5% and spinopelvic tilt demonstrating the highest accuracy at 100%. Intraclass correlation analysis revealed a high level of correlation and consistency in the global alignment parameters.

CONCLUSIONS

Vertebrai's measurements can accurately detect alignment parameters, making it a promising tool for measuring GSP automatically.

Capturing artificial intelligence applications' value proposition in healthcare - a qualitative research study

Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications' potential.We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.

Quantification of vesicoureteral reflux using machine learning

INTRODUCTION

The radiographic grading of voiding cystourethrogram (VCUG) images is often used to determine the clinical course and appropriate treatment in patients with vesicoureteral reflux (VUR). However, image-based evaluation of VUR remains highly subjective, so we developed a supervised machine learning model to automatically and objectively grade VCUG data.

STUDY DESIGN

A total of 113 VCUG images were gathered from public sources to compile the dataset for this study. For each image, VUR severity was graded by four pediatric radiologists and three pediatric urologists (low severity scored 1-3; high severity 4-5). Ground truth for each image was assigned based on the grade diagnosed by a majority of the expert assessors. Nine features were extracted from each VCUG image, then six machine learning models were trained, validated, and tested using 'leave-one-out' cross-validation. All features were compared and contrasted, with the highest-ranked then being used to train the final models.

RESULTS

F1-score is a metric that is often used to indicate performance accuracy of machine learning models. When using the highest-ranked VCUG image features, F1-scores for the support vector machine (SVM) and multi-layer perceptron (MLP) classifiers were 90.27 % and 91.14 %, respectively, indicating a high level of accuracy. When using all features combined, F1 scores were 89.37 % for SVM and 90.27 % for MLP.

DISCUSSION

These findings indicate that a distorted pattern of renal calyces is an accurate predictor of high-grade VUR. Machine learning protocols can be enhanced in future to improve objective grading of VUR.

Intelligent decision support systems for dementia care: A scoping review

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.

The Role of Artificial Intelligence in Surgery: What do General Surgery Residents Think?

BACKGROUND

Artificial intelligence (AI) holds significant potential in medical education and patient care, but its rapid emergence presents ethical and practical challenges. This study explored the perspectives of surgical residents on AI's role in medicine.

METHODS

We performed a cross-sectional study surveying general surgery residents at a university-affiliated teaching hospital about their views on AI in medicine and surgical training. The survey covered demographics, residents' understanding of AI, its integration into medical practice, and use of AI tools like ChatGPT. The survey design was inspired by a recent national survey and underwent pretesting before deployment.

RESULTS

Of the 31 participants surveyed, 24% identified diagnostics as AI's top application, 12% favored its use in identifying anatomical structures in surgeries, and 20% endorsed AI integration into EMRs for predictive models. Attitudes toward AI varied based on its intended application: 77.41% expressed concern about AI making life decisions and 70.97% felt excited about its application for repetitive tasks. A significant 67.74% believed AI could enhance the understanding of medical knowledge. Perception of AI integration varied with AI familiarity (P = .01), with more knowledgeable respondents expressing more positivity. Moreover, familiarity influenced the perceived academic use of ChatGPT (P = .039) and attitudes toward AI in operating rooms (P = .032). Conclusion: This study provides insights into surgery residents' perceptions of AI in medical practice and training. These findings can inform future research, shape policy decisions, and guide AI development, promoting a harmonious collaboration between AI and surgeons to improve both training and patient care.

Informatics Competencies of Students in a Doctor of Nursing Practice Program: A Descriptive Study

OBJECTIVES

Health systems that apply artificial intelligence (AI) are transforming the roles of healthcare providers, including those of Doctor of Nursing Practice (DNP) providers. These professionals are required to utilize informatics knowledge and skills to deliver quality care, necessitating a high level of informatics competencies, which should be developed through well-structured courses. The purpose of this study is to assess the informatics competency scale scores of DNP students and to provide recommendations for enhancing the informatics curriculum.

METHODS

An online informatics course was offered to students enrolled in a Bachelor of Science in Nursing to DNP program, and their informatics competency, which includes three subscales, was evaluated. Online survey data were collected from Fall 2021 to Fall 2022 using the "Self-Assessment of Informatics Competency Scale for Health Professionals."

RESULTS

An analysis of 127 student responses revealed that students demonstrated competence in overall informatics competency and in one subscale: "applied computer skills (clinical informatics)." They showed proficiency in the "basic computer skills" and the "role" subscales. However, they reported lower competency in managing data and integrating standard terminology into their practice.

CONCLUSIONS

The findings offer detailed insights into the current informatics competencies of DNP students and can inform informatics educators on how to enhance their courses. As healthcare institutions increasingly depend on AI applications, it is imperative for informatics educators to include AI-related content in their curricula.

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants' familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.

New evidence on the real role of digital economy in influencing public health efficiency

In recent years, the rapid advancement of digital technology has supported the growth of the digital economy. The transformation towards digitization in the public health sector serves as a key indicator of this economic shift. Understanding how the digital economy continuously improves the efficiency of public health services and its various pathways of influence has become increasingly important. It is essential to clarify the impact mechanism of the digital economy on public health services to optimize health expenditures and advance digital economic construction. This study investigates the impact of digital economic development on the efficiency of public health services from a novel perspective, considering social media usage and urban-rural healthcare disparities while constructing a comprehensive index of digital economic development. The findings indicate that the digital economy reduces the efficiency of public health services primarily through two transmission mechanisms: the promotion of social media usage and the widening urban-rural healthcare gap. Moreover, these impacts and transmission pathways exhibit spatial heterogeneity. This study unveils the intrinsic connection and mechanisms of interaction between digital economic development and the efficiency of public health services, providing a theoretical basis and reference for government policy formulation. However, it also prompts further considerations on achieving synergy and interaction between the digital economy and public health services.

The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey

Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.

Designing Chinese hospital emergency departments to leverage artificial intelligence-a systematic literature review on the challenges and opportunities

Artificial intelligence (AI) has witnessed rapid advances in the healthcare domain in recent years, especially in the emergency field, where AI is likely to radically reshape medical service delivery. Although AI has substantial potential to enhance diagnostic accuracy and operational efficiency in hospitals, research on its applications in Emergency Department building design remains relatively scarce. Therefore, this study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI. Two systematic literature reviews are combined, one in AI and the other in sensors, to explore their potential application to support decision-making, resource optimisation and patient monitoring. These reviews have then informed a discussion on integrating AI sensors in contemporary Emergency Department designs for use in China to support the evidence base on resuscitation units, emergency operating rooms and Emergency Department Intensive Care Unit (ED-ICU) design. We hope to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience.

A Knowledge-based Decision Support System for recommending safe recipes to individuals with dysphagia

BACKGROUND

Dysphagia is a disorder that can be associated to several pathological conditions, including neuromuscular diseases, with significant impact on quality of life. Dysphagia often leads to malnutrition, as a consequence of the dietary changes made by patients or their caregivers, who may deliberately decide to reduce or avoid specific food consistencies (because they are not perceived as safe), and the lack of knowledge in how to process foods are critics. Such dietary changes often result in unbalanced nutrients intake, which can have significant consequences for frail patients. This paper presents the development of a prototypical novel ontology-based Decision Support System (DSS) to support neuromuscular patients with dysphagia (following a per-oral nutrition) and their caregivers in preparing nutritionally balanced and safe meals.

METHOD

After reviewing scientific literature, we developed in collaboration with Ear-Nose-Throat (ENT) specialists, neurologists, and dieticians the DSS formalizes expert knowledge to suggest recipes that are considered safe according to patient's consistency limitations and dysphagia severity and also nutritionally well-balanced.

RESULTS

The prototype can be accessed via digital applications both by physicians to generate and verify the recommendations, and by the patients and their caregivers to follow the step-by-step procedures to autonomously prepare and process one or more recipe. The system is evaluated with 9 clinicians to assess the quality of the DSS's suggested recipes and its acceptance in clinical practice.

CONCLUSIONS

Preliminary results suggest a global positive outcome for the recipes inferred by the DSS and a good usability of the system.

Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders

Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.

Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study

Background

The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial.

Aims

This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare.

Methods

A cohort of 23 nursing professionals was recruited between April 7, 2023, and May 4, 2023, for this study. Employing a thematic analysis approach, qualitative data from interviews with nursing professionals were analyzed. Verbatim transcripts underwent rigorous coding, and these codes were organized into themes through constant comparative analysis. The themes were refined and developed through the grouping of related codes, ensuring an authentic representation of participants' viewpoints.

Results

After careful data analysis, ten key themes emerged including: (I) Perceptions of AI readiness; (II) Benefits and concerns; (III) Enhanced patient outcomes; (IV) Collaboration and workflow; (V) Human-tech balance: (VI) Training and skill development; (VII) Ethical and legal considerations; (VIII) AI implementation barriers; (IX) Patient-nurse relationships; (X) Future vision and adaptation.

Conclusion

This study provides valuable insights into nursing professionals' perspectives on the integration of AI in future nursing care. It highlights their enthusiasm for AI's potential benefits while emphasizing the importance of ethical and compassionate nursing practice. The findings underscore the need for comprehensive training programs to equip nursing professionals with the skills necessary for successful AI integration. Ultimately, this research contributes to the ongoing discourse on the role of AI in nursing, paving the way for a future where innovative technologies complement and enhance the delivery of patient-centered care.

Ethical and regulatory challenges of AI technologies in healthcare: A narrative review

Over the past decade, there has been a notable surge in AI-driven research, specifically geared toward enhancing crucial clinical processes and outcomes. The potential of AI-powered decision support systems to streamline clinical workflows, assist in diagnostics, and enable personalized treatment is increasingly evident. Nevertheless, the introduction of these cutting-edge solutions poses substantial challenges in clinical and care environments, necessitating a thorough exploration of ethical, legal, and regulatory considerations. A robust governance framework is imperative to foster the acceptance and successful implementation of AI in healthcare. This article delves deep into the critical ethical and regulatory concerns entangled with the deployment of AI systems in clinical practice. It not only provides a comprehensive overview of the role of AI technologies but also offers an insightful perspective on the ethical and regulatory challenges, making a pioneering contribution to the field. This research aims to address the current challenges in digital healthcare by presenting valuable recommendations for all stakeholders eager to advance the development and implementation of innovative AI systems.

Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks

The study primarily aimed to understand whether individual factors could predict how people perceive and evaluate artworks that are perceived to be produced by AI. Additionally, the study attempted to investigate and confirm the existence of a negative bias toward AI-generated artworks and to reveal possible individual factors predicting such negative bias. A total of 201 participants completed a survey, rating images on liking, perceived positive emotion, and believed human or AI origin. The findings of the study showed that some individual characteristics as creative personal identity and openness to experience personality influence how people perceive the presented artworks in function of their believed source. Participants were unable to consistently distinguish between human and AI-created images. Furthermore, despite generally preferring the AI-generated artworks over human-made ones, the participants displayed a negative bias against AI-generated artworks when subjective perception of source attribution was considered, thus rating as less preferable the artworks perceived more as AI-generated, independently on their true source. Our findings hold potential value for comprehending the acceptability of products generated by AI technology.

Women's perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study

BACKGROUND

Understanding women's perspectives can help to create an effective and acceptable artificial intelligence (AI) implementation for triaging mammograms, ensuring a high proportion of screening-detected cancer. This study aimed to explore Swedish women's perceptions and attitudes towards the use of AI in mammography.

METHOD

Semistructured interviews were conducted with 16 women recruited in the spring of 2023 at Capio S:t Görans Hospital, Sweden, during an ongoing clinical trial of AI in screening (ScreenTrustCAD, NCT04778670) with Philips equipment. The interview transcripts were analysed using inductive thematic content analysis.

RESULTS

In general, women viewed AI as an excellent complementary tool to help radiologists in their decision-making, rather than a complete replacement of their expertise. To trust the AI, the women requested a thorough evaluation, transparency about AI usage in healthcare, and the involvement of a radiologist in the assessment. They would rather be more worried because of being called in more often for scans than risk having overlooked a sign of cancer. They expressed substantial trust in the healthcare system if the implementation of AI was to become a standard practice.

CONCLUSION

The findings suggest that the interviewed women, in general, hold a positive attitude towards the implementation of AI in mammography; nonetheless, they expect and demand more from an AI than a radiologist. Effective communication regarding the role and limitations of AI is crucial to ensure that patients understand the purpose and potential outcomes of AI-assisted healthcare.

Assessing the role of GPT-4 in thyroid ultrasound diagnosis and treatment recommendations: enhancing interpretability with a chain of thought approach

Background

As artificial intelligence (AI) becomes increasingly prevalent in the medical field, the effectiveness of AI-generated medical reports in disease diagnosis remains to be evaluated. ChatGPT is a large language model developed by open AI with a notable capacity for text abstraction and comprehension. This study aimed to explore the capabilities, limitations, and potential of Generative Pre-trained Transformer (GPT)-4 in analyzing thyroid cancer ultrasound reports, providing diagnoses, and recommending treatment plans.

Methods

Using 109 diverse thyroid cancer cases, we evaluated GPT-4's performance by comparing its generated reports to those from doctors with various levels of experience. We also conducted a Turing Test and a consistency analysis. To enhance the interpretability of the model, we applied the Chain of Thought (CoT) method to deconstruct the decision-making chain of the GPT model.

Results

GPT-4 demonstrated proficiency in report structuring, professional terminology, and clarity of expression, but showed limitations in diagnostic accuracy. In addition, our consistency analysis highlighted certain discrepancies in the AI's performance. The CoT method effectively enhanced the interpretability of the AI's decision-making process.

Conclusions

GPT-4 exhibits potential as a supplementary tool in healthcare, especially for generating thyroid gland diagnostic reports. Our proposed online platform, "ThyroAIGuide", alongside the CoT method, underscores the potential of AI to augment diagnostic processes, elevate healthcare accessibility, and advance patient education. However, the journey towards fully integrating AI into healthcare is ongoing, requiring continuous research, development, and careful monitoring by medical professionals to ensure patient safety and quality of care.

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

Introduction

Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.

Methods

Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models-random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor-over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable's importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.

Results

With the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.

Discussion

Our models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.

The Use of ChatGPT for Education Modules on Integrated Pharmacotherapy of Infectious Disease: Educators' Perspectives

BACKGROUND

Artificial Intelligence (AI) plays an important role in many fields, including medical education, practice, and research. Many medical educators started using ChatGPT at the end of 2022 for many purposes.

OBJECTIVE

The aim of this study was to explore the potential uses, benefits, and risks of using ChatGPT in education modules on integrated pharmacotherapy of infectious disease.

METHODS

A content analysis was conducted to investigate the applications of ChatGPT in education modules on integrated pharmacotherapy of infectious disease. Questions pertaining to curriculum development, syllabus design, lecture note preparation, and examination construction were posed during data collection. Three experienced professors rated the appropriateness and precision of the answers provided by ChatGPT. The consensus rating was considered. The professors also discussed the prospective applications, benefits, and risks of ChatGPT in this educational setting.

RESULTS

ChatGPT demonstrated the ability to contribute to various aspects of curriculum design, with ratings ranging from 50% to 92% for appropriateness and accuracy. However, there were limitations and risks associated with its use, including incomplete syllabi, the absence of essential learning objectives, and the inability to design valid questionnaires and qualitative studies. It was suggested that educators use ChatGPT as a resource rather than relying primarily on its output. There are recommendations for effectively incorporating ChatGPT into the curriculum of the education modules on integrated pharmacotherapy of infectious disease.

CONCLUSIONS

Medical and health sciences educators can use ChatGPT as a guide in many aspects related to the development of the curriculum of the education modules on integrated pharmacotherapy of infectious disease, syllabus design, lecture notes preparation, and examination preparation with caution.

Harnessing the benefits of ChatGPT for radiography education: A discussion paper

OBJECTIVE

Radiography education is pivotal in training skilled radiographers for diagnostic imaging and therapeutic applications. With technological advancements, interest in innovative educational tools to enhance traditional teaching methods is growing. This discussion paper explores the possibility of the integration of ChatGPT, a cutting-edge conversational AI language model, into radiography education.

KEY FINDINGS

We report that ChatGPT offers interactive learning opportunities that can facilitate learning. It also provides self-paced learning, revision platforms, and supports educators in scenario creation, assessment development, group collaboration, and professional and research activities. Despite these benefits, it is important to carefully consider issues related to academic integrity and privacy, along with the opportunities and challenges presented by this new technology in radiography education.

CONCLUSION

This paper highlights some of the prospects and limitations of the potential applications of ChatGPT in radiography education, underscoring the benefits for both students and educators. However, its implementation must be considered thoughtfully and ethically, taking into account its strengths and limitations.

IMPLICATIONS FOR PRACTICE

Integrating ChatGPT in radiography education has the potential to improve radiography education by improving digital literacy and graduate outcomes of students while streamlining the preparation process for educators. However, ethical implementation is vital for optimal outcomes.

Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students

Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.

Defining dignity in higher education as an alternative to requiring 'Trigger Warnings'

This article examines trigger warnings, particularly the call for trigger warnings on university campuses, and from a Levinasian and Kantian ethical perspective, and addresses the question: When, if ever, are trigger warnings helpful to student's learning? The nursing curriculum is developed with key stakeholders and regulatory bodies to ensure graduate nurses are competent to deliver a high standard of care to patients and clients. Practical teaching practice and published research has uncovered an increasing use of 'Trigger Warnings' before a topic is discussed, or used as warnings on core module texts. It is appreciated that some students have personal experience of psychological or physical trauma. However, apart from identifying these students through Mitigating Circumstances committees, or when the student feels confident to share this information with a personal tutor, this information remains strictly confidential. There is the potential for covert skills such as critical analysis and skilful discussion not being attained by the student. With the assistance of Kants moral theory, an argument will develop that the insidious use of Trigger warnings and the embargo of recommended reading, requires critical discussion with the public. This would involve the rationale and pedagogical justification for the use of texts, and the necessity within nursing education to address challenging clinical topics. To support students with PTSD this may involve the research discussed on personal educational needs analysis.

Artificial intelligence in cardiovascular medicine: An updated review of the literature

Screening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.

3D Printing of Dietary Products for the Management of Inborn Errors of Intermediary Metabolism in Pediatric Populations

The incidence of Inborn Error of Intermediary Metabolism (IEiM) diseases may be low, yet collectively, they impact approximately 6-10% of the global population, primarily affecting children. Precise treatment doses and strict adherence to prescribed diet and pharmacological treatment regimens are imperative to avert metabolic disturbances in patients. However, the existing dietary and pharmacological products suffer from poor palatability, posing challenges to patient adherence. Furthermore, frequent dose adjustments contingent on age and drug blood levels further complicate treatment. Semi-solid extrusion (SSE) 3D printing technology is currently under assessment as a pioneering method for crafting customized chewable dosage forms, surmounting the primary limitations prevalent in present therapies. This method offers a spectrum of advantages, including the flexibility to tailor patient-specific doses, excipients, and organoleptic properties. These elements are pivotal in ensuring the treatment's efficacy, safety, and adherence. This comprehensive review presents the current landscape of available dietary products, diagnostic methods, therapeutic monitoring, and the latest advancements in SSE technology. It highlights the rationale underpinning their adoption while addressing regulatory aspects imperative for their seamless integration into clinical practice.

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

BACKGROUND

Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners.

OBJECTIVE

In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research.

METHODS

Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy.

RESULTS

From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine.

CONCLUSIONS

The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam

The application of new technologies in medical education still lags behind the extraordinary advances of AI. This study examined the understanding, attitudes, and perspectives of Vietnamese medical students toward AI and its consequences, as well as their knowledge of existing AI operations in Vietnam. A cross-sectional online survey was administered to 1142 students enrolled in undergraduate medicine and pharmacy programs. Most of the participants had no understanding of AI in healthcare (1053 or 92.2 %). The majority believed that AI would benefit their careers (890 or 77.9 %) and that such innovation will be used to oversee public health and epidemic prevention on their behalf (882 or 77.2 %). The proportion of students with satisfactory knowledge significantly differed depending on gender (P < 0.001), major (P = 0.003), experience (P < 0.001), and income (P = 0.011). The percentage of respondents with positive attitudes significantly differed by year level (P = 0.008) and income (P = 0.003), and the proportion with favorable perspectives regarding AI varied considerably by age (P = 0.046) and major (P < 0.001). Most of the participants wanted to integrate AI into radiology and digital imaging training (P = 0.283), while the fifth-year students wished to learn about AI in medical genetics and genomics (P < 0.001, 4.0 ± 0.8). The male students had 1.898 times more adequate knowledge of AI than their female counterparts, and those who had attended webinars/lectures/courses on AI in healthcare had 4.864 times more adequate knowledge than those having no such experiences. The majority believed that the barrier to implementing AI in healthcare is the lack of financial resources (83.54 %) and appropriate training (81.00 %). Participants saw AI as a "partner" rather than a "competitor", but the majority of low knowledge was recorded. Future research should take into account the way to integrate AI into medical training programs for healthcare students.

Can Artificial Intelligence Replace the Unique Nursing Role?

Artificial intelligence (AI) is transforming healthcare, offering potential benefits and challenges. In healthcare, AI enhances efficiency, streamlines processes, and supports decision-making. However, challenges include potential errors and biases in algorithms, data privacy concerns, legal and ethical issues, and resistance to change. In nursing, a delicate balance emerges between AI and human touch. While AI aids in data-driven decision-making and administrative tasks, it lacks the emotional intelligence, empathy, and nuanced understanding crucial to nursing care. Nurses excel in critical thinking, adaptability to dynamic situations, patient advocacy, collaboration, and establishing human connections. AI supports these functions by automating routine tasks and offering decision support tools, yet its rigidity in dynamic situations and lack of human touch pose limitations. This review underscores the necessity of careful AI integration in healthcare, acknowledging its advantages while mitigating drawbacks. In nursing, the symbiosis between AI and human qualities is vital. The role of AI should be to complement, not replace, the unique skills and empathetic aspects of nursing care. Addressing concerns related to bias, transparency, data privacy, and professional training is essential for maximizing the benefits of AI in healthcare while preserving the human touch in patient care. This article explores whether AI can replace unique nursing roles.

Artificial intelligence in the field of pharmacy practice: A literature review

Artificial intelligence (AI) is a transformative technology used in various industrial sectors including healthcare. In pharmacy practice, AI has the potential to significantly improve medication management and patient care. This review explores various AI applications in the field of pharmacy practice. The incorporation of AI technologies provides pharmacists with tools and systems that help them make accurate and evidence-based clinical decisions. By using AI algorithms and Machine Learning, pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations tailored to individual patient requirements. Various AI models have been developed to predict and detect adverse drug events, assist clinical decision support systems with medication-related decisions, automate dispensing processes in community pharmacies, optimize medication dosages, detect drug-drug interactions, improve adherence through smart technologies, detect and prevent medication errors, provide medication therapy management services, and support telemedicine initiatives. By incorporating AI into clinical practice, health care professionals can augment their decision-making processes and provide patients with personalized care. AI allows for greater collaboration between different healthcare services provided to a single patient. For patients, AI may be a useful tool for providing guidance on how and when to take a medication, aiding in patient education, and promoting medication adherence and AI may be used to know how and where to obtain the most cost-effective healthcare and how best to communicate with healthcare professionals, optimize the health monitoring using wearables devices, provide everyday lifestyle and health guidance, and integrate diet and exercise.

Arthrosis diagnosis and treatment recommendations in clinical practice: an exploratory investigation with the generative AI model GPT-4

BACKGROUND

The spread of artificial intelligence (AI) has led to transformative advancements in diverse sectors, including healthcare. Specifically, generative writing systems have shown potential in various applications, but their effectiveness in clinical settings has been barely investigated. In this context, we evaluated the proficiency of ChatGPT-4 in diagnosing gonarthrosis and coxarthrosis and recommending appropriate treatments compared with orthopaedic specialists.

METHODS

A retrospective review was conducted using anonymized medical records of 100 patients previously diagnosed with either knee or hip arthrosis. ChatGPT-4 was employed to analyse these historical records, formulating both a diagnosis and potential treatment suggestions. Subsequently, a comparative analysis was conducted to assess the concordance between the AI's conclusions and the original clinical decisions made by the physicians.

RESULTS

In diagnostic evaluations, ChatGPT-4 consistently aligned with the conclusions previously drawn by physicians. In terms of treatment recommendations, there was an 83% agreement between the AI and orthopaedic specialists. The therapeutic concordance was verified by the calculation of a Cohen's Kappa coefficient of 0.580 (p < 0.001). This indicates a moderate-to-good level of agreement. In recommendations pertaining to surgical treatment, the AI demonstrated a sensitivity and specificity of 78% and 80%, respectively. Multivariable logistic regression demonstrated that the variables reduced quality of life (OR 49.97, p < 0.001) and start-up pain (OR 12.54, p = 0.028) have an influence on ChatGPT-4's recommendation for a surgery.

CONCLUSION

This study emphasises ChatGPT-4's notable potential in diagnosing conditions such as gonarthrosis and coxarthrosis and in aligning its treatment recommendations with those of orthopaedic specialists. However, it is crucial to acknowledge that AI tools such as ChatGPT-4 are not meant to replace the nuanced expertise and clinical judgment of seasoned orthopaedic surgeons, particularly in complex decision-making scenarios regarding treatment indications. Due to the exploratory nature of the study, further research with larger patient populations and more complex diagnoses is necessary to validate the findings and explore the broader potential of AI in healthcare.

LEVEL OF EVIDENCE

Level III evidence.

American public opinion on artificial intelligence in healthcare

Billions of dollars are being invested into developing medical artificial intelligence (AI) systems and yet public opinion of AI in the medical field seems to be mixed. Although high expectations for the future of medical AI do exist in the American public, anxiety and uncertainty about what it can do and how it works is widespread. Continuing evaluation of public opinion on AI in healthcare is necessary to ensure alignment between patient attitudes and the technologies adopted. We conducted a representative-sample survey (total N = 203) to measure the trust of the American public towards medical AI. Primarily, we contrasted preferences for AI and human professionals to be medical decision-makers. Additionally, we measured expectations for the impact and use of medical AI in the future. We present four noteworthy results: (1) The general public strongly prefers human medical professionals make medical decisions, while at the same time believing they are more likely to make culturally biased decisions than AI. (2) The general public is more comfortable with a human reading their medical records than an AI, both now and "100 years from now." (3) The general public is nearly evenly split between those who would trust their own doctor to use AI and those who would not. (4) Respondents expect AI will improve medical treatment but more so in the distant future than immediately.

A Framework to Guide Implementation of AI in Health Care: Protocol for a Cocreation Research Project

BACKGROUND

Artificial intelligence (AI) has the potential in health care to transform patient care and administrative processes, yet health care has been slow to adopt AI due to many types of barriers. Implementation science has shown the importance of structured implementation processes to overcome implementation barriers. However, there is a lack of knowledge and tools to guide such processes when implementing AI-based applications in health care.

OBJECTIVE

The aim of this protocol is to describe the development, testing, and evaluation of a framework, "Artificial Intelligence-Quality Implementation Framework" (AI-QIF), intended to guide decisions and activities related to the implementation of various AI-based applications in health care.

METHODS

The paper outlines the development of an AI implementation framework for broad use in health care based on the Quality Implementation Framework (QIF). QIF is a process model developed in implementation science. The model guides the user to consider implementation-related issues in a step-by-step design and plan and perform activities that support implementation. This framework was chosen for its adaptability, usability, broad scope, and detailed guidance concerning important activities and considerations for successful implementation. The development will proceed in 5 phases with primarily qualitative methods being used. The process starts with phase I, in which an AI-adapted version of QIF is created (AI-QIF). Phase II will produce a digital mockup of the AI-QIF. Phase III will involve the development of a prototype of the AI-QIF with an intuitive user interface. Phase IV is dedicated to usability testing of the prototype in health care environments. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Cocreation is a guiding principle for the project and is an important aspect in 4 of the 5 development phases. The cocreation process will enable the use of both on research-based and practice-based knowledge.

RESULTS

The project is being conducted within the frame of a larger research program, with the overall objective of developing theoretically and empirically informed frameworks to support AI implementation in routine health care. The program was launched in 2021 and has carried out numerous research activities. The development of AI-QIF as a tool to guide the implementation of AI-based applications in health care will draw on knowledge and experience acquired from these activities. The framework is being developed over 2 years, from January 2023 to December 2024. It is under continuous development and refinement.

CONCLUSIONS

The development of the AI implementation framework, AI-QIF, described in this study protocol aims to facilitate the implementation of AI-based applications in health care based on the premise that implementation processes benefit from being well-prepared and structured. The framework will be coproduced to enhance its relevance, validity, usefulness, and potential value for application in practice.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50216.

Factors influencing student nurses' readiness to adopt artificial intelligence (AI) in their studies and their perceived barriers to accessing AI technology: A cross-sectional study

BACKGROUND

With the projected significant increase in the use of AI in nursing education, it becomes vital for nurse faculty to adequately equip student nurses with the necessary competences to effectively utilize AI in their studies. Ensuring that student nurses are prepared and ready to embrace AI technology is imperative for their successful integration into the healthcare workforce.

OBJECTIVE

This study aimed to examine student nurses' readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology.

DESIGN

Cross sectional study.

SETTINGS

One public-owned nursing school in the Philippines.

PARTICIPANTS

Three hundred twenty-three student nurses.

METHODS

Data were collected using structured questionnaires. Descriptive statistics and multivariable analysis were performed to analyze the data.

RESULTS

The results revealed that student nurses demonstrated moderate readiness to embrace AI in their studies (M = 2.906, SD = 0.692) and perceived moderate barriers to accessing AI technology (M = 2.336, SD = 0.719). Factors associated with students' readiness to embrace AI included self-rated technological proficiency (β = 0.170, p = 0.014), understanding of AI-powered technologies (β = 0.260, p < 0.001), and perceived AI use in nursing practice (β = 0.153, p = 0.022). The study also identified potential barriers to accessing AI technology, such as lack of computer skills to navigate AI, lack of AI knowledge and awareness, and time constraints.

CONCLUSION

The findings of this study provided valuable insights into the factors influencing student nurses' attitudes towards AI and shed light on their perceived barriers to accessing AI technology. By enhancing technological proficiency, increasing AI understanding, and providing practical experiences, nurse faculty can better prepare future nurses to effectively navigate the AI-driven healthcare environment and contribute to improved patient care outcomes.

Novel Machine Learning Algorithms for Prediction of Treatment Decisions in Adult Patients With Class III Malocclusion

BACKGROUND

Management of Class III (Cl III) dentoskeletal phenotype is often expert-driven.

PURPOSE

The aim is to identify critical morphological features in postcircumpubertal Cl III treatment and appraise the predictive ability of innovative machine learning (ML) algorithms for adult Cl III malocclusion treatment planning.

STUDY DESIGN

The Orthodontics Department at the University of Illinois Chicago undertook a retrospective cross-sectional study analyzing Cl III malocclusion cases (2003-2020) through dental records and pretreatment lateral cephalograms.

PREDICTOR

Forty features were identified through a literature review and gathered from pretreatment records, serving as ML model inputs. Eight ML models were trained to predict the best treatment for adult Cl III malocclusion.

OUTCOME VARIABLE

Predictive accuracy, sensitivity, and specificity of the models, along with the highest-contributing features, were evaluated for performance assessment.

COVARIATES

Demographic covariates, including age, gender, race, and ethnicity, were assessed. Inclusion criteria targeted patients with cervical vertebral maturation stage 4 or above. Operative covariates such as tooth extraction and types of orthognathic surgical maneuvers were also analyzed.

ANALYSES

Demographic characteristics of the camouflage and surgical study groups were described statistically. Shapiro-Wilk Normality test was employed to check data distribution. Differences in means between groups were evaluated using parametric and nonparametric independent sample tests, with statistical significance set at <0.05.

RESULTS

The study involved 182 participants; 65 underwent camouflage mechanotherapy, and 117 received orthognathic surgery. No statistical differences were found in demographic characteristics between the two groups (P > .05). Extreme values of pretreatment parameters suggested a surgical approach. Artificial neural network algorithms predicted treatment approach with 91% accuracy, while the Extreme Gradient Boosting model achieved 93% accuracy after recursive feature elimination optimization. The Extreme Gradient Boosting model highlighted Wit's appraisal, anterior overjet, and Mx/Md ratio as key predictors.

CONCLUSIONS

The research identified significant cephalometric differences between Cl III adults requiring orthodontic camouflage or surgery. A 93% accurate artificial intelligence model was formulated based on these insights, highlighting the potential role of artificial intelligence and ML as adjunct tools in orthodontic diagnosis and treatment planning. This may assist in minimizing clinician subjectivity in borderline cases.

Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia

BACKGROUND

Artificial intelligence (AI) is a novel technology that has been widely acknowledged for its potential to improve the processes' efficiency across industries. However, its barriers and facilitators in healthcare are not completely understood due to its novel nature.

STUDY PURPOSE

The purpose of this study is to explore the intricate landscape of AI use in family medicine, aiming to uncover the factors that either hinder or enable its successful adoption.

METHODS

A cross-sectional survey design is adopted in this study. The questionnaire included 10 factors (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, personal innovativeness, ethical concerns, and facilitators) affecting the acceptance of AI. A total of 157 family physicians participated in the online survey.

RESULTS

Effort expectancy (μ = 3.85) and facilitating conditions (μ = 3.77) were identified to be strong influence factors. Access to data (μ = 4.33), increased computing power (μ = 3.92), and telemedicine (μ = 3.78) were identified as major facilitators; regulatory support (μ = 2.29) and interoperability standards (μ = 2.71) were identified as barriers along with privacy and ethical concerns. Younger individuals tend to have more positive attitudes and expectations toward AI-enabled assistants compared to older participants (p < .05). Perceived privacy risk is negatively correlated with all factors.

CONCLUSION

Although there are various barriers and concerns regarding the use of AI in healthcare, the preference for AI use in healthcare, especially family medicine, is increasing.

Detection and Severity Identification of Neonatal Seizure Using Deep Convolutional Neural Networks from Multichannel EEG Signal

Introduction

One of the most frequent neurological conditions in newborns is neonatal seizures, which may indicate severe neurological dysfunction. These seizures may have very subtle or very modest clinical indications because patterns like oscillatory (spike) trains begin with relatively low amplitude and gradually increase over time. This becomes very challenging and erroneous if clinical observation is the primary basis for identifying newborn seizures. In this study, a diagnosis system using deep convolutional neural networks is proposed to determine and classify the severity level of neonatal seizures using multichannel neonatal EEG data.

Methods

Datasets from publicly accessible online sources were used to compile clinical multichannel EEG datasets. Various preprocessing steps were taken, including the conversion of 2D time series data to equivalent waveform pictures. The proposed models have undergone training, and evaluations of their performance were conducted.

Results

The proposed CNN was used to perform binary classification with an accuracy of 92.6%, F1-score of 92.7%, specificity of 92.8%, and precision of 92.6%. To detect newborn seizures, this model is utilized. Using the proposed CNN model, multiclassification was performed with accuracy rates of 88.6%, specificity rates of 92.18%, F1-score rates of 85.61%, and precision rates of 88.9%. The results demonstrated that the suggested strategy can assist medical professionals in making accurate diagnoses close to healthcare institutions.

Conclusion

The developed system was capable of detecting neonatal seizures and has the potential to be used as a decision-making tool in resource-limited areas with a scarcity of expert neurologists.

Assessing the Performance of Chat Generative Pretrained Transformer (ChatGPT) in Answering Andrology-Related Questions

OBJECTIVE

The internet and social media have become primary sources of health information, with men frequently turning to these platforms before seeking professional help. Chat generative pretrained transformer (ChatGPT), an artificial intelligence model developed by OpenAI, has gained popularity as a natural language processing program. The present study evaluated the accuracy and reproducibility of ChatGPT's responses to andrology-related questions.

METHODS

The study analyzed frequently asked andrology questions from health forums, hospital websites, and social media platforms like YouTube and Instagram. Questions were categorized into topics like male hypogonadism, erectile dysfunction, etc. The European Association of Urology (EAU) guideline recommendations were also included. These questions were input into ChatGPT, and responses were evaluated by 3 experienced urologists who scored them on a scale of 1 to 4.

RESULTS

Out of 136 evaluated questions, 108 met the criteria. Of these, 87.9% received correct and adequate answers, 9.3% were correct but insufficient, and 3 responses contained both correct and incorrect information. No question was answered completely wrong. The highest correct answer rates were for disorders of ejaculation, penile curvature, and male hypogonadism. The EAU guideline-based questions achieved a correctness rate of 86.3%. The reproducibility of the answers was over 90%.

CONCLUSION

The study found that ChatGPT provided accurate and reliable answers to over 80% of andrology-related questions. While limitations exist, such as potential outdated data and inability to understand emotional aspects, ChatGPT's potential in the health-care sector is promising. Collaborating with health-care professionals during artificial intelligence model development could enhance its reliability.

Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare

This comprehensive review delves into the impact and challenges of Artificial Intelligence (AI) in nursing science and healthcare. AI has already demonstrated its transformative potential in these fields, with applications spanning from personalized care and diagnostic accuracy to predictive analytics and telemedicine. However, the integration of AI has its complexities, including concerns related to data privacy, ethical considerations, and biases in algorithms and datasets. The future of healthcare appears promising, with AI poised to advance diagnostics, treatment, and healthcare practices. Nevertheless, it is crucial to remember that AI should complement, not replace, healthcare professionals, preserving the essential human element of care. To maximize AI's potential in healthcare, interdisciplinary collaboration, ethical guidelines, and the protection of patient rights are essential. This review concludes with a call to action, emphasizing the need for ongoing research and collective efforts to ensure that AI contributes to improved healthcare outcomes while upholding the highest standards of ethics and patient-centered care.