The potential for artificial intelligence in healthcare

The role of artificial intelligence in macular hole management: A scoping review

We focus on the utility of artificial intelligence (AI) in the management of macular hole (MH). We synthesize 25 studies, comprehensively reporting on each AI model's development strategy, validation, tasks, performance, strengths, and limitations. All models analyzed ophthalmic images, and 5 (20 %) also analyzed clinical features. Study objectives were categorized based on 3 stages of MH care: diagnosis, identification of MH characteristics, and postoperative predictions of hole closure and vision recovery. Twenty-two (88 %) AI models underwent supervised learning, and the models were most often deployed to determine a MH diagnosis. None of the articles applied AI to guiding treatment plans. AI model performance was compared to other algorithms and to human graders. Of the 10 studies comparing AI to human graders (i.e., retinal specialists, general ophthalmologists, and ophthalmology trainees), 5 (50 %) reported equivalent or higher performance. Overall, AI analysis of images and clinical characteristics in MH demonstrated high diagnostic and predictive accuracy. Convolutional neural networks comprised the majority of included AI models, including those which were high performing. Future research may consider validating algorithms to propose personalized treatment plans and explore clinical use of the aforementioned algorithms.

Overcoming barriers and enabling artificial intelligence adoption in allied health clinical practice: A qualitative study

Background

Artificial intelligence (AI) has the potential to revolutionise healthcare. If the implementation is successful it has the potential to improve healthcare outcomes for patients and organisations. Little is known about the perceptions of allied health professionals (AHPs) towards AI in healthcare.

Objective

This study investigated barriers and enablers to AI implementation in the delivery of healthcare from the AHPs perspective.

Methods

Qualitative methodology informed by behaviour change theory using focus groups with AHPs at a health service in Queensland, Australia.

Results

Twenty-four barriers and 24 enablers were identified by 25 participants across four focus groups. Barriers included: lack of AI knowledge, explainability challenges, risk to professional practice, negative impact on professional practice, and role replacement. Enablers include AI training and education, regulation, reputation, understanding the healthcare benefits of AI and engaging clinical champions.

Conclusions

AHPs have concerns about the impact and trustworthiness of AI and the readiness of organisations to support its use. Organisations must take a proactive approach and adopt targeted and multifaceted strategies to address barriers. This may include workforce upskilling, clear communication of the benefits of AI use of local champions and ongoing research.

Current Status and Future of Artificial Intelligence in Medicine

Artificial intelligence (AI) has rapidly emerged as a transformative force in medicine, revolutionizing various aspects of healthcare from diagnostics and treatment to public health and patient care. This narrative review synthesizes evidence from diverse study designs, exploring the current and future applications of AI in medicine. We highlight AI's role in improving diagnostic accuracy, optimizing treatment strategies, and enhancing patient care through personalized interventions and remote monitoring, drawing upon recent advancements and landmark studies. Emerging trends such as explainable AI and federated learning are also examined. While acknowledging the tremendous potential of AI in medicine, the review also addresses the barriers and ethical challenges that need to be overcome, including concerns about algorithmic bias, transparency, over-reliance, and the potential impact on the healthcare workforce. We emphasize the importance of establishing regulatory guidelines, fostering collaboration between clinicians and AI developers, and ensuring ongoing education for healthcare professionals. Despite these challenges, the future of AI in medicine holds immense promise, with the potential to significantly improve patient outcomes, transform healthcare delivery, and address healthcare disparities.

Impact of AI and big data analytics on healthcare outcomes: An empirical study in Jordanian healthcare institutions

Artificial intelligence (AI) and big data analytics are transforming healthcare globally and in Jordan. This study investigates the effects of AI and big data analytics on healthcare outcomes in Jordanian healthcare institutions. A comprehensive model is proposed to understand the antecedents of healthcare outcomes, including the impact of perceived ease of use, perceived usefulness, and organizational capabilities. Data were collected from 400 structured questionnaires, with a final sample size of 288 respondents, and analyzed using partial least squares structural equation modeling. The findings reveal that AI technologies significantly improve diagnostic accuracy and treatment planning, while big data analytics enhances operational efficiency and patient care. However, the comparative influence of AI on different healthcare processes was less significant. Additionally, robust organizational capabilities effectively enhanced the adoption and impact of AI and big data analytics. The study highlights the mediating roles of perceived ease of use and usefulness in the relationship between technology adoption and healthcare outcomes. Understanding the interplay between AI, big data analytics, and healthcare delivery is crucial for policymakers, healthcare administrators, and technology developers to develop effective strategies that improve patient care and operational efficiency. This study recommends investing in user-friendly AI and big data analytics tools, enhancing organizational capabilities, and providing comprehensive training for healthcare professionals. Future research should extend this study to different cultural contexts to validate the findings and contribute further to the literature on AI and healthcare.

Diagnostic Stewardship in Clinical Microbiology: An Indispensable Component of Patient Care

Emerging infectious diseases and increasing resistance to available antimicrobials are mapping the evolution of clinical microbiology and escalating the nature of undertakings required. Rapid diagnosis has become the need of the hour, which can affect diagnostic algorithms and therapeutic decisions simultaneously. Subsequently, the concept of 'diagnostic stewardship' was introduced into clinical practice for coherent implementation of available diagnostic modalities to ensure that these new rapid diagnostic technologies are conserved, rather than consumed as part of health care resources, with a view to improve the patient care and reduce Turnaround Time (TAT) and treatment expense. The present study highlights the requisite of diagnostic stewardship and outlines the infectious disease diagnostic modalities that can assist in its successful implementation. Diagnostic stewardship promotes precise, timely diagnostics, from the initial specimen collection and identification to reporting with appropriate TAT, so as to enable timely management of the patient. The main aim of diagnostic stewardship is to optimize the right choice of diagnostic test for the right patient to attain clinically significant reports with the least possible TAT for timely management and the least expected adverse effects for the patient, community, and the healthcare system. This underlines the requisite of a multifaceted approach to make technological advancements effective and successful for implementation as a part of diagnostic stewardship for the best patient care.

Feasibility and Preliminary Behavioral and Clinical Efficacy of a Diabetes Education Chatbot Pilot Among Adults With Type 2 Diabetes

BACKGROUND

Diabetes self-management education and support (DSMES) improves diabetes outcomes yet remains consistently underutilized. Chatbot technology offers the potential to increase access to and engagement in DSMES. Evidence supporting the case for chatbot uptake and efficacy in people with diabetes (PWD) is needed.

METHOD

A diabetes education and support chatbot was deployed in a regional health care system. Adults with type 2 diabetes with an A1C of 8.0% to 8.9% and/or having recently completed a 12-week diabetes care management program were enrolled in a pilot program. Weekly chats included three elements: knowledge assessment, limited self-reporting of blood glucose data and medication taking behaviors, and education content (short videos and printable materials). A clinician facing dashboard identified need for escalation via flags based on participant responses. Data were collected to assess satisfaction, engagement, and preliminary glycemic outcomes.

RESULTS

Over 16 months, 150 PWD (majority above 50 years of age, female, and African American) were enrolled. The unenrollment rate was 5%. Most escalation flags (N = 128) were for hypoglycemia (41%), hyperglycemia (32%), and medication issues (11%). Overall satisfaction was high for chat content, length, and frequency, and 87% reported increased self-care confidence. Enrollees completing more than one chat had a mean drop in A1C of -1.04%, whereas those completing one chat or less had a mean increase in A1C of +0.09% (P = .008).

CONCLUSION

This diabetes education chatbot pilot demonstrated PWD acceptability, satisfaction, and engagement plus preliminary evidence of self-care confidence and A1C improvement. Further efforts are needed to validate these promising early findings.

Superficial Fungal Infections and Artificial Intelligence: A Review on Current Advances and Opportunities: REVISION

BACKGROUND

Superficial fungal infections are among the most common infections in world, they mainly affect skin, nails and scalp without further invasion. Superficial fungal diseases are conventionally diagnosed with direct microscopy, fungal culture or histopathology, treated with topical or systemic antifungal agents and prevented in immunocompetent patients by improving personal hygiene. However, conventional diagnostic tests can be time-consuming, also treatment can be insufficient or ineffective and prevention can prove to be demanding. Artificial Intelligence (AI) refers to a digital system having an intelligence akin to a human being. The concept of AI has existed since 1956, but hasn't been practicalised until recently. AI has revolutionised medical research in the recent years, promising to influence almost all specialties of medicine.

OBJECTIVE

An increasing number of articles have been published about the usage of AI in cutaneous mycoses.

METHODS

In this review, the key findings of articles about utilisation of AI in diagnosis, treatment and prevention of superficial fungal infections are summarised. Moreover, the need for more research and development is highlighted.

RESULTS

Fifty-four studies were reviewed. Onychomycosis was the most researched superficial fungal infection. AI can be used diagnosing fungi in macroscopic and microscopic images and classify them to some extent. AI can be a tool and be used as a part of something bigger to diagnose superficial mycoses.

CONCLUSION

AI can be used in all three steps of diagnosing, treating and preventing. AI can be a tool complementary to the clinician's skills and laboratory results.

Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.

Challenges and opportunities of artificial intelligence in African health space

The application of artificial intelligence (AI) to healthcare in Africa has the potential to transform productivity, diagnosis, disease surveillance, and resource allocation by improving accuracy and efficiency. However, to fully realize its benefits, it is necessary to consider issues concerning data privacy, equity, infrastructure integration, and ethical policy development. The use of these tools may improve the detection of diseases, the distribution of resources, and the continuity of care. The use of AI allows for the development of policies that are tailored to address health disparities based on evidence. While AI may increase accessibility and affordability through telehealth, remote monitoring, and cost reductions, significant barriers remain. Ethical guidelines are needed to ensure AI decisions align with medical standards and patient autonomy. Strict privacy and security controls are crucial to protecting sensitive health data. This article evaluates the current and potential roles of AI in the African health sector. It identifies opportunities to address challenges through tailored interventions and an AI framework to simulate policy impacts.

Artificial intelligence versus clinical judgement: how accurately do generative models reflect CNS guidelines for chiari malformation?

OBJECTIVE

This study investigated the response and readability of generative artificial intelligence (AI) models to questions and recommendations proposed by the 2023 Congress of Neurological Surgeons (CNS) guidelines for Chiari 1 malformation.

METHODS

Thirteen questions were generated from CNS guidelines and asked to Perplexity, ChatGPT 4o, Microsoft Copilot, and Google Gemini. AI answers were divided into two categories, "concordant" and "non-concordant," according to their alignment with current CNS guidelines. Non-concordant answers were sub-categorized as "insufficient" or "over-conclusive." Responses were evaluated for readability via the Flesch-Kincaid Grade Level, Gunning Fog Index, SMOG (Simple Measure of Gobbledygook) Index, and Flesch Reading Ease test.

RESULTS

Perplexity displayed the highest concordance rate of 69.2 %, with non-concordant responses classified as 0 % insufficient and 30.8 % over-conclusive. ChatGPT 4o had the lowest concordance rate at 23.1 %, with 0 % insufficient and 76.9 % over-conclusive classifications. Copilot showed a 61.5 % concordance rate, with 7.7 % insufficient and 30.8 % over-conclusive. Gemini demonstrated a 30.8 % concordance rate, with 7.7 % insufficient and 61.5 % as over-conclusive. Flesch-Kincaid Grade Level scores ranged from 14.48 (Gemini) to 16.48 (Copilot), Gunning Fog Index scores varied between 16.18 (Gemini) and 18.8 (Copilot), SMOG Index scores ranged from 16 (Gemini) to 17.54 (Copilot), and Flesch Reading Ease scores were low across all models, with Gemini showing the highest mean score of 21.3.

CONCLUSION

Perplexity and Copilot emerged as the best-performing for concordance, while ChatGPT and Gemini displayed the highest over-conclusive rates. All responses showcased high complexity and difficult readability. While AI can be valuable in certain aspects of clinical practice, the low concordance rates show that AI should not replace clinician judgement.

Artificial intelligence in healthcare (Review)

The potential of artificial intelligence (AI) to significantly transform numerous aspects of contemporary civilization is substantial. Advancements in research show an increasing interest in creating AI solutions in the healthcare sector. This interest is driven by the broad spectrum and extensive nature of easily accessible patient data-including medical imaging, digitized data collection, and electronic health records - and by the ability to analyze and interpret complex data, facilitating more accurate and timely diagnoses. This review's goal is to provide a comprehensive overview of the advancements achieved by AI in healthcare, to elucidate the present state of AI in enhancing the healthcare system and improving the quality and efficiency of healthcare decision making, and to discuss selected medical applications of AI. Furthermore, the barriers and constraints that may impede the use of AI in healthcare are outlined, and the potential future directions of AI-augmented healthcare systems are discussed.

Generative AI Decision-Making Attributes in Complex Health Services: A Rapid Review

The advent of Generative Artificial Intelligence (Generative AI or GAI) marks a significant inflection point in AI development. Long viewed as the epitome of reasoning and logic, Generative AI incorporates programming rules that are normative. However, it also has a descriptive component based on its programmers' subjective preferences and any discrepancies in the underlying data. Generative AI generates both truth and falsehood, supports both ethical and unethical decisions, and is neither transparent nor accountable. These factors pose clear risks to optimal decision-making in complex health services such as health policy and health regulation. It is important to examine how Generative AI makes decisions both from a rational, normative perspective and from a descriptive point of view to ensure an ethical approach to Generative AI design, engineering, and use. The objective is to provide a rapid review that identifies and maps attributes reported in the literature that influence Generative AI decision-making in complex health services. This review provides a clear, reproducible methodology that is reported in accordance with a recognised framework and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 standards adapted for a rapid review. Inclusion and exclusion criteria were developed, and a database search was undertaken within four search systems: ProQuest, Scopus, Web of Science, and Google Scholar. The results include articles published in 2023 and early 2024. A total of 1,550 articles were identified. After removing duplicates, 1,532 articles remained. Of these, 1,511 articles were excluded based on the selection criteria and a total of 21 articles were selected for analysis. Learning, understanding, and bias were the most frequently mentioned Generative AI attributes. Generative AI brings the promise of advanced automation, but carries significant risk. Learning and pattern recognition are helpful, but the lack of a moral compass, empathy, consideration for privacy, and a propensity for bias and hallucination are detrimental to good decision-making. The results suggest that there is, perhaps, more work to be done before Generative AI can be applied to complex health services.

Enhancing Infection Control in ICUS Through AI: A Literature Review

Introduction

Infection control in intensive care units (ICUs) is crucial due to the high risk of healthcare-associated infections (HAIs), which can increase patient morbidity, mortality, and costs. Effective measures such as hand hygiene, use of personal protective equipment (PPE), patient isolation, and environmental cleaning are vital to minimize these risks. The integration of artificial intelligence (AI) offers new opportunities to enhance infection control, from predicting outbreaks to optimizing antimicrobial use, ultimately improving patient safety and care in ICUs.

Objectives

The primary objectives are to explore AI's impact on predicting HAIs, real-time monitoring, automated sterilization, resource optimization, and personalized infection control plans.

Methodology

A comprehensive search of PubMed and Scopus was conducted for relevant articles up to January 2024, including case series, reports, and cohort studies. Animal studies and irrelevant articles were excluded, with a focus on those considered to have significant clinical relevance.

Discussion

The review highlights AI's prowess in predicting HAIs, surpassing conventional methods. Existing evidence demonstrates AI's efficacy in accurately predicting and mitigating HAIs. Real-time patient monitoring and alert systems powered by AI are shown to enhance infection detection and patient outcomes. The paper also addresses AI's role in automating sterilization and disinfection, with studies affirming its effectiveness in reducing infections. AI's resource optimization capabilities are exemplified in ICU settings, showcasing its potential to improve resource allocation efficiency. Furthermore, the review emphasizes AI's personalized approach to infection control post-procedures, elucidating its ability to analyze patient data and create tailored control plans.

The application of artificial intelligence in health communication development: A scoping review

This scoping review explores the integration of Artificial Intelligence (AI) with communication, behavioral, and social theories to enhance health behavior interventions. A systematic search of articles published through February 2024, following PRISMA guidelines, identified 28 relevant studies from 13,723 screened. These studies, conducted across various countries, addressed health issues such as smoking cessation, musculoskeletal injuries, diabetes, chronic diseases and mental health using AI-driven tools like chatbots and apps. Despite AI's potential, a gap exists in aligning technical advancements with theoretical frameworks. The proposed AI Impact Communications Model (AI-ICM) aims to bridge this gap, offering a road map for future research and practice.

Current Application and Future Prospects of Artificial Intelligence in Healthcare and Medical Education: A Review of Literature

Artificial Intelligence (AI) is being used in every aspect of life today. It has found great application in the healthcare sector, with the use of this technology by medical schools all over the globe. AI has found multiple applications in medical fields such as diagnostics, medicine, surgery, oncology, radiology, ophthalmology, medical education, and numerous other medical fields. It has assisted in diagnosing conditions in a much quicker and more efficient manner, and the use of AI chatbots has greatly enhanced the learning process. Despite the benefits that AI applications provide, such as saving precious time for healthcare givers, there are also concerns regarding AI, mainly, ethical, and the fact that they might render the human race unemployed. However, despite these concerns, a lot of innovations are being made using AI applications, which show a very bright prospect for this technology. Although humans use AI in every part of their daily lives, they are also opposed to its use because they believe it could eventually replace them in the future. In this review of literature, a detailed analysis of the use of AI in the healthcare industry and medical education will be done, along with its shortcomings as well as its future prospects.

The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency

Background and Aims

Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.

Methods

This review analyzes the impact of AI on healthcare using data from the Web of Science (2014-2024), focusing on keywords like AI, ML, and healthcare applications. It examines the uses and effects of AI on healthcare by synthesizing recent literature and real-world case studies, such as Google Health and IBM Watson Health, highlighting AI technologies, their useful applications, and the difficulties in putting them into practice, including problems with data security and resource limitations. The review also discusses new developments in AI, and how they can affect society.

Results

The findings demonstrate how AI is enhancing the skills of medical professionals, enhancing diagnosis, and opening the door to more individualized treatment plans, as reflected in the steady rise of AI-related healthcare publications from 158 articles (3.54%) in 2014 to 731 articles (16.33%) by 2024. Core applications like remote monitoring and predictive analytics improve operational effectiveness and patient involvement. However, there are major obstacles to the mainstream implementation of AI in healthcare, including issues with data security and budget constraints.

Conclusion

Healthcare may be transformed by AI, but its successful use requires ethical and responsible use. To meet the changing demands of the healthcare sector and guarantee the responsible application of AI technologies, the evaluation highlights the necessity of ongoing research, instruction, and multidisciplinary cooperation. In the future, integrating AI responsibly will be essential to optimizing its advantages and reducing related dangers.

Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform

Bed regulation within Brazil's National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. In Rio Grande do Norte, Brazil, the RegulaRN Leitos Gerais platform was the information system developed to register requests for bed regulation for COVID-19 cases. However, the platform was expanded to cover a range of diseases that require hospitalization. This study explored different machine learning models in the RegulaRN database, from October 2021 to January 2024, totaling 47,056 regulations. From the data obtained, 12 features were selected from the 24 available. After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. Data was also correlated, balanced, and divided into training and test portions for application in machine learning models. The results showed better accuracy (87.77%) and recall (87.77%) for the XGBoost model, and higher precision (87.85%) and F1-Score (87.56%) for the Random Forest and Gradient Boosting models, respectively. As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.

Allied Health Professionals' Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey

BACKGROUND

Artificial intelligence (AI) has the potential to address growing logistical and economic pressures on the health care system by reducing risk, increasing productivity, and improving patient safety; however, implementing digital health technologies can be disruptive. Workforce perception is a powerful indicator of technology use and acceptance, however, there is little research available on the perceptions of allied health professionals (AHPs) toward AI in health care.

OBJECTIVE

This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use in health care delivery.

METHODS

A cross-sectional survey was conducted at a health service in, Queensland, Australia, using the Shinners Artificial Intelligence Perception tool.

RESULTS

A total of 231 (22.1%) participants from 11 AHPs responded to the survey. Participants were mostly younger than 40 years (157/231, 67.9%), female (189/231, 81.8%), working in a clinical role (196/231, 84.8%) with a median of 10 years' experience in their profession. Most participants had not used AI (185/231, 80.1%), had little to no knowledge about AI (201/231, 87%), and reported workforce knowledge and skill as the greatest challenges to incorporating AI in health care (178/231, 77.1%). Age (P=.01), profession (P=.009), and AI knowledge (P=.02) were strong predictors of the perceived professional impact of AI. AHPs generally felt unprepared for the implementation of AI in health care, with concerns about a lack of workforce knowledge on AI and losing valued tasks to AI. Prior use of AI (P=.02) and years of experience as a health care professional (P=.02) were significant predictors of perceived preparedness for AI. Most participants had not received education on AI (190/231, 82.3%) and desired training (170/231, 73.6%) and believed AI would improve health care. Ideas and opportunities suggested for the use of AI within the allied health setting were predominantly nonclinical, administrative, and to support patient assessment tasks, with a view to improving efficiencies and increasing clinical time for direct patient care.

CONCLUSIONS

Education and experience with AI are needed in health care to support its implementation across allied health, the second largest workforce in health. Industry and academic partnerships with clinicians should not be limited to AHPs with high AI literacy as clinicians across all knowledge levels can identify many opportunities for AI in health care.

Human Dignity and Artificial Intelligence in Healthcare: A Basis for a Catholic Ethics on AI

The rise of artificial intelligence (AI) has caught the attention of the world as it challenges the status quo on human operations. As AI has dramatically impacted education, healthcare, industry, and economics, a Catholic ethical study of human dignity in the context of AI in healthcare is presented in this article. The debates regarding whether AI will usher well or doom the dignity of humankind are occasioned by increasing developments of technology in patient care and medical decision-making. This paper draws from Catholic ethics, especially the concepts of inherent human dignity, the sanctity of human life, and morality in the medical field. It talks about using AI to upgrade healthcare outcomes without losing the essential humanity of human dignity in medical practice. It also touches on the most likely ethical issues: the morality of AI-related decisions and the depersonalization of health care. Finally, it provides a framework that brings AI development in tandem with a Catholic vision of human dignity and supports a healthcare system that caters to the common good but correctly respects the irreplaceable value of the human person and highlights moral responsibility.

Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis. This article provides a meticulous and comprehensive review of imaging-based SARS-CoV-2 diagnosis using deep learning techniques up to May 2024. This article starts with an overview of imaging-based SARS-CoV-2 diagnosis, covering the basic steps of deep learning-based SARS-CoV-2 diagnosis, SARS-CoV-2 data sources, data pre-processing methods, the taxonomy of deep learning techniques, findings, research gaps and performance evaluation. We also focus on addressing current privacy issues, limitations, and challenges in the realm of SARS-CoV-2 diagnosis. According to the taxonomy, each deep learning model is discussed, encompassing its core functionality and a critical assessment of its suitability for imaging-based SARS-CoV-2 detection. A comparative analysis is included by summarizing all relevant studies to provide an overall visualization. Considering the challenges of identifying the best deep-learning model for imaging-based SARS-CoV-2 detection, the article conducts an experiment with twelve contemporary deep-learning techniques. The experimental result shows that the MobileNetV3 model outperforms other deep learning models with an accuracy of 98.11%. Finally, the article elaborates on the current challenges in deep learning-based SARS-CoV-2 diagnosis and explores potential future directions and methodological recommendations for research and advancement.

Artificial intelligence in medicine and the negative outcome penalty paradox

Artificial intelligence (AI) holds considerable promise for transforming clinical diagnostics. While much has been written both about public attitudes toward the use of AI tools in medicine and about uncertainty regarding legal liability that may be delaying its adoption, the interface of these two issues has so far drawn less attention. However, understanding this interface is essential to determining how jury behaviour is likely to influence adoption of AI by physicians. One distinctive concern identified in this paper is a 'negative outcome penalty paradox' (NOPP) in which physicians risk being penalised by juries in cases with negative outcomes, whether they overrule AI determinations or accept them. The paper notes three reasons why AI in medicine is uniquely susceptible to the NOPP and urges serious further consideration of this complex dilemma.

Research surveys and their evolution: Past, current and future uses in healthcare

Research surveys are believed to have originated in antiquity with evidence of them being performed in ancient Egypt and Greece. In the past century, their use has grown significantly and they are now one of the most frequently employed research methods including in the field of healthcare. Modern validation techniques and processes have allowed researchers to broaden the scope of qualitative data they can gather through these surveys such as an individual's views on service quality to nationwide surveys that are undertaken regularly to follow healthcare trends. This article focuses on the evolution and current utility of research surveys, different methodologies employed in their creation, the advantages and disadvantages of different forms and their future use in healthcare research. We also review the role artificial intelligence and the importance of increased patient participation in the development of these surveys in order to obtain more accurate and clinically relevant data.

Artificial intelligence in risk prediction and diagnosis of vertebral fractures

With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of AI in diagnosing and predicting vertebral fractures through a systematic review and meta-analysis. A comprehensive search across major databases selected studies utilizing AI for vertebral fracture diagnosis or prognosis. Out of 14,161 studies initially identified, 79 were included, with 40 undergoing meta-analysis. Diagnostic models were stratified by pathology: non-pathological vertebral fractures, osteoporotic vertebral fractures, and vertebral compression fractures. The primary outcome measure was AUROC. AI showed high accuracy in diagnosing and predicting vertebral fractures: predictive AUROC = 0.82, osteoporotic vertebral fracture diagnosis AUROC = 0.92, non-pathological vertebral fracture diagnosis AUROC = 0.85, and vertebral compression fracture diagnosis AUROC = 0.87, all significant (p < 0.001). Traditional models had the highest median AUROC (0.90) for fracture prediction, while deep learning models excelled in diagnosing all fracture types. High heterogeneity (I² > 99%, p < 0.001) indicated significant variation in model design and performance. AI technologies show considerable promise in improving the diagnosis and prognostication of vertebral fractures, with high accuracy. However, observed heterogeneity and study biases necessitate further research. Future efforts should focus on standardizing AI models and validating them across diverse datasets to ensure clinical utility.

The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review

AIM

Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging.

METHODS

Following PRISMA guidelines, we systematically searched major databases up to February 2024, focusing on studies that applied deep learning to SpA imaging. Performance metrics, model types, and diagnostic tasks were extracted and analyzed. Study quality was assessed using QUADAS-2.

RESULTS

We analyzed 21 studies employing deep learning in SpA imaging diagnosis across MRI, CT, and X-ray modalities. These models, particularly advanced CNNs and U-Nets, demonstrated high accuracy in diagnosing SpA, differentiating arthritis forms, and assessing disease progression. Performance metrics frequently surpassed traditional methods, with some models achieving AUCs up to 0.98 and matching expert radiologist performance.

CONCLUSION

This systematic review underscores the effectiveness of deep learning in SpA imaging diagnostics across MRI, CT, and X-ray modalities. The studies reviewed demonstrated high diagnostic accuracy. However, the presence of small sample sizes in some studies highlights the need for more extensive datasets and further prospective and external validation to enhance the generalizability of these AI models.

KEY POINTS

Question How can deep learning models improve diagnostic accuracy in imaging for spondyloarthropathies (SpA), addressing challenges in early detection and differentiation from other forms of arthritis? Findings Deep learning models, especially CNNs and U-Nets, showed high accuracy in SpA imaging across MRI, CT, and X-ray, often matching or surpassing expert radiologists. Clinical relevance Deep learning models can enhance diagnostic precision in SpA imaging, potentially reducing diagnostic delays and improving treatment decisions, but further validation on larger datasets is required for clinical integration.

Knowledge, interest and perspectives on Artificial Intelligence in Neurosurgery. A global survey

Introduction

Artificial Intelligence (AI) applications in healthcare are growing exponentially. The field of neurosurgery is particularly suited to implement AI solutions given its technology-driven nature. It is of paramount importance to understand the basics of AI to make informed decision on how to shape current and future applications.

Research question

What is the level of confidence, knowledge and the attitude of the global neurosurgical community towards AI basic concepts and applications?

Material and methods

A 24-item survey was designed and distributed. The survey results reported on level of knowledge, confidence and interest in AI, perspectives and attitude towards the application of AI technologies in neurosurgery. The potential influence of demographics and work-related environment features on AI knowledge was investigated.

Results

We received a total of 250 responses from 61 countries. The correct definition of 'Machine Learning', 'Deep Learning' and main Big Data features were identified by respectively 42%, 23% and 23% of the respondents. The survey unveiled a strong interest and a positive attitude towards the introduction of AI in the neurosurgical practice. The main concerns included trustworthiness and liability, the main barriers to implementation were considered lack of funding, infrastructure, knowledge and multidisciplinary collaboration.

Discussion and conclusion

There is a low familiarity with basic AI concepts in the neurosurgical community. Nevertheless, there is a strong interest and a positive attitude towards AI implementation. The systematization of training and the production of educational resources will be key in guaranteeing a successful implementation of AI in the evolving history of neurosurgery.

Unravelling Orthopaedic Surgeons' Perceptions and Adoption of Generative AI Technologies

This mixed-methods study investigates the adoption of generative AI among orthopaedic surgeons, employing a Unified Theory of Acceptance and Use of Technology (UTAUT) based survey (n = 177) and follow-up interviews (n = 7). The research reveals varying levels of AI familiarity and usage patterns, with higher adoption in research and professional development compared to direct patient care. A significant generational divide in perceived ease of use highlights the need for tailored training approaches. Qualitative insights uncover barriers to adoption, including the need for more evidence-based support, as well as concerns about maintaining critical thinking skills. The study exposes a complex interplay of individual, technological, and organisational factors influencing AI adoption in orthopaedic surgery. The findings underscore the need for a nuanced approach to AI integration that considers the unique aspects of orthopaedic surgery and the diverse perspectives of surgeons at different career stages. This provides valuable insights for educational institutions and healthcare organisations in navigating the challenges and opportunities of AI adoption in specialised medical fields.

Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and repetitive behaviors, often associated with atypical visual attention patterns. In this paper, the Gaze-Based Autism Classifier (GBAC) is proposed, which is a Deep Neural Network model that leverages both data distillation and data attribution techniques to enhance ASD classification accuracy and explainability. Using data sampled by eye tracking sensors, the model identifies unique gaze behaviors linked to ASD and applies an explainability technique called TracIn for data attribution by computing self-influence scores to filter out noisy or anomalous training samples. This refinement process significantly improves both accuracy and computational efficiency, achieving a test accuracy of 94.35% while using only 77% of the dataset, showing that the proposed GBAC outperforms the same model trained on the full dataset and random sample reductions, as well as the benchmarks. Additionally, the data attribution analysis provides insights into the most influential training examples, offering a deeper understanding of how gaze patterns correlate with ASD-specific characteristics. These results underscore the potential of integrating explainable artificial intelligence into neurodevelopmental disorder diagnostics, advancing clinical research by providing deeper insights into the visual attention patterns associated with ASD.

Artificial Intelligence and Modern Technology in Dentistry: Attitudes, Knowledge, Use, and Barriers Among Dentists in Croatia-A Survey-Based Study

AIM

This study aims to assess Croatian dentists' knowledge, attitudes, and use of artificial intelligence (AI) and modern technology, while also identifying perceived barriers to AI and modern technology adoption and evaluating the need for further education and training.

MATERIALS AND METHODS

A cross-sectional survey was conducted in February 2024 among general dentists in Croatia using a self-structured questionnaire. A total of 200 respondents filled out the questionnaire. It included five sections: socio-demographic and professional information, self-assessment of AI and modern technology use, knowledge of AI in dentistry, current innovations and devices used in practice, and barriers to AI and modern technology integration in practice. Data were analyzed using descriptive statistics and a regression analysis to explore relationships between socio-demographic factors and AI knowledge.

RESULTS

The mean knowledge of AI systems was 3.62 ± 2.56 out of a possible score of 7, indicating relatively poor knowledge, with 47.5% demonstrating knowledge below the median. Most respondents (76.0%) did not use AI systems and modern technology in practice; however, prosthodontics (13.0%) and oral surgery (10.0%) were identified as the primary fields utilizing these technologies. Respondents rated their knowledge of modern technologies and AI as weak or moderate, with 60.5% engaged in continuous education. Despite 76.0% not using AI daily, 71.0% believed that these technologies could enhance patient care. Participants interested in further training showed significantly better knowledge of AI applications (p = 0.030). Major barriers included acquisition and maintenance costs (59.0%) and financial constraints (58.0%).

CONCLUSIONS

The study revealed that most respondents had poor knowledge of AI systems. Despite this, there is a recognition of AI's and modern technology potential in dentistry, emphasizing the need for enhanced education and training in this field.

Artificial Intelligence and cancer: Profile of registered clinical trials

Artificial Intelligence (AI) has made significant strides due to advancements in processing algorithms and data availability. Recent years have shown a resurgence in AI, driven by breakthroughs in deep machine learning. AI has attracted particular interest in the medical sector, especially in the field of personalized medicine, which for example uses large-scale genomic and molecular data to predict individual patient treatment responses. The applications of AI in disease diagnosis, monitoring, and treatment are expanding rapidly, leading to a growing number of registered trials. Therefore, this study aimed to identify and evaluate clinical trials registered between January 1st 2016, and September 30th 2023 that connect AI and cancer. Our findings show that the number of clinical trials linking AI with cancer research has grown significantly compared to other diseases, with colorectal and breast tumour types showing the highest number of registered trials. The most frequent intervention was disease diagnosis and monitoring. Regarding countries, China and the United States hold the highest numbers of registered trials. In conclusion, oncology is a field with a great interest in AI, where the developed countries are leading the studies in this field. Unfortunately, developing countries are still crawling in this aspect and government policies should be made to improve that area.

The impact of artificial intelligence on the knowledge, attitude, and practice of pharmacists across diverse settings: A cross-sectional study

The pharmacy practice landscape is undergoing a significant transformation with the increasing integration of artificial intelligence (AI). As essential members of the healthcare team, pharmacists' readiness and willingness to adopt AI technologies is critical. This cross-sectional study explores pharmacists' knowledge, attitudes, and practices (KAP) regarding AI in various practice settings. Utilizing a descriptive survey methodology, we collected data through a structured questionnaire targeting pharmacists across diverse working environments. Statistical analyses were conducted to calculate KAP scores. Results revealed that 44.8 % of participants possessed a moderate level of knowledge about AI, while 49.1 % expressed positive attitudes toward its potential applications in pharmacy. However, their current practices related to AI were rated as adequate (57.3 %). Notably, a significant association was found between knowledge, attitudes, and practices (p < 0.001). This study provides valuable insights into pharmacists' readiness to incorporate AI into their practice, emphasizing the need for targeted educational interventions to enhance knowledge and promote positive attitudes. Furthermore, efforts must be directed towards facilitating the integration of AI into pharmacy workflows to fully leverage this transformative technology and improve patient care outcomes.

ChatGPT-4.0 vs. Google: Which Provides More Academic Answers to Patients' Questions on Arthroscopic Meniscus Repair?

Purpose The purpose of this study was to evaluate the ability of a Chat Generative Pre-trained Transformer (ChatGPT) to provide academic answers to frequently asked questions using a comparison with Google web search FAQs and answers. This study attempted to determine what patients ask on Google and ChatGPT and whether ChatGPT and Google provide factual information for patients about arthroscopic meniscus repair. Method A cleanly installed Google Chrome browser and ChatGPT were used to ensure no individual cookies, browsing history, other side data, or sponsored sites. The term "arthroscopic meniscus repair" was entered into the Google Chrome browser and ChatGPT. The first 15 frequently asked questions (FAQs), answers, and sources of answers to FAQs were identified from both ChatGPT and Google search engines. Results Timeline of recovery (20%) and technical details (20%) were the most commonly asked question categories of a total of 30 questions. Technical details and timeline of recovery questions were more commonly asked on ChatGPT compared to Google (technical detail: 33.3% vs. 6.6%, p=0.168; timeline of recovery: 26.6% vs. 13.3%, p=0.651). Answers to questions were more commonly from academic websites in website categories in ChatGPT compared to Google (93.3% vs. 20%, p=0.0001). The most common answers to frequently asked questions were academic (20%) and commercial (20%) in Google. Conclusion Compared to Google, ChatGPT provided significantly fewer references to commercial content and offered responses that were more aligned with academic sources. ChatGPT may be a valuable adjunct in patient education when used under physician supervision, ensuring information aligns with evidence-based practices.

Revolutionizing healthcare in KSA: A deep dive into clinical practice guideline development and implementation

KSA is transforming its healthcare system by developing and implementing Clinical Practice Guidelines (CPGs), a tool designed to improve patient outcomes, standardize care, and facilitate evidence-based decision-making. CPGs are crucial in addressing healthcare disparities, thereby promoting health equity and patient experience. They are integral to KSA's healthcare transformation agenda. The development process involves topic selection, evidence gathering, and guideline development and dissemination, with the National Centre for Evidence-Based Medicine (NCEBM) responsible for the appraisal and publication of quality CPGs. However, the current guideline landscape faces challenges such as conflicts of interest, lack of key stakeholder groups involved, methodological rigor, and inadequate monitoring as well as feedback loop. In this article, we discuss how we can overcome these issues. For instance, healthcare organizations can provide resources, integrate CPGs into electronic health records (EHRs), and create a culture that values evidence-based practice and continuous quality improvement. Technology plays a pivotal role in the successful implementation of CPGs, with EHRs, telemedicine, and artificial intelligence reshaping healthcare delivery across the globe. Future prospects for healthcare in KSA include the increasing use of technology, patient-centric care, and the adoption of digital healthcare technologies. By embracing innovation, fostering collaboration, and prioritizing patient-centric care, Saudi Arabi is paving the way for a new era of excellence in healthcare.

Clinical reasoning using ChatGPT: Is it beyond credibility for physiotherapists use?

BACKGROUND

Artificial Intelligence (AI) tools are gaining popularity in healthcare. OpenAI released ChatGPT on November 30, 2022. ChatGPT is a language model that comprehends and generates human language, providing instant data analysis and recommendations. This is particularly significant in the dynamic field of physiotherapy, where its integration has the potential to enhance healthcare efficiency.

OBJECTIVES

This study aims to evaluate whether ChatGPT-3.5 (free version) provides consistent and accurate clinical responses, its ability to imitate human clinical reasoning in simple and complex scenarios, and its capability to produce a differential diagnosis.

METHODS

Two studies were conducted using the ChatGPT-3.5. Study 1 evaluated the consistency and accuracy of ChatGPT's responses in clinical assessment using ten user-participants who submitted the phrase "Which are the main steps for a completed physiotherapy assessment?" Study 2 assessed ChatGPT's differential diagnostic ability using published case studies by 2 independent participants. The case reports consisted of one simple and one complex scenario.

RESULTS

Study 1 underscored the variability in ChatGPT's responses, which ranged from comprehensive to concise. Notably, essential steps such as re-assessment and subjective examination were omitted in 30% and 40% of the responses, respectively. In Study 2, ChatGPT demonstrated its capability to develop evidence-based clinical reasoning, particularly evident in simple clinical scenarios. Question phrasing significantly impacted the generated answers.

CONCLUSIONS

This study highlights the potential benefits of using ChatGPT in healthcare. It also provides a balanced perspective on ChatGPT's strengths and limitations and emphasizes the importance of using AI tools in a responsible and informed manner.

Perspectives of medical imaging professionals about the impact of AI on Swiss radiographers

INTRODUCTION

Artificial Intelligence (AI) is increasingly implemented in medical imaging practice, however, its impact on radiographers practice is not well studied. The aim of this study was to explore the perceived impact of AI on radiographers' activities and profession in Switzerland.

METHODS

A survey conducted in the UK, translated into French and German, was disseminated through professional bodies and social media. The participants were Swiss radiographers (clinical/educators/ researchers/students) and physicians working within the medical imaging profession (radiology/nuclear medicine/radiation-oncology). The survey covered five sections: demographics, AI-knowledge, skills, confidence, perceptions about the AI impact. Descriptive, association statistics and qualitative thematic analysis were conducted.

RESULTS

A total of 242 responses were collected (89% radiographers; 11% physicians). AI is being used by 43% of participants in clinical practice, but 64% of them did not feel confident with AI-terminology. Participants viewed AI as an opportunity (57%), while 19% considered it as a threat. The opportunities were associated with streamlining repetitive tasks, minimizing errors, increasing time towards patient-centered care, research, and patient safety. The significant threats identified were reduction on work positions (23%), decrease of the radiographers' expertise level due to automation bias (16%). Participants (68%) did not feel well trained/prepared to implement AI in their practice, highlighting the non-availability of specific training (88%). 93% of the participants mentioned that AI education should be included at undergraduate education program.

CONCLUSION

Although most participants perceive AI as an opportunity, this study identified areas for improvement including lack of knowledge, educational supports/training, and confidence in radiographers. Customised training needs to be implemented to improve clinical practice and understanding of how AI can benefit radiographers.

Non-invasive, fast, and high-performance EGFR gene mutation prediction method based on deep transfer learning and model stacking for patients with Non-Small Cell Lung Cancer

Purpose

To propose an intelligent, non-invasive, highly precise, and rapid method to predict the mutation status of the Epidermal Growth Factor Receptor (EGFR) to accelerate treatment with Tyrosine Kinase Inhibitor (TKI) for patients with untreated adenocarcinoma Non-Small Cell Lung Cancer.

Materials and methods

Real-world data from 521 patients with adenocarcinoma NSCLC who performed a CT scan and underwent surgery or pathological biopsy to determine EGFR gene mutation between January 2021 and July 2022, is collected. Solutions to the problems that prevent the model from achieving very high precision, namely: human errors made during the annotation of the database and the low precision of the output decision of the model, are proposed. Thus, among the 521 analyzed cases, only 40 were selected as patients with EGFR gene mutation and 98 cases with wild-type EGFR.

Results

The proposed model is trained, validated, and tested on 12,040 2D images extracted from the 138 CT scans images where patients were randomly partitioned into training (80 %) and test (20 %) sets. The performance obtained for EGFR gene mutation prediction was 95.22 % for accuracy, 960.2 for F1_score, 95.89 % for precision, 96.92 % for sensitivity, 94.01 % for Cohen kappa, and 98 % for AUC.

Conclusion

An EGFR gene mutation status prediction method, with high-performance thanks to an intelligent prediction model entrained by highly accurate annotated data is proposed. The outcome of this project will facilitate rapid decision-making when applying a TKI as an initial treatment.

Artificial intelligence and omics in malignant gliomas

Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemotherapy and radiotherapy, the prognosis remains unfavorable. The heterogeneity of GBM and the presence of the blood-brain barrier further complicate the therapeutic process. It is crucial to adopt a multifaceted approach in GBM research to understand its biology and advance toward effective treatments. In particular, omics research, which primarily includes genomics, transcriptomics, proteomics, and epigenomics, helps us understand how GBM develops, finds biomarkers, and discovers new therapeutic targets. The availability of large-scale multiomics data requires the development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large omics databases. Although the application of AI tools in GBM-omics is currently in its early stages, a thorough exploration of AI utilization to uncover different aspects of GBM (subtype classification, prognosis, and survival) would have a significant impact on both researchers and clinicians. Here, we aim to review and provide database resources of different AI-based techniques that have been used to study GBM pathogenesis using multiomics data over the past decade. We summarize different types of GBM-related omics resources that can be used to develop AI models. Furthermore, we explore various AI tools that have been developed using either individual or integrated multiomics data, highlighting their applications and limitations in the context of advancing GBM research and treatment.

End user experience of a widely used artificial intelligence based sepsis system

Objectives

Research on the Epic Sepsis System (ESS) has predominantly focused on technical accuracy, neglecting the user experience of healthcare professionals. Understanding these experiences is crucial for the design of Artificial Intelligence (AI) systems in clinical settings. This study aims to explore the socio-technical dynamics affecting ESS adoption and use, based on user perceptions and experiences.

Materials and Methods

Resident doctors and nurses with recent ESS interaction were interviewed using purposive sampling until data saturation. A content analysis was conducted using Dedoose software, with codes generated from Sittig and Singh's and Salwei and Carayon's frameworks, supplemented by inductive coding for emerging themes.

Results

Interviews with 10 healthcare providers revealed mixed but generally positive or neutral perceptions of the ESS. Key discussion points included its workflow integration and usability. Findings were organized into 2 main domains: workflow fit, and usability and utility, highlighting the system's seamless electronic health record integration and identifying design gaps.

Discussion

This study offers insights into clinicians' experiences with the ESS, emphasizing the socio-technical factors that influence its adoption and effective use. The positive reception was tempered by identified design issues, with clinician perceptions varying by their professional experience and frequency of ESS interaction.

Conclusion

The findings highlight the need for ongoing ESS refinement, emphasizing a balance between technological advancement and clinical practicality. This research contributes to the understanding of AI system adoption in healthcare, suggesting improvements for future clinical AI tools.

How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

AI in Surgery: Navigating Trends and Managerial Implications Through Bibliometric and Text Mining Odyssey

Background: This research employs bibliometric and text-mining analysis to explore artificial intelligence (AI) advancements within surgical procedures. The growing significance of AI in healthcare underscores the need for healthcare managers to prioritize investments in this technology. Purpose: To assess the increasing impact of AI on surgical practices through a comprehensive analysis of scientific literature, providing insights that can guide managerial decision-making in adopting AI solutions.Research Design: The study analyzes over 6000 scientific articles published since 1990 to evaluate trends and contributions in the field, informing managers about the current landscape of AI in surgery.Study Sample: The research focuses on publications from various influential publishers across North America, Northern Asia, and Eastern & Western Europe, highlighting key markets for AI implementation in surgical settings.Data Collection and Analysis: A bibliometric approach was utilized to identify key contributors and influential journals. At the same time, text-mining techniques highlighted significant keywords related to AI in surgery, aiding managers in recognizing essential areas for further exploration and investment.Results: The year 2022 marked a significant upsurge in publications, indicating widespread AI integration in healthcare. The U.S. emerged as the foremost contributor, followed by China, the UK, Germany, Italy, the Netherlands, and India. Key journals, such as Annals of Surgery and Spine Journal, play a crucial role in disseminating research findings, serving as valuable resources for managers seeking to stay informed.Conclusions: The findings underscore AI's pivotal role in enhancing diagnostic precision, predicting treatment outcomes, and improving operational efficiency in surgical practices. This progress represents a significant milestone in modern medical science, paving the way for intelligent healthcare solutions and further advancements in the field. Healthcare managers should leverage these insights to foster innovation and improve patient care standards.

Integrating Catholic Social Teaching with AI Ethics to Address Inequity in AI Healthcare

Artificial intelligence (AI) in healthcare can potentially improve patient outcomes, operational efficiency, and diagnostic accuracy. However, it also raises serious ethical issues, especially in light of possible disparities in the distribution and accessibility of AI-powered healthcare resources. This study investigates how AI might affect health disparities. It bases its proposal for an equitable AI implementation framework on the justice teachings of the Catholic Church. In line with the Church's ethical commitment to social justice, the paper makes an ethical case for a responsible approach to AI in healthcare by examining the concepts of human dignity, the common good, and preferential option for the poor.

The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis

PURPOSE

To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.

METHODS

Data from 1539 adult chronic rhinosinusitis (CRS) patients who underwent paranasal sinus computed tomography (CT) were collected. The overall dataset consisted of 254 MFB cases and 1285 non-MFB cases. The CT images were constructed and labeled to form the deep learning models. Seventy percent of the images were used for training the deep-learning models, and 30% were used for testing. Whole image analysis and instance segmentation analysis were performed using three different architectures: MobileNetv3, ResNet50, and ResNet101 for whole image analysis, and YOLOv5X-SEG, YOLOv8X-SEG, and YOLOv9-C-SEG for instance segmentation analysis. The ROC curve was assessed. Accuracy, sensitivity (recall), specificity, precision, and F1-score were compared between the models and a rhinologist. Kappa agreement was evaluated.

RESULTS

Whole image analysis showed lower precision, recall, and F1-score compared to instance segmentation. The models exhibited an area under the ROC curve of 0.86 for whole image analysis and 0.88 for instance segmentation. In the testing dataset for whole images, the MobileNet V3 model showed 81.00% accuracy, 47.40% sensitivity, 87.90% specificity, 66.80% precision, and a 67.20% F1 score. Instance segmentation yielded the best evaluation with YOLOv8X-SEG showing 94.10% accuracy, 85.90% sensitivity, 95.80% specificity, 88.90% precision, and an 89.80% F1-score. The rhinologist achieved 93.5% accuracy, 84.6% sensitivity, 95.3% specificity, 78.6% precision, and an 81.5% F1-score.

CONCLUSION

Utilizing paranasal sinus CT imaging with enhanced localization and constructive instance segmentation in deep learning models can be the practical promising deep learning system in assisting physicians for diagnosing maxillary fungal ball.

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

BACKGROUND/OBJECTIVES

Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

METHODS

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

RESULTS

Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).

CONCLUSION

Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Exploring the Ethical Implications of ChatGPT in Medical Education: Privacy, Accuracy, and Professional Integrity in a Cross-Sectional Survey

BACKGROUND

The inclusion of artificial intelligence in medical education, specifically through the use of ChatGPT (OpenAI, San Francisco, CA), has transformed learning and generated many ethical questions. This study aims to analyze the medical students' ethical concerns about using ChatGPT in medical education, focusing on privacy, accuracy, and professional integrity.

METHODS

The study format was a cross-sectional survey distributed to 219 medical students at ABWA Medical College, Pakistan. A pre-validated, pre-structured questionnaire was created with Google Forms, including questions regarding the accuracy of ChatGPT, confidentiality, and its impact on the students' critical thinking faculties. This information was collected after obtaining informed consent. The data collected were descriptively and inferentially analyzed using SPSS software version 26.0 (IBM Corp., Armonk, NY).

RESULTS

In total, 95% of respondents (n = 190) confirmed that the information provided by ChatGPT was accurate, and 80% (n = 160) stated that they trusted the medical information from the tool. However, 83% (n = 166 students) indicated concerns about privacy and data security. While 69% of participants (n = 138) discovered that ChatGPT supplemented their critical thinking skills, the rest (31%; n = 62) believed it led to decreased autonomy over time. However, since health science-related courses often involve sensitive patient information, 22% (n = 44) of students raised concerns about using ChatGPT in future medical education due to the potential issues with privacy and the risk of inaccuracies in recorded information.

CONCLUSION

ChatGPT offers promising educational benefits in medical training but raises significant ethical concerns, particularly regarding data privacy and the potential for over-reliance. The results suggest the need for responsible integration of AI in medical education, ensuring it supplements rather than replaces traditional learning methods.

Artificial intelligence in community pharmacy practice: Pharmacists' perceptions, willingness to utilize, and barriers to implementation

Background

Artificial intelligence (AI) has a significant potential to impact pharmacy practices worldwide. This study investigates pharmacists' perceptions of AI's role in pharmacy practices, their willingness to adopt it, and perceived barriers to its implementation at community pharmacies in Ethiopia.

Methods

A cross-sectional study was conducted among community pharmacists in Ethiopia. Data were collected using a self-administered questionnaire. Independent samples t-test, one-way ANOVA, and post-hoc analyses were used to compare pharmacists' perception and willingness scores. A linear regression analysis examined the association of independent variables with pharmacists' perception of AI and willingness to utilize AI. A p-value <0.05 was considered statistically significant.

Results

Of 241 pharmacists approached, 225 (93.3 %) completed the survey. Overall, about two-thirds (67.1 % and 66.2 %) of community pharmacists had a high level of perception and willingness to use AI applications in pharmacy, respectively. Pharmacists with bachelor's degrees and above (β = 2.76: 95 % CI: 0.09, 5.01 vs. β = 1.79: 95 % CI: 0.05, 4.21), those who utilized scientific drug information sources (β = 2.45, 95 %: 0.17, 4.45 vs. β = 1.76, 95 % CI: 0.91, 3.89), pharmacists who had a previous exposure of AI (β = 1.02, 95 %: 0.03, 3.24 vs. β =1.13, 95 % CI: 0.07, 2.93), and those who with higher perceived AI knowledge (β =1.09, 95 % CI: 0.02, 2.46 vs. β = 1.14, 95 %CI: 0.17, 3.11) had significantly higher perception of AI and willingness to utilize it, respectively compared to their counterparts. Lack of internet availability (89.3 %), lack of AI-related software/hardware (88.2 %), and limited training (80.9 %) were the most frequently reported barriers by pharmacists to AI adoption. Over 90 % of pharmacists agreed on the importance of internet availability (93.3 %), policies/frameworks (91.6 %), and research/learning from others (89.3 %) for successful AI integration.

Conclusion

Despite positive perceptions and willingness from pharmacists, AI implementation in community pharmacies could be hindered by resource limitations, training gaps, skill constraints, and infrastructure issues. To facilitate adoption, enhancing knowledge and skills, and developing policies/frameworks are crucial.

Development of Machine Learning Models for Predicting the 1-Year Risk of Reoperation After Lower Limb Oncological Resection and Endoprosthetic Reconstruction Based on Data From the PARITY Trial

BACKGROUND

Oncological resection and reconstruction involving the lower extremities commonly lead to reoperations that impact patient outcomes and healthcare resources. This study aimed to develop a machine learning (ML) model to predict this reoperation risk.

METHODS

This study was conducted according to TRIPOD + AI. Data from the PARITY trial was used to develop ML models to predict the 1-year reoperation risk following lower extremity oncological resection and reconstruction. Six ML algorithms were tuned and calibrated based on fivefold cross-validation. The best-performing model was identified using classification and calibration metrics.

RESULTS

The polynomial support vector machine (SVM) model was chosen as the best-performing model. During internal validation, the SVM exhibited an AUC-ROC of 0.73 and a Brier score of 0.17. Using an optimal threshold that balances all quadrants of the confusion matrix, the SVM exhibited a sensitivity of 0.45 and a specificity of 0.81. Using a high-sensitivity threshold, the SVM exhibited a sensitivity of 0.68 and a specificity of 0.68. Total operative time was the most important feature for reoperation risk prediction.

CONCLUSION

The models may facilitate reoperation risk stratification, allowing for better patient counseling and for physicians to implement measures that reduce surgical risks.

Barriers impeding research data sharing on chronic disease prevention among the older adults in low-and middle-income countries: a systematic review

Introduction

Chronic diseases, including cardiovascular disease, diabetes, cancer, and chronic respiratory diseases, are a growing public health concern in low-and middle-income countries (LMICs) among the older population. The current review aimed to identify the main barriers that impede researchers from sharing research data on the prevention of chronic diseases in older adults living in LMICs). The review included both older women and men from these countries.

Methods

Studies were selected from 11 databases, including Web of Science, Scopus, PubMed, Taylor and Francis, Biomedical Central, BioOne, CINAHL, EBSCOHost, ScienceDirect, Wiley Online, and Google Scholar, were then transferred to CADIMA, an online tool for screening purposes, and a total of 1,305,316 studies were identified through a robust search strategy. CADIMA also ensured the quality of all studies in this review. The sampling techniques were performed by selecting and screening studies per this review's eligibility criteria. Ultimately, 13 studies were found to meet these criteria. A PRISMA flow chart was used to map out the number of studies that were identified, included, and excluded.

Results

Five main barriers were consistently highlighted, including a lack of necessary resources (9, 69%), dealing with complex and sensitive research data (2,15%), lack of policies, procedures, guidelines (5,38%), medical big data processing and integration (2,15%), and inadequate ethical considerations, legal compliance, and privacy protection (6,46%). Discussion: By shedding light on these obstacles, researchers can develop strategies to overcome the identified barriers and address areas requiring further investigation. The registration details of this review can be found under PROSPERO 2023 CRD42023437385, underscoring the importance of this review in advancing our collective understanding of chronic disease prevention among older adults worldwide.

Systematic review registration

PROSPERO, identifier CRD42023437385, available at: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023437385.