Artificial intelligence in healthcare: An essential guide for health leaders

The moderating role of ethical awareness in the relationship between nurses' artificial intelligence perceptions, attitudes, and innovative work behavior: a cross-sectional study

BACKGROUND

Artificial intelligence is rapidly advancing and being integrated into healthcare, potentially revolutionizing patient care and improving outcomes by leveraging large datasets and complex algorithms.

AIM

Investigate the moderating role of ethical awareness between nurses' artificial intelligence perceptions, attitudes, and innovative work behaviors.

DESIGN AND METHODS

A cross-sectional descriptive correlational design adhering to STROBE guidelines. A non-probability convenience sample of 415 Alexandria Main University Hospital nurses was analyzed. Statistical methods included one-way ANOVA, the student t-test, and the Pearson coefficient, with results evaluated for significance at the 5% level and internal consistency assessed via Cronbach's α. Linear regression assessed the predicting and moderating effect between ethical awareness, nurses' artificial intelligence perceptions, attitudes, and innovative work behavior. The perceptions of using the Artificial Intelligence Scale, general attitudes towards the Artificial Intelligence Scale, ethical awareness of Using Artificial Intelligence, and the Employee Innovative Behavior Scale were used to respond to the research aim.

RESULTS

The study revealed that perception of AI use among nurses has a mean score of 50.25 (SD = 3.49), attitudes towards AI have a mean score of 71.40 (SD = 4.98), ethical awareness regarding AI use shows a mean score of 43.85 (SD = 3.39), and nurses innovative behavior exhibits a mean score of 83.63 (SD = 5.22). Attitude and ethical awareness were statistically significant predictors of innovation. Specifically, for every one-unit increase in attitude, innovative work behaviors increase by 1.796 units (p = 0.001), and for every one-unit increase in ethical awareness, innovative work behaviors increase by 2.567 units (p = 0.013). The interaction effects between perception, ethical awareness, attitude, and ethical awareness were also examined. Only the interaction between attitude and ethical awareness was found to be significant (p = 0.002), suggesting that the effect of attitude on innovative work behaviors depends on the level of ethical awareness. In other words, ethical awareness moderates the relationship between attitudes and innovative work behaviors rather than perception and innovation.

CONCLUSION

There is a statistically significant correlation between attitude, ethical awareness, and creativity, highlighting that ethical awareness moderates the relationship between attitudes and innovative work behaviors. These findings emphasize the importance of ethical awareness in fostering positive attitudes towards AI and enhancing innovative practices in nursing, ultimately contributing to nurses' well-being.

[Supporting medical and nursing activities with AI: recommendations for responsible design and use]

Clinical decision support systems (CDSS) based on artificial intelligence (AI) are complex socio-technical innovations and are increasingly being used in medicine and nursing to improve the overall quality and efficiency of care, while also addressing limited financial and human resources. However, in addition to such intended clinical and organisational effects, far-reaching ethical, social and legal implications of AI-based CDSS on patient care and nursing are to be expected. To date, these normative-social implications have not been sufficiently investigated. The BMBF-funded project DESIREE (DEcision Support In Routine and Emergency HEalth Care: Ethical and Social Implications) has developed recommendations for the responsible design and use of clinical decision support systems. This article focuses primarily on ethical and social aspects of AI-based CDSS that could have a negative impact on patient health. Our recommendations are intended as additions to existing recommendations and are divided into the following action fields with relevance across all stakeholder groups: development, clinical use, information and consent, education and training, and (accompanying) research.

The Paradoxes of Digital Tools in Hospitals: Qualitative Interview Study

BACKGROUND

Digital tools are progressively reshaping the daily work of health care professionals (HCPs) in hospitals. While this transformation holds substantial promise, it leads to frustrating experiences, raising concerns about negative impacts on clinicians' well-being.

OBJECTIVE

The goal of this study was to comprehensively explore the lived experiences of HCPs navigating digital tools throughout their daily routines.

METHODS

Qualitative in-depth interviews with 52 HCPs representing 24 medical specialties across 14 hospitals in Switzerland were performed.

RESULTS

Inductive thematic analysis revealed 4 main themes: digital tool use, workflow and processes, HCPs' experience of care delivery, and digital transformation and management of change. Within these themes, 6 intriguing paradoxes emerged, and we hypothesized that these paradoxes might partly explain the persistence of the challenges facing hospital digitalization: the promise of efficiency and the reality of inefficiency, the shift from face to face to interface, juggling frustration and dedication, the illusion of information access and trust, the complexity and intersection of workflows and care paths, and the opportunities and challenges of shadow IT.

CONCLUSIONS

Our study highlights the central importance of acknowledging and considering the experiences of HCPs to support the transformation of health care technology and to avoid or mitigate any potential negative experiences that might arise from digitalization. The viewpoints of HCPs add relevant insights into long-standing informatics problems in health care and may suggest new strategies to follow when tackling future challenges.

The impact of AI-based decision support systems on nursing workflows in critical care units

AIM

This research examines the effects of artificial intelligence (AI)-based decision support systems (DSS) on the operational processes of nurses in critical care units (CCU) located in Amman, Jordan.

BACKGROUND

The deployment of AI technology within the healthcare sector presents substantial opportunities for transforming patient care, with a particular emphasis on the field of nursing.

METHOD

This paper examines how AI-based DSS affect CCU nursing workflows in Amman, Jordan, using a cross-sectional analysis. A study group of 112 registered nurses was enlisted throughout a research period spanning one month. Data were gathered using surveys that specifically examined several facets of nursing workflows, the employment of AI, encountered problems, and the sufficiency of training.

RESULT

The findings indicate a varied demographic composition among the participants, with notable instances of AI technology adoption being reported. Nurses have the perception that there are favorable effects on time management, patient monitoring, and clinical decision-making. However, they continue to face persistent hurdles, including insufficient training, concerns regarding data privacy, and technical difficulties.

DISCUSSION

The study highlights the significance of thorough training programs and supportive mechanisms to improve nurses' involvement with AI technologies and maximize their use in critical care environments. Although there are differing degrees of contentment with existing AI systems, there is a general agreement on the necessity of ongoing enhancement and fine-tuning to optimize their efficacy in enhancing patient care results.

CONCLUSION AND IMPLICATIONS FOR NURSING AND/OR HEALTH POLICY

This research provides essential knowledge about the intricacies of incorporating AI into nursing practice, highlighting the significance of tackling obstacles to guarantee the ethical and efficient use of AI technology in healthcare.

Mobile Diagnostic Clinics

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

Advancing healthcare: the role and impact of AI and foundation models

BACKGROUND

The integration of artificial intelligence (AI) into the healthcare domain is a monumental shift with profound implications for diagnostics, medical interventions, and the overall structure of healthcare systems.

PURPOSE

This study explores the transformative journey of foundation AI models in healthcare, shedding light on the challenges, ethical considerations, and vast potential they hold for improving patient outcome and system efficiency. Notably, in this investigation we observe a relatively slow adoption of AI within the public sector of healthcare. The evolution of AI in healthcare is un-paralleled, especially its prowess in revolutionizing diagnostic processes.

RESULTS

This research showcases how these foundational models can unravel hidden patterns within complex medical datasets. The impact of AI reverberates through medical interventions, encompassing pathology, imaging, genomics, and personalized healthcare, positioning AI as a cornerstone in the quest for precision medicine. The paper delves into the applications of generative AI models in critical facets of healthcare, including decision support, medical imaging, and the prediction of protein structures. The study meticulously evaluates various AI models, such as transfer learning, RNN, autoencoders, and their roles in the healthcare landscape. A pioneering concept introduced in this exploration is that of General Medical AI (GMAI), advocating for the development of reusable and flexible AI models.

CONCLUSION

The review article discusses how AI can revolutionize healthcare by stressing the significance of transparency, fairness and accountability, in AI applications regarding patient data privacy and biases. By tackling these issues and suggesting a governance structure the article adds to the conversation about AI integration in healthcare environments.

Artificial intelligence applied to laparoscopic cholecystectomy: what is the next step? A narrative review

Artificial Intelligence (AI) is playing an increasing role in several fields of medicine. AI is also used during laparoscopic cholecystectomy (LC) surgeries. In the literature, there is no review that groups together the various fields of application of AI applied to LC. The aim of this review is to describe the use of AI in these contexts. We performed a narrative literature review by searching PubMed, Web of Science, Scopus and Embase for all studies on AI applied to LC, published from January 01, 2010, to December 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly improve surgical efficiency and accuracy during LC. Future prospects include speeding up, implementing, and improving the automaticity with which AI recognizes, differentiates and classifies the phases of the surgical intervention and the anatomic structures that are safe and those at risk.

Artificial intelligence in healthcare: an Italian perspective on ethical and medico-legal implications

Artificial intelligence (AI) is a multidisciplinary field intersecting computer science, cognitive science, and other disciplines, able to address the creation of systems that perform tasks generally requiring human intelligence. It consists of algorithms and computational methods that allow machines to learn from data, make decisions, and perform complex tasks, aiming to develop an intelligent system that can work independently or collaboratively with humans. Since AI technologies may help physicians in life-threatening disease prevention and diagnosis and make treatment smart and more targeted, they are spreading in health services. Indeed, humans and machines have unique strengths and weaknesses and can complement each other in providing and optimizing healthcare. However, the healthcare implementation of these technologies is related to emerging ethical and deontological issues regarding the fearsome reduction of doctors' decision-making autonomy and acting discretion, generally strongly conditioned by cognitive elements concerning the specific clinical case. Moreover, this new operational dimension also modifies the usual allocation system of responsibilities in case of adverse events due to healthcare malpractice, thus probably imposing a redefinition of the established medico-legal assessment criteria of medical professional liability. This article outlines the new challenges arising from AI healthcare integration and the possible ways to overcome them, with a focus on Italian legal framework. In this evolving and transitional context emerges the need to balance the human dimension with the artificial one, without mutual exclusion, for a new concept of medicine "with" machines and not "of" machines.

Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre

BACKGROUND

Artificial intelligence (AI) technologies are expected to "revolutionise" healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital.

METHODS

Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework.

RESULTS

Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients' digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors' priorities and the needs and expectations of healthcare organisations and systems.

CONCLUSION

Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

Externally validated treatment algorithm acceptably predicts nonoperative treatment success in patients with anterior cruciate ligament rupture

PURPOSE

This study aims to develop and externally validate a treatment algorithm to predict nonoperative treatment success or failure in patients with anterior cruciate ligament (ACL) rupture.

METHODS

Data were used from two completed studies of adult patients with ACL ruptures: the Conservative versus Operative Methods for Patients with ACL Rupture Evaluation study (development cohort) and the KNee osteoArthritis anterior cruciate Ligament Lesion study (validation cohort). The primary outcome variable is nonoperative treatment success or failure. Potential predictor variables were collected, entered into the univariable logistic regression model and then incorporated into the multivariable logistic regression model for constructing the treatment algorithm. Finally, predictive performance and goodness-of-fit were assessed and externally validated by discrimination and calibration measures.

RESULTS

In the univariable logistic regression model, a stable knee measured with the pivot shift test and a posttrauma International Knee Documentation Committee (IKDC) score <50 were predictive of needing an ACL reconstruction. Age >30 years and a body mass index > 30 kg/m2 were predictive for not needing an ACL reconstruction. Age, pretrauma Tegner score, the outcome of the pivot shift test and the posttrauma IKDC score are entered into the treatment algorithm. The predictability of needing an ACL reconstruction after nonoperative treatment (discrimination) is acceptable in both the development and the validation cohort: area under the curve = resp. 0.69 (95% confidence interval [CI]: 0.58-0.81) and 0.68 (95% CI: 0.58-0.78).

CONCLUSION

This study shows that the treatment algorithm can acceptably predict whether an ACL injury patient will have a(n) (un)successful nonoperative treatment (discrimination). Calibration of the treatment algorithm suggests a systematical underestimation of the need for ACL reconstruction. Given the limitations regarding the sample size of this study, larger data sets must be constructed to improve the treatment algorithm further.

LEVEL OF EVIDENCE

Level II.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

Artificial Intelligence-Enabled Stool Analysis for Lactulose Titration Assistance in Hepatic Encephalopathy Through a Smartphone Application

INTRODUCTION

Management of hepatic encephalopathy relies on self-titration of lactulose. In this feasibility trial, we assess an artificial intelligence-enabled tool to guide lactulose use through a smartphone application.

METHODS

Subjects with hepatic encephalopathy on lactulose captured bowel movement pictures during lead-in and intervention phases. During the intervention phase, daily feedback on lactulose titration was delivered through the application. Goals were determined according to number of bowel movement and Bristol Stool Scale reports.

RESULTS

Subjects completed the study with more than 80% satisfaction. In the lead-in phase, less compliant subjects achieved Bristol Stool Scale goal on 62/111 (56%) of days compared with 107/136 (79%) in the intervention phase ( P = 0.041), while the most compliant subjects showed no difference. Severe/recurrent hepatic encephalopathy group achieved Bristol Stool Scale goal on 80/104 (77%) days in the lead-in phase and 90/110 (82%) days in the intervention phase ( P = NS), compared with 89/143 (62%) days and 86/127 (68%) days in the stable group.

DISCUSSION

Dieta application is a promising tool for objective Bowel Movement/Bristol Stool Scale tracking for hepatic encephalopathy and may potentially be used to assist with lactulose titration.

Radiologists' perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study

In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research focuses on the technical features of AI rather than its real-world clinical implementation. To evaluate the implementation process of an AI-based computer-aided detection system (AI-CAD) for prostate MRI readings, we interviewed German radiologists in a pre-post design. We embedded our findings in the Model of Workflow Integration and the Technology Acceptance Model to analyze workflow effects, facilitators, and barriers. The most prominent barriers were: (i) a time delay in the work process, (ii) additional work steps to be taken, and (iii) an unstable performance of the AI-CAD. Most frequently named facilitators were (i) good self-organization, and (ii) good usability of the software. Our results underline the importance of a holistic approach to AI implementation considering the sociotechnical work system and provide valuable insights into key factors of the successful adoption of AI technologies in work systems.

Prediction of coronary artery bypass graft outcomes using a single surgical note: An artificial intelligence-based prediction model study

BACKGROUND

Healthcare providers currently calculate risk of the composite outcome of morbidity or mortality associated with a coronary artery bypass grafting (CABG) surgery through manual input of variables into a logistic regression-based risk calculator. This study indicates that automated artificial intelligence (AI)-based techniques can instead calculate risk. Specifically, we present novel numerical embedding techniques that enable NLP (natural language processing) models to achieve higher performance than the risk calculator using a single preoperative surgical note.

METHODS

The most recent preoperative surgical consult notes of 1,738 patients who received an isolated CABG from July 1, 2014 to November 1, 2022 at a single institution were analyzed. The primary outcome was the Society of Thoracic Surgeons defined composite outcome of morbidity or mortality (MM). We tested three numerical-embedding techniques on the widely used TextCNN classification model: 1a) Basic embedding, treat numbers as word tokens; 1b) Basic embedding with a dataloader that Replaces out-of-context (ROOC) numbers with a tag, where context is defined as within a number of tokens of specified keywords; 2) ScaleNum, an embedding technique that scales in-context numbers via a learned sigmoid-linear-log function; and 3) AttnToNum, a ScaleNum-derivative that updates the ScaleNum embeddings via multi-headed attention applied to local context. Predictive performance was measured via area under the receiver operating characteristic curve (AUC) on holdout sets from 10 random-split experiments. For eXplainable-AI (X-AI), we calculate SHapley Additive exPlanation (SHAP) values at an ngram resolution (SHAP-N). While the analyses focus on TextCNN, we execute an analogous performance pipeline with a long short-term memory (LSTM) model to test if the numerical embedding advantage is robust to model architecture.

RESULTS

A total of 567 (32.6%) patients had MM following CABG. The embedding performances are as follows with the TextCNN architecture: 1a) Basic, mean AUC 0.788 [95% CI (confidence interval): 0.768-0.809]; 1b) ROOC, 0.801 [CI: 0.788-0.815]; 2) ScaleNum, 0.808 [CI: 0.785-0.821]; and 3) AttnToNum, 0.821 [CI: 0.806-0.834]. The LSTM architecture produced a similar trend. Permutation tests indicate that AttnToNum outperforms the other embedding techniques, though not statistically significant verse ScaleNum (p-value of .07). SHAP-N analyses indicate that the model learns to associate low blood urine nitrate (BUN) and creatinine values with survival. A correlation analysis of the attention-updated numerical embeddings indicates that AttnToNum learns to incorporate both number magnitude and local context to derive semantic similarities.

CONCLUSION

This research presents both quantitative and clinical novel contributions. Quantitatively, we contribute two new embedding techniques: AttnToNum and ScaleNum. Both can embed strictly positive and bounded numerical values, and both surpass basic embeddings in predictive performance. The results suggest AttnToNum outperforms ScaleNum. With regards to clinical research, we show that AI methods can predict outcomes after CABG using a single preoperative note at a performance that matches or surpasses the current risk calculator. These findings reveal the potential role of NLP in automated registry reporting and quality improvement.

Assessment of the relationship between executive Nurses' leadership Self-Efficacy and medical artificial intelligence readiness

AIMS

This study aims to assess the relationship between management nurses' leadership self-efficacy and medical artificial intelligence readiness.

METHODS

The research was conducted using a descriptive-correlational design. The sample of the study consisted of 196 management nurses working in public, private, and educational research hospitals in Gaziantep, Turkey. The data collection tools included the Personal Information Form, the Leadership Self-Efficacy Scale, and the Medical Artificial Intelligence Readiness Scale.

RESULTS

The majority of the participants in the research were female (71.4 %), married (80.1 %) and graduates of a bachelor's or higher degree in nursing (74.5 %), had 16 years or more of work experience in the profession (39.3 %), and worked during the day shift (75.5 %). Among the participating management nurses, those who were single had a significantly higher mean score in the cognition subscale and the total score of medical artificial intelligence readiness (p < 0.05). The management nurses working in shifts had significantly higher mean scores in the cognition and ability subscales, as well as the total score of medical artificial intelligence readiness (p < 0.05). The management nurses who received leadership/management-related training after their undergraduate education had a significantly higher mean score in the cognition subscale (p < 0.05). Furthermore, there was a significant relationship (p < 0.05) between leadership self-efficacy, medical artificial intelligence readiness, and their subscales, concerning following and finding artificial intelligence applications useful, as well as informing team members about artificial intelligence applications.

CONCLUSIONS

In the research, it was determined that the leadership self-efficacy of the manager nurses was at a good level and that their artificial intelligence readiness was at a medium level in terms of cognition, skill, foresight and ethics while presenting their professional knowledge. A positive and significant relationship was found between leadership self-efficacy and medical artificial intelligence readiness.

Artificial intelligence based on falling in older people: A bibliometric analysis

Objectives

This study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective.

Methods

The Web of Science database was searched for titles of English-language articles containing the words "artificial intelligence," "deep learning," "machine learning," "natural language processing,", "neural artificial network," "fall," "geriatric," "elderly," "aging," "older," and "old age." An R-based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis.

Results

Thirty-seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was "Engineering Electrical Electronic" with seven articles. The most productive country was the United States, followed by China. The most common words were "injuries," "people," and "risk factors."

Conclusion

Publications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed.

Knowledge, Awareness and Practice of Artificial Intelligence and Types of Realities Among Healthcare Professionals: A Nationwide Survey From Pakistan

Background Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, enabling them to perform tasks. The advancements in AI have also improved virtual reality (VR), augmented reality (AR) and mixed reality (MR) experience allowing a greater opportunity for use in the field of medicine. Objective To evaluate the knowledge, attitude and practice of AI and types of realities among Pakistani healthcare professionals (HCPs). Materials and methods This was a prospective, nationwide study designed at the Department of Neurosurgery at Punjab Institute of Neurosciences (PINS), Lahore, was conducted between January 2024 to February 2024. More than 500 HCPs were approached, out of which 176 participated in this survey consensually. A pre-formed general questionnaire based on knowledge, attitude and practices of AI and types of realities was modified according to local conditions. Google Forms (Google Inc., USA) was used to conduct the one-time sign up response. Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, Version 24, USA) was used to analyze submitted responses. Results About 69.9% respondents were male HCPs. Most of the respondents were from the fields of neurosurgery, medicine and general surgery, i.e., 10.80%, 10.20% and 4%, respectively. More than 90% HCPs used Internet and electronic devices daily. A majority of 62.50% respondents agreed that AI brings benefits for the patients, while at the same time, 45.50% agreed that they would not trust the assessment of AI more than that of HCPs. 61% HCPs feared that AI-based systems could be manipulated from the outside sources, like terrorists and hackers. Although 90% respondents knew the definition of AR and VR, a strikingly low 40% respondents could only identify the practical applications of these realities when asked in a mini-quiz. About 61.40% HCPs never used any AI-based application throughout their clinical practice, but Google Health was used by 29.50% respondents, followed by Remote Patient Monitoring AI application used by 3.4% individuals. Conclusion There is an evident under-utilization of AI and types of realities in clinical practice in Pakistan. Lack of awareness, paucity of resources and conventional clinical practices are the key reasons identified. Pakistan is on the path towards the point where the developed world is currently. There is a potential to move past the initial stages of AI implementation and into more advanced modes of adopting AI and types of realities.

Artificial intelligence in paediatric endocrinology: conflict or cooperation

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.

Transforming health care systems towards high-performance organizations: qualitative study based on learning from COVID-19 pandemic in the Basque Country (Spain)

BACKGROUND

The COVID-19 pandemic is one of the worst health catastrophes of the last century, which caused severe economic, political, and social consequences worldwide. Despite these devastating consequences, lessons learned provide a great opportunity that can drive the reform of health systems to become high-performing, effective, equitable, accessible, and sustainable organisations. This work identifies areas in which changes must be encouraged that will enable health systems to deal effectively with current and future challenges, beyond COVID-19.

METHODS

A realist design was chosen, based on qualitative data collection techniques, content analysis and triangulation to identify key domains of organizational interventions behind the changes implemented to react to the COVID-19 pandemic in the Basque Country. Twenty key informants were used as an expert source of information. Thematic analysis was done using the Framework Method.

RESULTS

The analysis of the interviews resulted in the identification of 116 codes, which were reviewed and agreed upon by the researchers. Following the process of methodological analysis, these codes were grouped into domains: seven themes and 23 sub-themes. Specifically, the themes are: responsiveness, telehealth, integration, knowledge management, professional roles, digitisation, and organisational communication. The detailed description of each theme and subtheme is presented.

CONCLUSIONS

The findings of this work pretend to guide the transformation of health systems into organisations that can improve the health of their populations and provide high quality care. Such a multidimensional and comprehensive reform encompasses both strategic and operational actions in diverse areas and requires a broad and sustained political, technical, and financial commitment.

Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review

Recent progress in the realm of artificial intelligence has shown effectiveness in various industries, particularly within the healthcare sector. However, there are limited insights on existing studies regarding ethical, social, privacy, and technological aspects of AI in the health sector, which is the gap our study aims to fill. This study aimed to synthesize empirical studies on the challenges and opportunities of using AI by conducting a systematic review. We reviewed 33 articles published between 2015 and 2022 in the PubMed, IEEE Xplore, and Science Direct databases. The results show that artificial intelligence has the promise of improving health care and faces obstacles when implemented. Most of the reviewed studies indicated that the use of AI provides several opportunities, including teamwork and decision-making, technological advancement, diagnosis and patient monitoring, drug development, and virtual health assistance. However, the findings show that the use of AI in the health sector hinders multifaceted challenges, including ethical and privacy-related issues, lack of awareness, unreliability of technology, and professional liability. The findings highlight that artificial intelligence has the potential to transform healthcare and that addressing these challenges is crucial to fully utilize its potential.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review

BACKGROUND

Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease. Hence, researchers have attempted to utilize ML models to predict the outcome of patients with sudden sensorineural hearing loss. The objectives of this study were to review the performance of these machine learning models and assess their applicability in real-world settings.

METHODS

A systematic search was conducted in PubMed, Web of Science and Scopus. Only studies that built machine learning prediction models were included, and studies that used algorithms such as logistic regression only for the purpose of adjusting for confounding variables were excluded. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST).

RESULTS

After screening, a total of 7 papers were eligible for synthesis. In total, these studies built 48 ML models. The most common utilized algorithms were Logistic Regression, Support Vector Machine (SVM) and boosting. The area under the curve of the receiver operating characteristic curve ranged between 0.59 and 0.915. All of the included studies had a high risk of bias; hence there are concerns regarding their applicability.

CONCLUSION

Although these models showed great performance and promising results, future studies are still needed before these models can be applied in a real-world setting. Future studies should employ multiple cohorts, different feature selection methods, and external validation to further validate the models' applicability.

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.

Unlocking the potential of big data and AI in medicine: insights from biobanking

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Analyzing patients satisfaction level for medical services using twitter data

Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer's satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

AIM

This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future.

DESIGN AND METHODS

A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice.

RESULTS

The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.

Health literacy in ChatGPT: exploring the potential of the use of artificial intelligence to produce academic text

The aim of this study was to identify and analyze the main constituent elements of text generated by ChatGPT in response to questions on an emerging topic in the academic literature in Portuguese - health literacy - and discuss how the evidence produced can contribute to improving our understanding of the limits and challenges of using artificial intelligence (AI) in academic writing. We conducted an exploratory descriptive study based on responses to five consecutive questions in Portuguese and English with increasing levels of complexity put to ChatGPT. Our findings reveal the potential of the use of widely available, unrestricted access AI-based technologies like ChatGPT for academic writing. Featuring a simple and intuitive interface, the tool generated structured and coherent text using natural-like language. Considering that academic productivism is associated with a growing trend in professional misconduct, especially plagiarism, there is a need too take a careful look at academic writing and scientific knowledge dissemination processes mediated by AI technologies.

Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems

The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.

Older Adults' Perceptions About Using Intelligent Toilet Seats Beyond Traditional Care: Web-Based Interview Survey

BACKGROUND

In contemporary society, age tech (age technology) represents a significant advancement in health care aimed at enhancing patient engagement, ensuring sustained independence, and promoting quality of life for older people. One innovative form of age tech is the intelligent toilet seat, which is designed to collect, analyze, and provide insights based on toileting logs and excreta data. Understanding how older people perceive and interact with such technology can offer invaluable insights to researchers, technology developers, and vendors.

OBJECTIVE

This study examined older adults' perspectives regarding the use of intelligent toilet seats. Through a qualitative methodology, this research aims to unearth the nuances of older people's opinions, shedding light on their preferences, concerns, and potential barriers to adoption.

METHODS

Data were collected using a web-based interview survey distributed on Amazon Mechanical Turk. The analyzed data set comprised 174 US-based individuals aged ≥65 years who voluntarily participated in this study. The qualitative data were carefully analyzed using NVivo (Lumivero) based on detailed content analysis, ensuring that emerging themes were coded and classified based on the conceptual similarities in the respondents' narratives.

RESULTS

The analysis revealed 5 dominant themes encompassing the opinions of aging adults. The perceived benefits and advantages of using the intelligent toilet seat were grouped into 3 primary themes: health-related benefits including the potential for early disease detection, continuous health monitoring, and seamless connection to health care insights. Technology-related advantages include the noninvasive nature of smart toilet seats and leveraging unique and innovative data collection and analysis technology. Use-related benefits include ease of use, potential for multiple users, and cost reduction owing to the reduced need for frequent clinical visits. Conversely, the concerns and perceived risks were classified into 2 significant themes: psychological concerns, which included concerns about embarrassment and aging-related stereotypes, and the potential emotional impact of constant health monitoring. Technical performance risks include concerns centered on privacy and security, device reliability, data accuracy, potential malfunctions, and the implications of false positives or negatives.

CONCLUSIONS

The decision of older adults to incorporate intelligent toilet seats into their daily lives depends on myriad factors. Although the potential health and technological benefits are evident, valid concerns that need to be addressed remain. To foster widespread adoption, it is imperative to enhance the advantages while simultaneously addressing and mitigating the identified risks. This balanced approach will pave the way for a more holistic integration of smart health care devices into the routines of the older population, ensuring that they reap the full benefits of age tech advancements.

Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis

In recent decades, artificial intelligence (AI) has played an increasingly important role in medicine, including dermatology. Worldwide, numerous studies have reported on AI applications in dermatology, rapidly increasing interest in this field. However, no bibliometric studies have been conducted to evaluate the past, present, or future of this topic. This study aimed to illustrate past and present research and outline future directions for global research on AI applications in dermatology using bibliometric analysis. We conducted an online search of the Web of Science Core Collection database to identify scientific papers on AI applications in dermatology. The bibliometric metadata of each selected paper were extracted, analyzed, and visualized using VOS viewer and Cite Space. A total of 406 papers, comprising 8 randomized controlled trials and 20 prospective studies, were deemed eligible for inclusion. The United States had the highest number of papers (n = 166). The University of California System (n = 24) and Allan C. Halpern (n = 11) were the institution and author with the highest number of papers, respectively. Based on keyword co-occurrence analysis, the studies were categorized into 9 distinct clusters, with clusters 2, 3, and 7 containing keywords with the latest average publication year. Wound progression prediction using machine learning, the integration of AI into teledermatology, and applications of the algorithms in skin diseases, are the current research priorities and will remain future research aims in this field.

The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews

BACKGROUND AND PURPOSE

In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients.

DATA SOURCES

The acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately.

RESULTS

In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis.

CONCLUSIONS

The detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems.

TRIAL REGISTRATION

Systematic review registration. Prospero registration number: CRD42022307403.

Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions

BACKGROUND

Artificial intelligence (AI) performed variously among test sets with different diversity due to sample selection bias, which can be stumbling block for AI applications. We previously tested AI named ENDOANGEL, diagnosing early gastric cancer (EGC) on single-center videos in man-machine competition. We aimed to re-test ENDOANGEL on multi-center videos to explore challenges applying AI in multiple centers, then upgrade ENDOANGEL and explore solutions to the challenge.

METHODS

ENDOANGEL was re-tested on multi-center videos retrospectively collected from 12 institutions and compared with performance in previously reported single-center videos. We then upgraded ENDOANGEL to ENDOANGEL-2022 with more training samples and novel algorithms and conducted competition between ENDOANGEL-2022 and endoscopists. ENDOANGEL-2022 was then tested on single-center videos and compared with performance in multi-center videos; the two AI systems were also compared with each other and endoscopists.

RESULTS

Forty-six EGCs and 54 non-cancers were included in multi-center video cohort. On diagnosing EGCs, compared with single-center videos, ENDOANGEL showed stable sensitivity (97.83% vs. 100.00%) while sharply decreased specificity (61.11% vs. 82.54%); ENDOANGEL-2022 showed similar tendency while achieving significantly higher specificity (79.63%, p < 0.01) making fewer mistakes on typical lesions than ENDOANGEL. On detecting gastric neoplasms, both AI showed stable sensitivity while sharply decreased specificity. Nevertheless, both AI outperformed endoscopists in the two competitions.

CONCLUSIONS

Great increase of false positives is a prominent challenge for applying EGC diagnostic AI in multiple centers due to high heterogeneity of negative cases. Optimizing AI by adding samples and using novel algorithms is promising to overcome this challenge.

Clinicians' Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

INTRODUCTION

The healthcare industry has made significant progress in information technology, which has improved healthcare procedures and brought about advancements in clinical care services. This includes gathering crucial clinical data and implementing intelligent health information management. Artificial Intelligence (AI) has the potential to bolster further existing health information systems, notably electronic health records (EHRs). With AI, EHRs can offer more customized and adaptable roles for patients. This study aims to delve into the current and potential uses of AI and examine the obstacles that come with it.

METHOD

In this study, we employed a qualitative methodology and purposive sampling to select participants. We sought out clinicians who were eager to share their professional insights. Our research involved conducting three focus group interviews, each lasting an hour. The moderator began each session by introducing the study's goals and assuring participants of confidentiality to foster a collaborative environment. The facilitator asked open-ended questions about EHR, including its applications, challenges, and AI-assisted features.

RESULTS

The research conducted by 26 participants has identified five crucial areas of using AI in healthcare delivery. These areas include predictive analysis, clinical decision support systems, data visualization, natural language processing (NLP), patient monitoring, mobile technology, and future and emerging trends. However, the hype surrounding AI and the fact that the technology is still in its early stages pose significant challenges. Technical limitations related to language processing and context-specific reasoning must be addressed. Furthermore, medico-legal challenges arise when AI supports or autonomously delivers healthcare services. Governments must develop strategies to ensure AI's responsible and transparent application in healthcare delivery.

CONCLUSION

AI technology has the potential to revolutionize healthcare through its integration with EHRs and other existing technologies. However, several challenges must be addressed before this potential can be fully realized. The development and testing of complex EHR systems that utilize AI must be approached with care to ensure their accuracy and trustworthiness in decision-making about patient treatment. Additionally, there is a need to navigate medico-legal obligations and ensure that benefits are equitably distributed.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions

Background: The use of artificial intelligence (AI) in health care has grown exponentially with the promise of facilitating biomedical research and enhancing diagnosis, treatment, monitoring, disease prevention, and health care delivery. We aim to examine the current state, limitations, and future directions of AI in thyroidology. Summary: AI has been explored in thyroidology since the 1990s, and currently, there is an increasing interest in applying AI to improve the care of patients with thyroid nodules (TNODs), thyroid cancer, and functional or autoimmune thyroid disease. These applications aim to automate processes, improve the accuracy and consistency of diagnosis, personalize treatment, decrease the burden for health care professionals, improve access to specialized care in areas lacking expertise, deepen the understanding of subtle pathophysiologic patterns, and accelerate the learning curve of less experienced clinicians. There are promising results for many of these applications. Yet, most are in the validation or early clinical evaluation stages. Only a few are currently adopted for risk stratification of TNODs by ultrasound and determination of the malignant nature of indeterminate TNODs by molecular testing. Challenges of the currently available AI applications include the lack of prospective and multicenter validations and utility studies, small and low diversity of training data sets, differences in data sources, lack of explainability, unclear clinical impact, inadequate stakeholder engagement, and inability to use outside of the research setting, which might limit the value of their future adoption. Conclusions: AI has the potential to improve many aspects of thyroidology; however, addressing the limitations affecting the suitability of AI interventions in thyroidology is a prerequisite to ensure that AI provides added value for patients with thyroid disease.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization

This editorial discusses the potential benefits of integrating large language models (LLMs), such as GPT-4, into electronic health records (EHRs) to optimize patient care, improve clinical decision-making, and promote efficient healthcare management. Artificial intelligence (AI)-driven LLMs can revolutionize healthcare practices by streamlining the data input process, expediting information extraction from unstructured narratives, and facilitating personalized patient communication. However, concerns related to patient privacy, data security, and potential biases must be addressed to ensure equitable healthcare for all. Therefore, we encourage healthcare professionals and researchers to explore innovative solutions that leverage AI capabilities while addressing the challenges associated with privacy and equity.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

The General Population's Perspectives on Implementation of Artificial Intelligence in Radiology in the Western Region of Saudi Arabia

Background  Artificial intelligence (AI) is a broad spectrum of computer-executed operations that mimics the human intellect. It is expected to improve healthcare practice in general and radiology in particular by enhancing image acquisition, image analysis, and processing speed. Despite the rapid development of AI systems, successful application in radiology requires analysis of social factors such as the public's perspectives toward the technology. Objectives The current study aims to investigate the general population's perspectives on AI implementation in radiology in the Western region of Saudi Arabia. Methods A cross-sectional study was conducted from November 2022 and July 2023 utilizing a self-administrative online survey distributed via social media platforms. A convenience sampling technique was used to recruit the study participants. After obtaining Institutional Review Board approval, data were collected from citizens and residents of the western region of Saudi Arabia aged 18 years or older. Results A total of 1,024 participants were included in the present study, with the mean age of respondents being 29.6 ± 11.3. Of them, 49.9% (511) were men, and 50.1% (513) were women. The comprehensive mean score of the first four domains among our participants was 3.93 out of 5.00. Higher mean scores suggest being more negative regarding AI in radiology, except for the fifth domain. Respondents had less trust in AI utilization in radiology, as evidenced by their overall distrust and accountability domain mean score of 3.52 out of 5. The majority of respondents agreed that it is essential to understand every step of the diagnostic process, and the mean score for the procedural knowledge domain was 4.34 out of 5. The mean score for the personal interaction domain was 4.31 out of 5, indicating that the participants agreed on the value of direct communication between the patient and the radiologist for discussing test results and asking questions. Our data show that people think AI is more effective than human doctors in making accurate diagnoses and decreasing patient wait times, with an overall mean score of the efficiency domain of 3.56 out of 5. Finally, the fifth domain, "being informed," had a mean score of 3.91 out of 5. Conclusion The application of AI in radiologic assessment and interpretation is generally viewed negatively. Even though people think AI is more efficient and accurate at diagnosing than humans, they still think that computers will never be able to match a specialist doctor's years of training.

Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.

Use of artificial intelligence in paediatric anaesthesia: a systematic review

Objectives

Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this systematic review aims to characterise the current use of AI in paediatric anaesthesia and to identify barriers to the successful integration of such technologies.

Methods

This review was registered with PROSPERO (CRD42022304610), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in five electronic databases (Embase, Medline, Central, Scopus, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for paediatric anaesthesia (<18 yr old) within the perioperative setting.

Results

From 3313 records identified in the initial search, 40 were included in this review. Identified applications of AI were described for patient risk factor prediction (24 studies; 60%), anaesthetic depth estimation (2; 5%), anaesthetic medication/technique decision guidance (2; 5%), intubation assistance (1; 2.5%), airway device selection (3; 7.5%), physiological variable monitoring (6; 15%), and operating room scheduling (2; 5%). Multiple domains of AI were discussed including machine learning, computer vision, fuzzy logic, and natural language processing.

Conclusion

There is an emerging literature regarding applications of AI for paediatric anaesthesia, and their clinical integration holds potential for ultimately improving patient outcomes. However, multiple barriers to their clinical integration remain including a lack of high-quality input data, lack of external validation/evaluation, and unclear generalisability to diverse settings.

Systematic review protocol

CRD42022304610 (PROSPERO).

Automation: A revolutionary vision of artificial intelligence in theranostics

The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.