Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

Diagnostic Accuracy of IDX-DR for Detecting Diabetic Retinopathy: A Systematic Review and Meta-Analysis

PURPOSE

Diabetic retinopathy (DR) is a leading cause of vision loss worldwide, making early detection critical to prevent blindness. IDX-DR, an FDA-approved autonomous artificial intelligence (AI) system, has emerged as an innovative solution to improve access to DR screening. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of IDX-DR in detecting diabetic retinopathy.

DESIGN

Systematic review and meta-analysis.

METHODS

A comprehensive literature search was conducted across PubMed, Embase, Scopus and Web of Science, identifying studies published through October 5, 2024. Studies involving adult patients with Type 1 or Type 2 diabetes and reporting diagnostic metrics such as sensitivity and specificity were included. The primary outcomes were pooled sensitivity and specificity of IDX-DR. A bivariate random-effects model was used for meta-analysis, and summary receiver operating characteristic (SROC) curves were generated to assess diagnostic performance. Statistical analyses were performed using MetaDisc software version 2.0.

RESULTS

Thirteen studies involving 13,233 participants met the inclusion criteria. IDX-DR's pooled sensitivity was 0.95 (95% CI: 0.82-0.99), and its pooled specificity was 0.91 (95% CI: 0.84-0.95). The SROC curve confirmed IDX-DR's high diagnostic accuracy in detecting diabetic retinopathy across various clinical environments. The AUC value of 0.95 demonstrated high sensitivity and specificity, indicating a robust diagnostic performance for IDX-DR in detecting diabetic retinopathy.

CONCLUSION

IDX-DR is a highly effective diagnostic tool for diabetic retinopathy screening, with robust sensitivity and good specificity. Its integration into clinical practice, especially in resource-limited settings, can potentially improve early detection and reduce vision loss. However, careful implementation is needed to address challenges such as over-diagnosis and ensure the tool complements clinical judgment. Future studies should explore the long-term impacts of AI-based screening and address ethical considerations surrounding its use.

Man and machine: exploring the intersection of artificial intelligence and men's health

PURPOSE OF REVIEW

Explore the current state of artificial intelligence in the Men's Health space.

RECENT FINDINGS

Artificial intelligence is emerging in the field of Men's Health with recent publications highlighting a role for optimization of male infertility diagnostics and treatment, clinical predictive tools, patient education, and improvements in clinical workflow.

SUMMARY

Artificial intelligence is set to be a prime instrument in the advancement of both patient care and patient education in the Men's Health space.

An artificial intelligence perspective on geriatric syndromes: assessing the information accuracy and readability of ChatGPT

PURPOSE

ChatGPT, a comprehensive language processing model, provides the opportunity for supportive and professional interactions with patients. However, its use to address patients' frequently asked questions (FAQs) and the readability of the text generated by ChatGPT remain unexplored, particularly in geriatrics. We identified the FAQs about common geriatric syndromes and assessed the accuracy and readability of the responses provided by ChatGPT.

METHODS

Two geriatricians with extensive knowledge and experience in geriatric syndromes independently reviewed the 28 responses provided by ChatGPT. The accuracy of the responses generated by ChatGPT was categorized on a rating scale from 0 (harmful) to 4 (excellent) based on current guidelines and approaches. The readability of the text generated by ChatGPT was assessed by administering two tests: the Flesch-Kincaid Reading Ease (FKRE) and the Flesch-Kincaid Grade Level (FKGL).

RESULTS

ChatGPT-generated responses with an overall mean accuracy score of 88% (3.52/4). Responses generated for sarcopenia diagnosis and depression treatment in older adults had the lowest accuracy scores (2.0 and 2.5, respectively). The mean FKRE score of the texts was 25.2, while the mean FKGL score was 14.5.

CONCLUSION

The accuracy scores of the responses generated by ChatGPT were high in most common geriatric syndromes except for sarcopenia diagnosis and depression treatment. Moreover, the text generated by ChatGPT was very difficult to read and best understood by college graduates. ChatGPT may reduce the uncertainty many patients face. Nevertheless, it remains advisable to consult with subject matter experts when undertaking consequential decision-making.

The future of Alzheimer's disease risk prediction: a systematic review

BACKGROUND

Alzheimer's disease is the most prevalent kind of age-associated dementia among older adults globally. Traditional diagnostic models for predicting Alzheimer's disease risks primarily rely on demographic and clinical data to develop policies and assess probabilities. However, recent advancements in machine learning (ML) and other artificial intelligence (AI) have shown promise in developing personalized risk models. These models use specific patient data from medical imaging and related reports. In this systematic review, different studies comprehensively examined the use of ML in magnetic resonance imaging (MRI), genetics, radiomics, and medical data for Alzheimer's disease risk assessment. I highlighted the results of our rigorous analysis of this research and emphasized the exciting potential of ML methods for Alzheimer's disease risk prediction. We also looked at current research projects and possible uses of AI-driven methods to enhance Alzheimer's disease risk prediction and enable more efficient investigating and individualized risk mitigation strategies.

AIM AND METHODS

This review integrates both conventional and AI-based models to thoroughly analyze neuroimaging and non-neuroimaging features used in Alzheimer's disease prediction. This study examined factors related to imaging, radiomics, genetics, and clinical aspects. In addition, this study comprehensively presented machine learning for predicting the risk of Alzheimer's disease detection to benefit both beginner and expert researchers.

RESULTS

A total of 700 publications from 2000 and 2024, were initially retrieved, out of which 120 studies met the inclusion criteria and were elected for review. The diagnosis of neurological disorders, along with the application of deep learning (DL) and machine learning (ML) were central themes in studies on the subject. When analyzing the medical implementation or design of innovative models, various machine learning models applied to neuroimaging and non-neuroimaging data may help researchers and clinicians become more informed. This review provides an extensive guide to the state of Alzheimer's disease risk assessment with artificial AI.

CONCLUSION

By integrating diverse neuroimaging and non-neuroimaging data sources, this study provides researchers with an alternative viewpoint on the application of AI in Alzheimer's disease risk prediction emphasizing its potential to improve early diagnosis and personalized intervention strategies.

Assessing Patient-Reported Satisfaction With Care and Documentation Time in Primary Care Through AI-Driven Automatic Clinical Note Generation: Protocol for a Proof-of-Concept Study

BACKGROUND

Relisten is an artificial intelligence (AI)-based software developed by Recog Analytics that improves patient care by facilitating more natural interactions between health care professionals and patients. This tool extracts relevant information from recorded conversations, structuring it in the medical record, and sending it to the Health Information System after the professional's approval. This approach allows professionals to focus on the patient without the need to perform clinical documentation tasks.

OBJECTIVE

This study aims to evaluate patient-reported satisfaction and perceived quality of care, assess health care professionals' satisfaction with the care provided, and measure the time spent on entering records into the electronic medical record using this AI-powered solution.

METHODS

This proof-of-concept (PoC) study is conducted as a multicenter trial with the participation of several health care professionals (nurses and physicians) in primary care centers (CAPs). The key outcome measures include (1) patient-reported quality of care (evaluated through anonymous surveys), (2) health care professionals' satisfaction with the care provided (assessed through surveys and structured interviews), and (3) time saved on clinical documentation (determined by comparing the time spent manually writing notes versus reviewing and correcting AI-generated notes). Statistical analyses will be performed for each objective, using independent sample comparison tests according to normality evaluated with the Kolmogorov-Smirnov test and Lilliefors correction. Stratified statistical tests will also be performed to consider the variance between professionals.

RESULTS

The protocol has been developed using the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) checklist. Recruitment began in July 2024, and as of November 2024, a total of 318 patients have been enrolled. Recruitment is expected to be completed by March 2025. Data analysis will take place between April and May 2025, with results expected to be published in June 2025.

CONCLUSIONS

We expect an improvement in the perceived quality of care reported by patients and a significant reduction in the time spent taking clinical notes, with a saving of at least 30 seconds per visit. Although a high quality of the notes generated is expected, it is uncertain whether a significant improvement over the control group, which is already expected to have high-quality notes, will be demonstrated.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06618092; https://clinicaltrials.gov/study/NCT06618092.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/66232.

What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach

This article reflects on explainability in the context of medical artificial intelligence (AI) applications, focusing on AI-based clinical decision support systems (CDSS). After introducing the concept of explainability in AI and providing a short overview of AI-based clinical decision support systems (CDSSs) and the role of explainability in CDSSs, four use cases of AI-based CDSSs will be presented. The examples were chosen to highlight different types of AI-based CDSSs as well as different types of explanations: a machine language (ML) tool that lacks explainability; an approach with post hoc explanations; a hybrid model that provides medical knowledge-based explanations; and a causal model that involves complex moral concepts. Then, the role, relevance, and implications of explainability in the context of the use cases will be discussed, focusing on seven explainability-related aspects and themes. These are: (1) The addressees of explainability in medical AI; (2) the relevance of explainability for medical decision making; (3) the type of explanation provided; (4) the (often-cited) conflict between explainability and accuracy; (5) epistemic authority and automation bias; (6) Individual preferences and values; (7) patient autonomy and doctor-patient relationships. The case-based discussion reveals that the role and relevance of explainability in AI-based CDSSs varies considerably depending on the tool and use context. While it is plausible to assume that explainability in medical AI has positive implications, empirical data on explainability and explainability-related implications is scarce. Use-case-based studies are needed to investigate not only the technical aspects of explainability but also the perspectives of clinicians and patients on the relevance of explainability and its implications.

Clinical Applications of Artificial Intelligence in Neurology Practice

ABSTRACT

As artificial intelligence (AI) tools become increasingly mainstream, they can potentially transform neurology clinical practice by improving patient care and reducing clinician workload. However, with these promises also come perils, and neurologists must understand AI as it becomes integrated into health care. This article presents a brief background on AI and explores some of the potential applications in health care and neurology clinical practice with a focus on improving diagnostic testing, documentation, and clinical workflows and highlighting opportunities to address long-standing human biases and challenges and potential mitigation strategies.

Artificial intelligence applications in smile design dentistry: A scoping review

PURPOSE

Artificial intelligence (AI) applications are growing in smile design and aesthetic procedures. The current expansion and performance of AI models in digital smile design applications have not yet been systematically documented and analyzed. The purpose of this review was to assess the performance of AI models in smile design, assess the criteria of points of reference using AI analysis, and assess different AI software performance.

METHODS

An electronic review was completed in five databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. Studies that developed AI models for smile design were included. The search strategy included articles published until November 1, 2024. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies and Textual Evidence: Expert Opinion Results.

RESULTS

The search resulted in 2653 articles. A total of 2649 were excluded according to the exclusion criteria after reading the title, abstract, and/or full-text review. Four articles published between 2023 and 2024 were included in the present investigation. Two articles compared 2D and 3D points while one article compared the outcome of satisfaction between dentists and patients, and the last article emphasized the ethical components of using AI.

CONCLUSION

The results of the studies reviewed in this paper suggest that AI-generated smile designs are not significantly different from manually created designs in terms of esthetic perception. 3D designs are more accurate than 2D designs and offer more advantages. More articles are needed in the field of AI and smile design.

Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human

Rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA), present diagnostic and management challenges due to their impact on connective tissues and the musculoskeletal system. Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. Convolutional neural networks, a DL model type, have shown great potential in medical image classification, segmentation, and anomaly detection, often surpassing human performance in tasks like tumor identification and disease severity grading. In rheumatology, DL models have been applied to plain radiography, ultrasounds, and MRI for assessing joint damage, synovial inflammation, and disease progression in RA, OA, and SpA patients. Despite the promise of DL, challenges such as data bias, limited explainability, and the need for large annotated datasets remain significant barriers to its widespread adoption. Furthermore, human oversight and value judgment are essential for ensuring the ethical use and effective implementation of DL in clinical settings. This review provides a comprehensive overview of DL's applications in rheumatologic imaging and explores its future potential in enhancing diagnosis, treatment decisions, and personalized medicine.

AGA Living Clinical Practice Guideline on Computer-Aided Detection-Assisted Colonoscopy

BACKGROUND & AIMS

This American Gastroenterological Association (AGA) guideline is intended to provide an overview of the evidence and support endoscopists and patients on the use of computer-aided detection (CADe) systems for the detection of colorectal polyps during colonoscopy.

METHODS

A multidisciplinary panel of content experts and guideline methodologists used the Grading of Recommendations Assessment, Development and Evaluation framework and relied on the following sources of evidence: (1) a systematic review examining the desirable and undesirable effects (ie, benefits and harms) of CADe-assisted colonoscopy, (2) a microsimulation study estimating the effects of CADe on longer-term patient-important outcomes, (3) a systematic search of evidence evaluating the values and preferences of patients undergoing colonoscopy, and (4) a systematic review of studies evaluating health care providers' trust in artificial intelligence technology in gastroenterology.

RESULTS

The panel reached the conclusion that no recommendation could be made for or against the use of CADe-assisted colonoscopy in light of very low certainty of evidence for the critical outcomes, desirable and undesirable (11 fewer colorectal cancers per 10,000 individuals and 2 fewer colorectal cancer deaths per 10,000 individuals), increased burden of more intensive surveillance colonoscopies (635 more per 10,000 individuals), and cost and resource implications. The panel acknowledged the 8% (95% CI, 6%-10%) increase in adenoma detection rate and 2% (95% CI, 0%-4%) increase in advanced adenoma and/or sessile serrated lesion detection rate.

CONCLUSIONS

This guideline highlights the close tradeoff between desirable and undesirable effects and the limitations in the current evidence to support a recommendation. The panel acknowledged the potential for CADe to continually improve as an iterative artificial intelligence application. Ongoing publications providing evidence for critical outcomes will help inform a future recommendation.

Wearable biosensors for monitoring and as a predictive adjunct for patients at risk for ischemic cardiac-related injury

Despite increased attention and preventive efforts, the prevalence of major adverse cardiovascular events continues to rise, resulting in profound concerns for both the individual and the population at large. Rapidly evolving biotechnologies, micro-computerization, communication, and battery design have led to widespread commercial adoption, use, and dependence on smart devices, and, more recently, biosensors. Currently worn and carried, smart devices such as mobile phones and smart watches possess impressive computational and communication capabilities, monitoring a variety of biometrics such as heart rate, blood pressure, and cardiac rhythm. Several promising biomarkers have been identified that are expressed early in the development of cardiac injury. Biosensors that can assay multiple variants are now described, obviating the limitations generally attributed to dependence upon a single biomarker. Employing mathematical modeling along with intelligent learning capabilities complements and augments their potential value. Data derived from wearable multivariate biosensors linked to already worn smart devices can communicate information to protected settings with enhanced computational capability and cogency by evaluating relayed biometrics and early expressed biomarkers as well as trending data, improving sensitivity and specificity. Integrating intelligent learning capabilities can further power these efforts with beneficial impact on individuals and groups at risk, yielding great promise as monitoring and predictive adjuncts. Future derivations might, for those of particular concern, be linked to critical drug delivery and interventional systems.

Revolutionizing clinical laboratories: The impact of artificial intelligence in diagnostics and patient care

INTRODUCTION

The integration of artificial intelligence (AI) is fundamentally transforming clinical laboratories, significantly improving diagnostic precision and operational effectiveness in the fields of pathology, microbiology, and biochemistry. This evolution holds great promise for advancing patient care and enhancing disease management strategies.

METHODOLOGY

A comprehensive literature review was performed using established databases, including Google Scholar, Embase, Science Direct, Scopus, and PubMed. Recent studies published between 2020 and 2024 were sourced using focused keywords related to AI's application in clinical laboratory settings. The inclusion criteria prioritized peer-reviewed articles that contributed to innovations in diagnostic methodologies and operational efficiency. A thematic analysis was conducted to collate findings regarding AI's impact across the preanalytical, analytical, and postanalytical phases of laboratory work.

DISCUSSION

AI significantly enhances various laboratory processes, such as histopathology, immunohistochemistry, and microbiological diagnostics. Notable applications include workflow automation, detailed analysis of biomarker data, and real-time processing to facilitate clinical decision-making. However, the benefits of AI come with challenges, including concerns about data integrity, ethical implications, and potential biases in algorithms, requiring careful management as AI becomes more integrated into clinical practice.

CONCLUSION

The future of clinical laboratories is poised for increased automation and the incorporation of AI and IoT technologies. While these advancements offer the potential for improved healthcare outcomes through greater accuracy and efficiency, evolving ethical and legal frameworks are crucial to address issues related to data privacy and accountability of algorithms. Ongoing adaptation and exploration of AI applications are vital to fully harnessing its capabilities in diagnostics.

How Italian radiation oncologists use ChatGPT: a survey by the young group of the Italian association of radiotherapy and clinical oncology (yAIRO)

PURPOSE

To investigate the awareness and the spread of ChatGPT and its possible role in both scientific research and clinical practice among the young radiation oncologists (RO).

MATERIAL AND METHODS

An anonymous, online survey via Google Forms (including 24 questions) was distributed among young (< 40 years old) ROs in Italy through the yAIRO network, from March 15, 2024, to 31, 2024. These ROs were officially registered with yAIRO in 2023. We particularly focused on the emerging use of ChatGPT and its future perspectives in clinical practice.

RESULTS

A total of 76 young physicians answered the survey. Seventy-three participants declared to be familiar with ChatGPT, and 71.1% of the surveyed physicians have already used ChatGPT. Thirty-one (40.8%) participants strongly agreed that AI has the potential to change the medical landscape in the future. Additionally, 79.1% of respondents agreed that AI will be mainly successful in research processes such as literature review and drafting articles/protocols. The belief in ChatGPT's potential results in direct use in daily practice in 43.4% of the cases, with mainly a fair grade of satisfaction (43.2%). A large part of participants (69.7%) believes in the implementation of ChatGPT into clinical practice, even though 53.9% fear an overall negative impact.

CONCLUSIONS

The results of the present survey clearly highlight the attitude of young Italian ROs toward the implementation of ChatGPT into clinical and academic RO practice. ChatGPT is considered a valuable and effective tool that can ease current and future workflows.

Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis

Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into medicine, including ophthalmology, owing to its strong capabilities in image recognition. Methods: This review focuses on the most recent key applications of AI in the diagnosis and management of, as well as research on, glaucoma by performing a systematic review of the latest papers in the literature. Results: In glaucoma, AI can help analyze large amounts of data from diagnostic tools, such as fundus images, optical coherence tomography scans, and visual field tests. Conclusions: AI technologies can enhance the accuracy of glaucoma diagnoses and could provide significant economic benefits by automating routine tasks, improving diagnostic accuracy, and enhancing access to care, especially in underserved areas. However, despite these promising results, challenges persist, including limited dataset size and diversity, class imbalance, the need to optimize models for early detection, and the integration of multimodal data into clinical practice. Currently, ophthalmologists are expected to continue playing a leading role in managing glaucomatous eyes and overseeing the development and validation of AI tools.

Commentary on "Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualitative Systematic Review" by Nogueira et al. (2025)

No Level Assigned This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Prospects and perils of ChatGPT in diabetes

ChatGPT, a popular large language model developed by OpenAI, has the potential to transform the management of diabetes mellitus. It is a conversational artificial intelligence model trained on extensive datasets, although not specifically health-related. The development and core components of ChatGPT include neural networks and machine learning. Since the current model is not yet developed on diabetes-related datasets, it has limitations such as the risk of inaccuracies and the need for human supervision. Nevertheless, it has the potential to aid in patient engagement, medical education, and clinical decision support. In diabetes management, it can contribute to patient education, personalized dietary guidelines, and providing emotional support. Specifically, it is being tested in clinical scenarios such as assessment of obesity, screening for diabetic retinopathy, and provision of guidelines for the management of diabetic ketoacidosis. Ethical and legal considerations are essential before ChatGPT can be integrated into healthcare. Potential concerns relate to data privacy, accuracy of responses, and maintenance of the patient-doctor relationship. Ultimately, while ChatGPT and large language models hold immense potential to revolutionize diabetes care, one needs to weigh their limitations, ethical implications, and the need for human supervision. The integration promises a future of proactive, personalized, and patient-centric care in diabetes management.

A scoping review of digital health technologies in multimorbidity management: mechanisms, outcomes, challenges, and strategies

INTRODUCTION

Multimorbidity amplifies healthcare burdens due to the intricate requirements of patients and the pathophysiological complexities of multiple diseases. To address this, digital health technologies play a crucial role in effective healthcare delivery, requiring comprehensive evidence on their applications in managing multimorbidity. Therefore, this scoping review aims to identify various types of digital health technologies, explore their mechanisms, and identify barriers and facilitators within the context of multimorbidity.

METHODS

This scoping review follows the Preferred Reporting Items for Scoping Reviews guidelines. PubMed, Scopus, Web of Science, EMBASE, and Google Scholar were used to search articles. Data extraction focused on study characteristics, types of health technologies, mechanisms, outcomes, challenges, and facilitators. Results were presented using figures, tables, and texts. Thematic analysis was employed to describe mechanisms, impacts, challenges, and strategies related to digital health technologies in managing multimorbidity.

RESULTS

Digital health technology encompasses smartphone apps, wearable devices, and platforms for remote healthcare (telehealth). These technologies work through care coordination, collaboration, communication, self-management, remote monitoring, health data management, and tele-referrals. Digital health technologies improved quality of care and life, cost efficiency, acceptability of care, collaboration, streamlined healthcare delivery, reduced workload, and bridging knowledge gaps. Patients' and healthcare providers' resistance and skills, lack of support (technical, financial, and infrastructure), and ethical concerns (e.g., privacy) barred digital health technologies implementation. Arranging organization, providing technical support, employing care coordination strategies, enhancing acceptability, deploying appropriate technology, considering patient needs, and adhering with ethical principles facilitate digital health technologies implementation.

CONCLUSIONS

Digital health technology holds significant promise in improving care for individuals with multimorbidity by enhancing coordination, self-management, and monitoring. Successful implementation requires addressing challenges such as patient resistance and infrastructure limitations through targeted strategies and investments. It is also essential to consider usability, privacy, and trustworthiness when adopting these tools.

Deep Learning in Scaphoid Nonunion Treatment

Background/Objectives: Scaphoid fractures are notorious for a high rate of nonunion, resulting in chronic pain and impaired wrist function. The decision for surgical intervention often involves extensive imaging and prolonged conservative management, leading to delays in definitive treatment. The effectiveness of such treatment remains a subject of ongoing clinical debate, with no universally accepted predictive tool for surgical success. The objective of this study was to train a deep learning algorithm to reliably identify cases of nonunion with a high probability of subsequent union following operative revision. Methods: This study utilized a comprehensive database of 346 patients diagnosed with scaphoid nonunions, with preoperative and postoperative X-rays available for analysis. A classical logistic regression for clinical parameters was used, as well as a TensorFlow deep learning algorithm on X-rays. The latter was developed and applied to these imaging datasets to predict the likelihood of surgical success based solely on the preoperative anteroposterior (AP) X-ray view. The model was trained and validated over six epochs to optimize its predictive accuracy. Results: The logistic regression yielded an accuracy of 66.3% in predicting the surgical outcome based on patient parameters. The deep learning model demonstrated remarkable predictive accuracy, achieving a success rate of 93.6%, suggesting its potential as a reliable tool for guiding clinical decision-making in scaphoid nonunion management. Conclusions: The findings of this study indicate that the preoperative AP X-ray of a scaphoid nonunion provides sufficient information to predict the likelihood of surgical success when analyzed using our deep learning model. This approach has the potential to streamline decision-making and reduce reliance on extensive imaging and prolonged conservative treatment.

Should Artificial Intelligence-Based Patient Preference Predictors Be Used for Incapacitated Patients? A Scoping Review of Reasons to Facilitate Medico-Legal Considerations

BACKGROUND

Research indicates that surrogate decision-makers often struggle to accurately interpret and reflect the preferences of incapacitated patients they represent. This discrepancy raises important concerns about the reliability of such practice. Artificial intelligence (AI)-based Patient Preference Predictors (PPPs) are emerging tools proposed to guide healthcare decisions for patients who lack decision-making capacity.

OBJECTIVES

This scoping review aims to provide a thorough analysis of the arguments, both for and against their use, presented in the academic literature.

METHODS

A search was conducted in PubMed, Web of Science, and Scopus to identify relevant publications. After screening titles and abstracts based on predefined inclusion and exclusion criteria, 16 publications were selected for full-text analysis.

RESULTS

The arguments in favor are fewer in number compared to those against. Proponents of AI-PPPs highlight their potential to improve the accuracy of predictions regarding patients' preferences, reduce the emotional burden on surrogates and family members, and optimize healthcare resource allocation. Conversely, critics point to risks including reinforcing existing biases in medical data, undermining patient autonomy, raising critical concerns about privacy, data security, and explainability, and contributing to the depersonalization of decision-making processes.

CONCLUSIONS

Further empirical studies are needed to assess the acceptability and feasibility of these tools among key stakeholders, such as patients, surrogates, and clinicians. Moreover, robust interdisciplinary research is needed to explore the legal and medico-legal implications associated with their implementation, ensuring that these tools align with ethical principles and support patient-centered and equitable healthcare practices.

The ethical considerations of integrating artificial intelligence into surgery: a review

The integration of artificial intelligence (AI) into surgery raises significant ethical concerns, including the impact on autonomy, human authority and the patient-doctor relationship. This study underscores the need for a multidisciplinary approach to navigate these ethical dilemmas, involving stakeholders from various fields. A comprehensive literature review up to March 2024 was conducted to assess the ethical implications of AI applications in surgery. This included an examination of data privacy, informed consent, algorithmic bias, the role of advanced robotics, and the impact on surgeons' decision-making. The study also considered the development of autonomous surgical robots and their ethical implications. The review highlights that while AI can enhance surgical precision and improve clinical decision-making, it also poses several ethical challenges. AI's ability to support decision-making risks undermining surgeons' autonomy and judgement, raising concerns about over-reliance on technology. Issues such as data privacy, algorithmic bias and equitable access to AI-driven tools were identified as key ethical concerns. Autonomous surgical robots, while promising, introduce complex questions about accountability and liability, particularly when unexpected outcomes occur. Effective integration of AI into surgical practices demands the development of ethical frameworks that respect both the capabilities of AI and the irreplaceable value of human judgement. Balancing technological advancement with ethical integrity is essential to safeguard patient-centred care and ensure equitable access to AI benefits in healthcare.

Utilisation of robots in nursing practice: an umbrella review

BACKGROUND

The provision of nursing care across the globe is confronted with a range of challenges, including the surge in the older persons population which amplifies the reliance on nursing services and exacerbates the shortage of nurses worldwide. A possible solution could be the broader implementation of robotics in nursing practice. Therefore, this umbrella review aimed to assess and synthesise systematically reviewed evidence on the utilisation of robots in nursing practice.

METHODS

An extensive search of nine relevant databases was conducted for research syntheses. We included reviews that reported the experiences of nurses, perceived benefits and challenges of using robots in nursing practice in all care settings and published between the years 2012 and 2022. A supplementary search was conducted in October 2024 using the same criteria. Quality appraisal, data extraction and syntheses were carried out according to Joanna Brigg's Institute's guidelines for undertaking umbrella reviews. The protocol of this umbrella review was registered on PROSPERO prior to the commencement of the review (Registration ID CRD42022361835).

RESULTS

Thirteen reviews (representing 558 studies) were included following the quality appraisal. The evidence was summarised in narrative form with supporting quotes from the reviews. The findings were grouped into categories, which were further categorised into three main synthesised findings: 'Documented experiences of nurses in using robots', 'perceived benefits of using robots' and 'perceived challenges of using robots'. To the best of our knowledge, this is the first umbrella review that synthesised evidence on the experiences and perceptions of nurses regarding the use of robots. This umbrella review has limitations as it is not the primary source of evidence, relying on the quality of the included reviews and studies.

CONCLUSIONS

Evidence shows that there is a perception that robots can support nurses in their work. However, there is not enough experiential evidence from nurses who work with robots in practice to support this. There are also perceived challenges that are of concern to nurses, particularly in relation to liability, ethical dilemmas and patient safety. The authors have no competing interests to declare in the conduct of this review.

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.

Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges

INTRODUCTION

Artificial Intelligence is in the phase of health care, with transformative innovations in diagnostics, personalized treatment, and operational efficiency. While having potential, critical challenges are apparent in areas of safety, trust, security, and ethical governance. The development of these challenges is important for promoting the responsible adoption of AI technologies into healthcare systems.

METHODS

This systematic review of studies published between 2010 and 2023 addressed the applications of AI in healthcare and their implications for safety, transparency, and ethics. A comprehensive search was performed in PubMed, IEEE Xplore, Scopus, and Google Scholar. Those studies that met the inclusion criteria provided empirical evidence, theoretical insights, or systematic evaluations addressing trust, security, and ethical considerations.

RESULTS

The analysis brought out both the innovative technologies and the continued challenges. Explainable AI (XAI) emerged as one of the significant developments. It made it possible for healthcare professionals to understand AI-driven recommendations, by this means increasing transparency and trust. Still, challenges in adversarial attacks, algorithmic bias, and variable regulatory frameworks remain strong. According to several studies, more than 60 % of healthcare professionals have expressed their hesitation in adopting AI systems due to a lack of transparency and fear of data insecurity. Moreover, the 2024 WotNot data breach uncovered weaknesses in AI technologies and highlighted the dire requirement for robust cybersecurity.

DISCUSSION

Full understanding of the potential of AI will be possible only with putting into practice of ethical and technical maintains in healthcare systems. Effective strategies would include integrating bias mitigation methods, strengthening cybersecurity protocols to prevent breaches. Also by adopting interdisciplinary collaboration with the goal of forming transparent regulatory guidelines. These are very important steps toward earning trust and ensuring that AI systems are safe, reliable, and fair.

CONCLUSION

AI can bring transformative opportunities to improve healthcare outcomes, but successful implementation will depend on overcoming the challenges of trust, security, and ethics. Future research should focus on testing these technologies in multiple real-world settings, enhance their scalability, and fine-tune regulations to facilitate accountability. Only by combining technological innovations with ethical principles and strong governance can AI reshape healthcare, ensuring at the same time safety and trustworthiness.

Assessing the efficacy of artificial intelligence to provide peri-operative information for patients with a stoma

BACKGROUND

Stomas present significant lifestyle and psychological challenges for patients, requiring comprehensive education and support. Current educational methods have limitations in offering relevant information to the patient, highlighting a potential role for artificial intelligence (AI). This study examined the utility of AI in enhancing stoma therapy management following colorectal surgery.

MATERIAL AND METHODS

We compared the efficacy of four prominent large language models (LLM)-OpenAI's ChatGPT-3.5 and ChatGPT-4.0, Google's Gemini, and Bing's CoPilot-against a series of metrics to evaluate their suitability as supplementary clinical tools. Through qualitative and quantitative analyses, including readability scores (Flesch-Kincaid, Flesch-Reading Ease, and Coleman-Liau index) and reliability assessments (Likert scale, DISCERN score and QAMAI tool), the study aimed to assess the appropriateness of LLM-generated advice for patients managing stomas.

RESULTS

There are varying degrees of readability and reliability across the evaluated models, with CoPilot and ChatGPT-4 demonstrating superior performance in several key metrics such as readability and comprehensiveness. However, the study underscores the infant stage of LLM technology in clinical applications. All responses required high school to college level education to comprehend comfortably. While the LLMs addressed users' questions directly, the absence of incorporating patient-specific factors such as past medical history generated broad and generic responses rather than offering tailored advice.

CONCLUSION

The complexity of individual patient conditions can challenge AI systems. The use of LLMs in clinical settings holds promise for improving patient education and stoma management support, but requires careful consideration of the models' capabilities and the context of their use.

Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy

BACKGROUND

This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and patients.

METHODS

Six questions about definitive radiotherapy for prostate cancer were designed based on common patient inquiries. These questions were presented to different LLMs [ChatGPT‑4, ChatGPT-4o (both OpenAI Inc., San Francisco, CA, USA), Gemini (Google LLC, Mountain View, CA, USA), Copilot (Microsoft Corp., Redmond, WA, USA), and Claude (Anthropic PBC, San Francisco, CA, USA)] via the respective web interfaces. Responses were evaluated for readability using the Flesch Reading Ease Index. Five radiation oncologists assessed the responses for relevance, correctness, and completeness using a five-point Likert scale. Additionally, 35 prostate cancer patients evaluated the responses from ChatGPT‑4 for comprehensibility, accuracy, relevance, trustworthiness, and overall informativeness.

RESULTS

The Flesch Reading Ease Index indicated that the responses from all LLMs were relatively difficult to understand. All LLMs provided answers that clinicians found to be generally relevant and correct. The answers from ChatGPT‑4, ChatGPT-4o, and Claude AI were also found to be complete. However, we found significant differences between the performance of different LLMs regarding relevance and completeness. Some answers lacked detail or contained inaccuracies. Patients perceived the information as easy to understand and relevant, with most expressing confidence in the information and a willingness to use ChatGPT‑4 for future medical questions. ChatGPT-4's responses helped patients feel better informed, despite the initially standardized information provided.

CONCLUSION

Overall, LLMs show promise as a tool for patient education in prostate cancer radiotherapy. While improvements are needed in terms of accuracy and readability, positive feedback from clinicians and patients suggests that LLMs can enhance patient understanding and engagement. Further research is essential to fully realize the potential of artificial intelligence in patient education.

Prioritizing Trust in Podiatrists' Preference for AI in Supportive Roles Over Diagnostic Roles in Health Care: Qualitative Interview and Focus Group Study

BACKGROUND

As artificial intelligence (AI) evolves, its roles have expanded from helping out with routine tasks to making complex decisions, once the exclusive domain of human experts. This shift is pronounced in health care, where AI aids in tasks ranging from image recognition in radiology to personalized treatment plans, demonstrating the potential to, at times, surpass human accuracy and efficiency. Despite AI's accuracy in some critical tasks, the adoption of AI in health care is a challenge, in part because of skepticism about being able to rely on AI decisions.

OBJECTIVE

This study aimed to identify and delve into more effective and acceptable ways of integrating AI into a broader spectrum of health care tasks.

METHODS

We included 2 qualitative phases to explore podiatrists' views on AI in health care. Initially, we interviewed 9 podiatrists (7 women and 2 men) with a mean age of 41 (SD 12) years and aimed to capture their sentiments regarding the use and role of AI in their work. Subsequently, a focus group with 5 podiatrists (4 women and 1 man) with a mean age of 54 (SD 10) years delved into AI's supportive and diagnostic roles on the basis of the interviews. All interviews were recorded, transcribed verbatim, and analyzed using Atlas.ti and QDA-Miner, using both thematic analysis for broad patterns and framework analysis for structured insights per established guidelines.

RESULTS

Our research unveiled 9 themes and 3 subthemes, clarifying podiatrists' nuanced views on AI in health care. Key overlapping insights in the 2 phases included a preference for using AI in supportive roles, such as triage, because of its efficiency and process optimization capabilities. There is a discernible hesitancy toward leveraging AI for diagnostic purposes, driven by concerns regarding its accuracy and the essential nature of human expertise. The need for transparency and explainability in AI systems emerged as a critical factor for fostering trust in both phases.

CONCLUSIONS

The findings highlight a complex view from podiatrists on AI, showing openness to its application in supportive roles while exercising caution with diagnostic use. This result is consistent with a careful introduction of AI into health care in roles, such as triage, in which there is initial trust, as opposed to roles that ask the AI for a complete diagnosis. Such strategic adoption can mitigate initial resistance, gradually building the confidence to explore AI's capabilities in more nuanced tasks, including diagnostics, where skepticism is currently more pronounced. Adopting AI stepwise could thus enhance trust and acceptance across a broader range of health care tasks, aligning technology integration with professional comfort and patient care standards.

Exploring a decade of deep learning in dentistry: A comprehensive mapping review

OBJECTIVES

Artificial Intelligence (AI), particularly deep learning, has significantly impacted healthcare, including dentistry, by improving diagnostics, treatment planning, and prognosis prediction. This systematic mapping review explores the current applications of deep learning in dentistry, offering a comprehensive overview of trends, models, and their clinical significance.

MATERIALS AND METHODS

Following a structured methodology, relevant studies published from January 2012 to September 2023 were identified through database searches in PubMed, Scopus, and Embase. Key data, including clinical purpose, deep learning tasks, model architectures, and data modalities, were extracted for qualitative synthesis.

RESULTS

From 21,242 screened studies, 1,007 were included. Of these, 63.5% targeted diagnostic tasks, primarily with convolutional neural networks (CNNs). Classification (43.7%) and segmentation (22.9%) were the main methods, and imaging data-such as cone-beam computed tomography and orthopantomograms-were used in 84.4% of cases. Most studies (95.2%) applied fully supervised learning, emphasizing the need for annotated data. Pathology (21.5%), radiology (17.5%), and orthodontics (10.2%) were prominent fields, with 24.9% of studies relating to more than one specialty.

CONCLUSION

This review explores the advancements in deep learning in dentistry, particulary for diagnostics, and identifies areas for further improvement. While CNNs have been used successfully, it is essential to explore emerging model architectures, learning approaches, and ways to obtain diverse and reliable data. Furthermore, fostering trust among all stakeholders by advancing explainable AI and addressing ethical considerations is crucial for transitioning AI from research to clinical practice.

CLINICAL RELEVANCE

This review offers a comprehensive overview of a decade of deep learning in dentistry, showcasing its significant growth in recent years. By mapping its key applications and identifying research trends, it provides a valuable guide for future studies and highlights emerging opportunities for advancing AI-driven dental care.

Chat Generative Pre-Trained Transformer (ChatGPT) in Oral and Maxillofacial Surgery: A Narrative Review on Its Research Applications and Limitations

Objectives: This review aimed to evaluate the role of ChatGPT in original research articles within the field of oral and maxillofacial surgery (OMS), focusing on its applications, limitations, and future directions. Methods: A literature search was conducted in PubMed using predefined search terms and Boolean operators to identify original research articles utilizing ChatGPT published up to October 2024. The selection process involved screening studies based on their relevance to OMS and ChatGPT applications, with 26 articles meeting the final inclusion criteria. Results: ChatGPT has been applied in various OMS-related domains, including clinical decision support in real and virtual scenarios, patient and practitioner education, scientific writing and referencing, and its ability to answer licensing exam questions. As a clinical decision support tool, ChatGPT demonstrated moderate accuracy (approximately 70-80%). It showed moderate to high accuracy (up to 90%) in providing patient guidance and information. However, its reliability remains inconsistent across different applications, necessitating further evaluation. Conclusions: While ChatGPT presents potential benefits in OMS, particularly in supporting clinical decisions and improving access to medical information, it should not be regarded as a substitute for clinicians and must be used as an adjunct tool. Further validation studies and technological refinements are required to enhance its reliability and effectiveness in clinical and research settings.

A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice

Artificial Intelligence (AI) is reshaping healthcare through advancements in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and generate new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions.

Reducing the workload of medical diagnosis through artificial intelligence: A narrative review

Artificial intelligence (AI) has revolutionized medical diagnostics by enhancing efficiency, improving accuracy, and reducing variability. By alleviating the workload of medical staff, AI addresses challenges such as increasing diagnostic demands, workforce shortages, and reliance on subjective interpretation. This review examines the role of AI in reducing diagnostic workload and enhancing efficiency across medical fields from January 2019 to February 2024, identifying limitations and areas for improvement. A comprehensive PubMed search using the keywords "artificial intelligence" or "AI," "efficiency" or "workload," and "patient" or "clinical" identified 2587 articles, of which 51 were reviewed. These studies analyzed the impact of AI on radiology, pathology, and other specialties, focusing on efficiency, accuracy, and workload reduction. The final 51 articles were categorized into 4 groups based on diagnostic efficiency, where category A included studies with supporting material provided, category B consisted of those with reduced data volume, category C focused on independent AI diagnosis, and category D included studies that reported data reduction without changes in diagnostic time. In radiology and pathology, which require skilled techniques and large-scale data processing, AI improved accuracy and reduced diagnostic time by approximately 90% or more. Radiology, in particular, showed a high proportion of category C studies, as digitized data and standardized protocols facilitated independent AI diagnoses. AI has significant potential to optimize workload management, improve diagnostic efficiency, and enhance accuracy. However, challenges remain in standardizing applications and addressing ethical concerns. Integrating AI into healthcare workforce planning is essential for fostering collaboration between technology and clinicians, ultimately improving patient care.

Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis

BACKGROUND

Artificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. Accurate timing of treatment is crucial, but traditional methods such as cervical vertebral maturation (CVM) staging have limitations due to observer variability and complexity. AI has the potential to automate CVM assessment, enhancing reliability and user-friendliness. This systematic review and meta-analysis aimed to evaluate the overall performance of artificial intelligence (AI) models in assessing cervical vertebrae maturation (CVM) in radiographs, when compared to clinicians.

METHODS

Electronic databases of Medline (via PubMed), Google Scholar, Scopus, Embase, IEEE ArXiv and MedRxiv were searched for publications after 2010, without any limitation on language. In the present review, we included studies that reported AI models' performance on CVM assessment. Quality assessment was done using Quality assessment and diagnostic accuracy Tool-2 (QUADAS-2). Quantitative analysis was conducted using hierarchical logistic regression for meta-analysis on diagnostic accuracy. Subgroup analysis was conducted on different AI subsets (Deep learning, and Machine learning).

RESULTS

A total of 1606 studies were screened of which 25 studies were included. The performance of the models was acceptable. However, it varied based on the methods employed. Eight studies had a low risk of bias in all domains. Twelve studies were included in the meta-analysis and their pooled values for sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated for each cervical stage (CS). The most accurate CVM evaluation was observed for CS1, boasting a sensitivity of 0.87, a specificity of 0.97, and a DOR of 213. Conversely, CS3 exhibited the lowest performance with a sensitivity of 0.64, and a specificity of 0.96, yet maintaining a DOR of 32.

CONCLUSION

AI has demonstrated encouraging outcomes in CVM assessment, achieving notable accuracy.

Targeting bacterial transcription factors for infection control: opportunities and challenges

The rising threat of antibiotic resistance in pathogenic bacteria emphasizes the need for new therapeutic strategies. This review focuses on bacterial transcription factors (TFs), which play crucial roles in bacterial pathogenesis. We discuss the regulatory roles of these factors through examples, and we outline potential therapeutic strategies targeting bacterial TFs. Specifically, we discuss the use of small molecules to interfere with TF function and the development of transcription factor decoys, oligonucleotides that compete with promoters for TF binding. We also cover peptides that target the interaction between the bacterial TF and other factors, such as RNA polymerase, and the targeting of sigma factors. These strategies, while promising, come with challenges, from identifying targets to designing interventions, managing side effects, and accounting for changing bacterial resistance patterns. We also delve into how Artificial Intelligence contributes to these efforts and how it may be exploited in the future, and we touch on the roles of multidisciplinary collaboration and policy to advance this research domain.Abbreviations: AI, artificial intelligence; CNN, convolutional neural networks; DTI: drug-target interaction; HTH, helix-turn-helix; IHF, integration host factor; LTTRs, LysR-type transcriptional regulators; MarR, multiple antibiotic resistance regulator; MRSA, methicillin resistant Staphylococcus aureus; MSA: multiple sequence alignment; NAP, nucleoid-associated protein; PROTACs, proteolysis targeting chimeras; RNAP, RNA polymerase; TF, transcription factor; TFD, transcription factor decoying; TFTRs, TetR-family transcriptional regulators; wHTH, winged helix-turn-helix.

The Future of Health Physics: Trends, Challenges, and Innovation

ABSTRACT

This paper offers a comprehensive exploration of the future trajectory of health physics, examining influential factors in external and internal dimensions. External factors include an in-depth analysis of low-dose (10-100 mSv) measurement challenges and priorities, highlighting the transformative potential of biomarkers in solving radiation susceptibility following low-dose exposures. Cutting-edge technologies are at the forefront, with insights into emerging radiation detection tools like plastic scintillators with triple discrimination capabilities and sensors based on plastic scintillation microspheres (PSm) for estimating α and β emitting radionuclides in environmental samples. Remote detection systems using drones, robot dogs, and quantum sensors boasting heightened sensitivity and precision also are discussed. Integrating artificial intelligence (AI) and data analytics emerges as a pivotal element, promising to redefine health physics by minimizing radiation exposure risks. The exploration includes innovative materials for radiation shielding, advancements in virtual reality applications, preparation for radiological protection during armed conflicts, and the ever-evolving landscape of decommissioning health physics. Examining health effects from non-ionizing radiation and analyzing broader contextual factors such as regulatory shifts, geopolitics, and socioeconomic influences adds depth to understanding the external forces leading to the future of health physics. Internally, the paper focuses on the transformative dynamics of health physics education and training, encompassing expanded educational horizons, innovative delivery methods, targeted student outreach strategies, and insights into navigating health physics careers amid a dynamically evolving job market. The discussion unfolds further, focusing on new risk communication strategies, the collaborative potential of interdisciplinary approaches, and the significance of health physics summer schools and consortia for transformative educational paradigms. The objective of this paper is not only to unravel the multifaceted factors shaping the future of health physics but also to foster dialogue and collaboration for the unpredictable yet exciting journey ahead.

Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance

Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central ethical principles in risk assessment and regulatory requirements. This paper explores these concerns and provides a roadmap to how to address these challenges by considering data collection, privacy protection, transparency and accountability, model training, and explainability in artificial intelligence decision making for drug safety surveillance. A number of responsible approaches have been identified including an ethics framework and best practices to enhance artificial intelligence use in healthcare. The document also recognizes some initiatives that have demonstrated the importance of ethics in artificial intelligence pharmacovigilance. Nevertheless, the major needs mentioned in this paper are transparency, accountability, data protection, and fairness, which stress the necessity of collaboration to construct a cognitive framework aimed at integrating ethical artificial intelligence into pharmacovigilance. In conclusion, innovation should be balanced with ethical responsibility to enhance public health outcomes as well as patient safety.

Ethical Concerns of AI in Neurosurgery: A Systematic Review

BACKGROUND

The relentless integration of Artificial Intelligence (AI) into neurosurgery necessitates a meticulous exploration of the associated ethical concerns. This systematic review focuses on synthesizing empirical studies, reviews, and opinion pieces from the past decade, offering a nuanced understanding of the evolving intersection between AI and neurosurgical ethics.

MATERIALS AND METHODS

Following PRISMA guidelines, a systematic review was conducted to identify studies addressing AI in neurosurgery, emphasizing ethical dimensions. The search strategy employed keywords related to AI, neurosurgery, and ethics. Inclusion criteria encompassed empirical studies, reviews, and ethical analyses published in the last decade, with English language restriction. Quality assessment using Joanna Briggs Institute tools ensured methodological rigor.

RESULTS

Eight key studies were identified, each contributing unique insights to the ethical considerations associated with AI in neurosurgery. Findings highlighted limitations of AI technologies, challenges in data bias, transparency, and legal responsibilities. The studies emphasized the need for responsible AI systems, regulatory oversight, and transparent decision-making in neurosurgical practices.

CONCLUSIONS

The synthesis of findings underscores the complexity of ethical considerations in the integration of AI in neurosurgery. Transparent and responsible AI use, regulatory oversight, and mitigation of biases emerged as recurring themes. The review calls for the establishment of comprehensive ethical guidelines to ensure safe and equitable AI integration into neurosurgical practices. Ongoing research, educational initiatives, and a culture of responsible innovation are crucial for navigating the evolving landscape of AI-driven advancements in neurosurgery.

Knowledge, attitude, and perception of orthodontic students, and orthodontists regarding role of artificial intelligence in field of orthodontics-An online cross-sectional survey

BACKGROUND

Artificial intelligence (AI) is an emerging technology in orthodontics. The objective of this survey was to evaluate the knowledge, attitude, and perception (KAP) of orthodontists and postgraduate students regarding the plausible employment of AI within the realm of orthodontics.

METHODS

An observational, cross-sectional, online questionnaire survey was conducted with 440 participants (264 postgraduates and 176 faculty members). The questionnaire was divided into four domains: Part A, focused on sociodemographic characteristics, Part B (eight questions) identifying the basic knowledge of the participants about the use of AI in the field of orthodontics, Part C (six questions) assessing the participants' perceptions of the use of AI, and Part D (five questions) assessing the attitudes of participants towards AI. The KAP scores of the participants regarding the use of AI in the field of orthodontics were assessed using a three-point Likert scale for 17 questions and two multiple-choice questions. Responses were analyzed using the chi-square test, Kruskal-Wallis test, and Mann-Whitney test.

RESULTS

A total of 266 participants completed the survey, and the majority agreed with the use of AI in the field of orthodontics, particularly for 3-dimensional diagnosis of orthognathic surgeries, cephalometric analysis, and prediction of treatment outcomes. Most participants felt that AI training should be incorporated into the postgraduate curriculum (73%), and were willing to incorporate it into clinical practice (74%). Barriers to the use of AI were high costs, lack of technical knowledge, and lack of awareness. The participants' KAP scores showed a weak negative correlation with age, years of experience, and designation.

CONCLUSION

The present study concluded that most of the participants were optimistic about the future of AI in orthodontics. Although most orthodontists and postgraduate students had knowledge of AI, there were many barriers to its use in the field of orthodontics.

A guide to prompt design: foundations and applications for healthcare simulationists

Large Language Models (LLMs) like ChatGPT, Gemini, and Claude gain traction in healthcare simulation; this paper offers simulationists a practical guide to effective prompt design. Grounded in a structured literature review and iterative prompt testing, this paper proposes best practices for developing calibrated prompts, explores various prompt types and techniques with use cases, and addresses the challenges, including ethical considerations for using LLMs in healthcare simulation. This guide helps bridge the knowledge gap for simulationists on LLM use in simulation-based education, offering tailored guidance on prompt design. Examples were created through iterative testing to ensure alignment with simulation objectives, covering use cases such as clinical scenario development, OSCE station creation, simulated person scripting, and debriefing facilitation. These use cases provide easy-to-apply methods to enhance realism, engagement, and educational alignment in simulations. Key challenges associated with LLM integration, including bias, privacy concerns, hallucinations, lack of transparency, and the need for robust oversight and evaluation, are discussed alongside ethical considerations unique to healthcare education. Recommendations are provided to help simulationists craft prompts that align with educational objectives while mitigating these challenges. By offering these insights, this paper contributes valuable, timely knowledge for simulationists seeking to leverage generative AI's capabilities in healthcare education responsibly.

AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders

Introduction

Evaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed.

Objectives

This paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms.

Methodology

The proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient's specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers.

Anticipated results

The AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient's cognitive and emotional state, as well as potential compensatory strategies.

Conclusions

By integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders.

Artificial Intelligence-Driven Translation Tools in Intensive Care Units for Enhancing Communication and Research

There is a need to improve communication for patients and relatives who belong to cultural minority communities in intensive care units (ICUs). As a matter of fact, language barriers negatively impact patient safety and family participation in the care of critically ill patients, as well as recruitment to clinical trials. Recent studies indicate that Google Translate and ChatGPT are not accurate enough for advanced medical terminology. Therefore, developing and implementing an ad hoc machine translation tool is essential for bridging language barriers. This tool would enable language minority communities to access advanced healthcare facilities and innovative research in a timely and effective manner, ensuring they receive the comprehensive care and information they need.

METHOD

Key factors that facilitate access to advanced health services, in particular ICUs, for language minority communities are reviewed.

RESULTS

The existing digital communication tools in emergency departments and ICUs are reviewed. To the best of our knowledge, no AI English/French translation app has been developed for deployment in ICUs. Patient privacy and data confidentiality are other important issues that should be addressed.

CONCLUSIONS

Developing an artificial intelligence-driven translation tool for intensive care units (AITIC) which uses language models trained with medical/ICU terminology datasets could offer fast and accurate real-time translation. An AITIC could support communication, and consolidate and expand original research involving language minority communities.

The Role of Artificial Intelligence and Emerging Technologies in Advancing Total Hip Arthroplasty

Total hip arthroplasty (THA) is a widely performed surgical procedure that has evolved significantly due to advancements in artificial intelligence (AI) and robotics. As demand for THA grows, reliable tools are essential to enhance diagnosis, preoperative planning, surgical precision, and postoperative rehabilitation. AI applications in orthopedic surgery offer innovative solutions, including automated hip osteoarthritis (OA) diagnosis, precise implant positioning, and personalized risk stratification, thereby improving patient outcomes. Deep learning models have transformed OA severity grading and implant identification by automating traditionally manual processes with high accuracy. Additionally, AI-powered systems optimize preoperative planning by predicting the hip joint center and identifying complications using multimodal data. Robotic-assisted THA enhances surgical precision with real-time feedback, reducing complications such as dislocations and leg length discrepancies while accelerating recovery. Despite these advancements, barriers such as cost, accessibility, and the steep learning curve for surgeons hinder widespread adoption. Postoperative rehabilitation benefits from technologies like virtual and augmented reality and telemedicine, which enhance patient engagement and adherence. However, limitations, particularly among elderly populations with lower adaptability to technology, underscore the need for user-friendly platforms. To ensure comprehensiveness, a structured literature search was conducted using PubMed, Scopus, and Web of Science. Keywords included "artificial intelligence", "machine learning", "robotics", and "total hip arthroplasty". Inclusion criteria emphasized peer-reviewed studies published in English within the last decade focusing on technological advancements and clinical outcomes. This review evaluates AI and robotics' role in THA, highlighting opportunities and challenges and emphasizing further research and real-world validation to integrate these technologies into clinical practice effectively.

Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance

Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning and deep learning assist in pathogen detection, resistance prediction, and drug discovery. These tools improve antibiotic stewardship and identify effective compounds such as antimicrobial peptides and small molecules. This review explores AI applications in diagnostics, therapy, and drug discovery, emphasizing both strengths and areas needing improvement.

Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making

Artificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a frontrunner beneficiary. This article explores the growing impact of AI and Machine Learning (ML) within pharmaceutical Regulatory Affairs, particularly in dossier preparation, compilation, documentation, submission, review, and regulatory compliance. By automating time-intensive tasks, these technologies streamline workflows, accelerate result generation, and shorten the product approval timeline. However, despite their immense potential, AI and ML also introduce new challenges. Issues such as AI software validation, data management security and privacy, potential biases, ethical concerns, and change management requirements must be addressed. This review highlights current AI-based tools actively used by regulatory professionals such as DocShifter, Veeva Vault, RiskWatch, Freyr SubmitPro, Litera Microsystems, cortical.io etc., examines both the benefits and obstacles of integrating these advanced systems into regulatory practices. Given the rapid pace of technological innovation, the article underscores the need for proactive collaboration with regulatory bodies to manage these developments. It also stresses the importance of adapting to evolving regulatory frameworks and embracing new technologies. Although regulatory agencies like the United Sates Food and Drug Administration (USFDA), European Medicines Agency (EMA), and Medicines and Healthcare products Regulatory Agency (MHRA) are working on guidelines for AI and ML adoption, clear, standardized protocols are still in the works. While the journey ahead may be complex, the integration of AI promises to fundamentally reshape regulatory processes and accelerate the approval of safe, effective pharmaceutical products.

Analysis of total RNA as a potential biomarker of Parkinson's disease in silico

Knowledge about total RNA molecules in Parkinson's disease is limited. This gene expression profiling study was conducted with a preclinical experimental design using a mouse model to examine the molecular-biological characteristics and the pathological implication of total RNA gene interaction in Parkinson's disease in silico. In silico analysis of total RNA molecules, the Gene Expression Omnibus database, published results, and preliminary findings of available patient samples apply. The potential signaling network and the effect of the interaction of molecules with total RNA was predicted and confirmed. The research consists of four parts. At first, we analyzed the control and MPTP groups. In the second part, we analyzed FVB-N control and MPTP. In the third part, we analyzed controls. In the fourth part, we analyzed MTPT separately. The constructed network contains total RNA, where the Kyoto Encyclopedia of Genes and Genomes database analysis showed that genes from the signaling pathway are involved in the development and complications of Parkinson's disease in male and female rats. Identified total RNA and genes are involved in altered signaling. There is direct interconnection and interdependence of interactions in the signaling network. Results identified the significant total-RNA molecules of the signaling pathway that connect other molecules. In silico analysis shows upregulated and downregulated genes in Parkinson's disease rats. Preliminary data shows that total RNA molecules interact with other genes, and they are applicable in Parkinson's disease course monitoring, shedding light on how factors impact the expression of genes and offering strategies for management.

Navigating the legal complexities of telesurgery in China: An assessment of tort liability and the path forward

This study investigates the legal challenges posed by telesurgery, an emergent healthcare modality facilitated by advancements in 5G and Artificial Intelligence. It highlights the urgent need for a comprehensive legal framework reconciling the complexities of healthcare delivery and technology integration. The paper examines the Chinese adjudication of negligence and the evidentiary hurdles in telesurgery, interrogating the application of the 'reasonable doctor' standard, the intricate causation-negligence nexus and the distribution of evidentiary burdens. The analysis contends that current statutes require revision to apportion telesurgery-induced damages fairly. Further, it proposes the formation of multidisciplinary committees to oversee medical technology, calls for systemic reforms, more reasonable liability differentiation and fortifying medical insurance frameworks.

Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee

OBJECTIVES

Artificial intelligence (AI) represents an exciting and evolving technology that is increasingly being utilized across pain medicine. Large language models (LLMs) are one type of AI that has become particularly popular. Currently, there is a paucity of literature analyzing the impact that AI may have on trainee education. As such, we sought to assess the benefits and pitfalls that AI may have on pain medicine trainee education. Given the rapidly increasing popularity of LLMs, we particularly assessed how these LLMs may promote and hinder trainee education through a pilot quality improvement project.

MATERIALS AND METHODS

A comprehensive search of the existing literature regarding AI within medicine was performed to identify its potential benefits and pitfalls within pain medicine. The pilot project was approved by UPMC Quality Improvement Review Committee (#4547). Three of the most commonly utilized LLMs at the initiation of this pilot study - ChatGPT Plus, Google Bard, and Bing AI - were asked a series of multiple choice questions to evaluate their ability to assist in learner education within pain medicine.

RESULTS

Potential benefits of AI within pain medicine trainee education include ease of use, imaging interpretation, procedural/surgical skills training, learner assessment, personalized learning experiences, ability to summarize vast amounts of knowledge, and preparation for the future of pain medicine. Potential pitfalls include discrepancies between AI devices and associated cost-differences, correlating radiographic findings to clinical significance, interpersonal/communication skills, educational disparities, bias/plagiarism/cheating concerns, lack of incorporation of private domain literature, and absence of training specifically for pain medicine education. Regarding the quality improvement project, ChatGPT Plus answered the highest percentage of all questions correctly (16/17). Lowest correctness scores by LLMs were in answering first-order questions, with Google Bard and Bing AI answering 4/9 and 3/9 first-order questions correctly, respectively. Qualitative evaluation of these LLM-provided explanations in answering second- and third-order questions revealed some reasoning inconsistencies (e.g., providing flawed information in selecting the correct answer).

CONCLUSIONS

AI represents a continually evolving and promising modality to assist trainees pursuing a career in pain medicine. Still, limitations currently exist that may hinder their independent use in this setting. Future research exploring how AI may overcome these challenges is thus required. Until then, AI should be utilized as supplementary tool within pain medicine trainee education and with caution.

Leveraging Artificial Intelligence to Predict and Manage Complications in Patients With Multimorbidity: A Literature Review

Artificial intelligence (AI) is revolutionizing healthcare by improving diagnostic accuracy, streamlining treatment protocols, and augmenting patient care, especially in the management of multimorbidity. This review assesses the applications of AI in forecasting and controlling problems in multimorbid patients, emphasizing predictive analytics, real-time data integration, and enhancements in diagnostics. Utilizing extensive datasets from electronic health records and medical imaging, AI models facilitate early complication prediction and prompt therapies in diseases such as cancer, cardiovascular disorders, and diabetes. Notable developments encompass AI systems for the diagnosis of lung and breast cancer, markedly decreasing false positives and minimizing superfluous follow-ups. A comprehensive literature search was performed via PubMed and Google Scholar, applying Boolean logic with keywords such as "artificial intelligence", "multimorbidity", "predictive analytics", "machine learning", and "diagnosis". Articles published in English from January 2010 to December 2024, encompassing original research, systematic reviews, and meta-analyses regarding the use of AI in managing multimorbidity and healthcare decision-making, were included. Studies not pertinent to therapeutic applications, devoid of outcome measurements, or restricted to editorials were discarded. This review emphasizes AI's capacity to augment diagnostic precision and boost clinical results while also identifying substantial hurdles, including data bias, ethical issues, and the necessity for rigorous validation and longitudinal research to guarantee sustainable integration in clinical environments. This review's limitations encompass the possible exclusion of pertinent studies due to language and publication year constraints, as well as the disregard for grey literature, potentially constraining the comprehensiveness of the findings.

Ethics: The Elixir of Publications

Scientific papers are the driving force for research, information dissemination, and policymaking that directly impacts society. Thus, ethical practices are the elixir of publications. Adherence to ethical practices promotes integrity in research and publication. Transgression of ethics is thus considered a poison to science. Although there is no definition of ethics, it includes a systematic approach that not only recommends but also defends and protects concepts of the conduct of right and wrong. Therefore, ethical principles should be strictly adhered to and upheld at any cost for the progression of science. This article addresses various actions that are considered ethical misconduct and guidelines to fix them. Recommendations of various organizations related to the ethics in publications are also discussed.

Exploring artificial intelligence for healthcare from the health professionals' perspective: The case of limited resource settings

Introduction

Although artificial intelligence (AI) can boost clinical decision-making, personalize patient treatment, and advance the global health sectors, there are unique implementation challenges and considerations in developing countries. The perceptions, attitudes, and behavioral factors among the users are limitedly identified in Ethiopia.

Objective

This study aimed to explore AI in healthcare from the perspectives of health professionals in a resource-limited setting.

Methods

We employed a cross-sectional descriptive study including 404 health professionals. Data were collected using a self-structured questionnaire. A simple random sampling technique was applied. We used SPSS to analyze data. Tables and graphs were used to present the findings.

Results

A 95.7% response rate was reported. The mean age of the respondents was 32.57 ± 5.34 SD. Almost 254 (62.9%) of the participants were Bachelors of Science degree holders. Nearly 156 (38.6%) of the participants were medical doctors. More than 50% (52.2%) of them said AI would be applicable for diagnosis and treatment purposes in healthcare organizations. This study identified that a favorable attitude, good knowledge, and formal training regarding AI technologies would foster clinical decision-making practices more efficiently and accurately. Similarly, our study also identified the potential barriers to AI technologies in healthcare such as ethical issues, privacy and security of patient data were some to mention.

Conclusions

Our study revealed that positive attitude, good knowledge, and formal training are crucial to advance healthcare using AI technologies. In addition, this study identified self-reported AI concerns of the participants such as; privacy and security of data, ethical issues, and accuracy of AI systems. Attention could be given to overcome the barriers of AI systems in the health system. Providing training, allocating time to practice AI tools, incorporating AI courses in the curricula of medical education, and improving knowledge can further the usage of AI systems in healthcare settings.

Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review

BACKGROUND

Recent healthcare advancements highlight the potential of Artificial Intelligence (AI) - and especially, among its subfields, Machine Learning (ML) - in enhancing Breast Cancer (BC) clinical care, leading to improved patient outcomes and increased radiologists' efficiency. While medical imaging techniques have significantly contributed to BC detection and diagnosis, their synergy with AI algorithms has consistently demonstrated superior diagnostic accuracy, reduced False Positives (FPs), and enabled personalized treatment strategies. Despite the burgeoning enthusiasm for leveraging AI for early and effective BC clinical care, its widespread integration into clinical practice is yet to be realized, and the evaluation of AI-based health technologies in terms of health and economic outcomes remains an ongoing endeavor.

OBJECTIVES

This scoping review aims to investigate AI (and especially ML) applications that have been implemented and evaluated across diverse clinical tasks or decisions in breast imaging and to explore the current state of evidence concerning the assessment of AI-based technologies for BC clinical care within the context of Health Technology Assessment (HTA).

METHODS

We conducted a systematic literature search following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist in PubMed and Scopus to identify relevant studies on AI (and particularly ML) applications in BC detection and diagnosis. We limited our search to studies published from January 2015 to October 2023. The Minimum Information about CLinical Artificial Intelligence Modeling (MI-CLAIM) checklist was used to assess the quality of AI algorithms development, evaluation, and reporting quality in the reviewed articles. The HTA Core Model® was also used to analyze the comprehensiveness, robustness, and reliability of the reported results and evidence in AI-systems' evaluations to ensure rigorous assessment of AI systems' utility and cost-effectiveness in clinical practice.

RESULTS

Of the 1652 initially identified articles, 104 were deemed eligible for inclusion in the review. Most studies examined the clinical effectiveness of AI-based systems (78.84%, n= 82), with one study focusing on safety in clinical settings, and 13.46% (n=14) focusing on patients' benefits. Of the studies, 31.73% (n=33) were ethically approved to be carried out in clinical practice, whereas 25% (n=26) evaluated AI systems legally approved for clinical use. Notably, none of the studies addressed the organizational implications of AI systems in clinical practice. Of the 104 studies, only two of them focused on cost-effectiveness analysis, and were analyzed separately. The average percentage scores for the first 102 AI-based studies' quality assessment based on the MI-CLAIM checklist criteria were 84.12%, 83.92%, 83.98%, 74.51%, and 14.7% for study design, data and optimization, model performance, model examination, and reproducibility, respectively. Notably, 20.59% (n=21) of these studies relied on large-scale representative real-world breast screening datasets, with only 10.78% (n =11) studies demonstrating the robustness and generalizability of the evaluated AI systems.

CONCLUSION

In bridging the gap between cutting-edge developments and seamless integration of AI systems into clinical workflows, persistent challenges encompass data quality and availability, ethical and legal considerations, robustness and trustworthiness, scalability, and alignment with existing radiologists' workflow. These hurdles impede the synthesis of comprehensive, robust, and reliable evidence to substantiate these systems' clinical utility, relevance, and cost-effectiveness in real-world clinical workflows. Consequently, evaluating AI-based health technologies through established HTA methodologies becomes complicated. We also highlight potential significant influences on AI systems' effectiveness of various factors, such as operational dynamics, organizational structure, the application context of AI systems, and practices in breast screening or examination reading of AI support tools in radiology. Furthermore, we emphasize substantial reciprocal influences on decision-making processes between AI systems and radiologists. Thus, we advocate for an adapted assessment framework specifically designed to address these potential influences on AI systems' effectiveness, mainly addressing system-level transformative implications for AI systems rather than focusing solely on technical performance and task-level evaluations.