Diagnostic Artificial Intelligence Research Articles

An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm

Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.

Does ChatGPT-4 succeed in the ECG interpretation: friend or foe to cardiologists?

Abstract Background/Introduction Artificial intelligence (AI) and large language models (LLMs) are increasingly being tested for medical purposes, especially in clinical settings and one of the most well-known LLMs is OpenAI’s ChatGPT-4. Recent explorations have considered using ChatGPT to answer medical questions, and the new ChatGPT-4 also allows graphical inputs, which could aid medical doctors in interpreting electrocardiograms (ECGs) in everyday practice. AI-powered ECG interpretation has shown promising results, improving the detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities. However, whether ChatGPT4’s interpretation of ECGs is comparable to that of experienced cardiologists has yet to be established. Purpose We sought to test whether ChatGPT-4 could analyze and interpret ECGs similarly to experienced cardiologists. Methods We tasked ChatGPT-4 with interpreting, for scientific purposes, 12-lead ECGs, from 150 patients included in our prospective CaRD registry, presenting with various cardiac pathologies, including acute coronary syndrome, arrhythmias (primarily atrial fibrillation), conduction abnormalities, pacemaker rhythms and normal ECGs. Four experienced cardiologists reviewed the ECG interpretations provided by ChatGPT-4. The model provided information on rate, rhythm, axis, P-wave, PR interval, QRS complex, ST segment, T wave, differential diagnoses, most likely diagnosis and further diagnostic step. Results In this observational study, ChatGPT-4 demonstrated low overall accuracy in interpreting ECGs, with only 24% of the most likely diagnosis being correct. ChatGPT-4 performed the best when interpreting normal ECGs with sinus rhythm, achieving 62% accuracy rate, followed by 17%, 10% and 7.3% for ECGs with arrhythmias, acute ischemic changes, and conduction abnormalities or pacing rhythms, respectively. Conclusion ChatGPT-4 exhibited very low accuracy in interpreting different categories of pathology in 12-lead ECG, yielding less than 20% success rate. While the use of AI in medical diagnostics is an appealing concept in theory, the results suggest that, in its current state, ChatGPT-4 would not provide significant aid to experienced cardiologists in clinical settings.

Computer-aided immunofluorescence microscopy in antinuclear antibody testing; increasing role of artificial intelligence in diagnostics

Computer-aided immunofluorescence microscopy in antinuclear antibody testing; increasing role of artificial intelligence in diagnostics

Artificial intelligence in diagnostics of genetic diseases

Artificial intelligence is helping scientists using computing software, new programs and algorithms to identify the DNA variants in our genomes and misspellings in human DNA that are most likely to cause disease. These predictions may help to diagnose even uncommon diseases more quickly and provide guidance for the creation of new drugs and a succesful treatment

The impact of artificial intelligence on medical diagnostics: A letter to the editor

Madam, Artificial intelligence (AI) describes the generation of intelligent machines that are capable of carrying out activities that usually call for human intellect. (1) It involves creating algorithms and models that enable computers to learn from and analyze vast amounts of data, recognize patterns, make decisions, and even engage in natural language processing. (2) AI has become increasingly important in various fields due to its potential to automate complex processes, enhance efficiency, and drive innovation. It can transform industries, improve decision-making, and revolutionize how we live and work. In medical diagnostics, AI has had a profound impact on improving accuracy, efficiency, and patient outcomes. By leveraging machine learning algorithms, AI systems can process and analyze large volumes of medical data, including patient records, laboratory results, medical images, and genomic data. This allows for more accurate and timely diagnoses, as AI algorithms can detect subtle patterns and anomalies that may not be visible to human observers. AI-powered diagnostics can assist healthcare professionals in detecting diseases, predicting patient outcomes, and designing personalized treatment plans. (3) Furthermore, AI can contribute to the early detection of diseases, thereby enabling timely interventions and improving patient survival rates. For example, AI algorithms analyze medical imaging data, such as X-rays, MRIs, and CT scans, to detect abnormalities and potential signs of diseases like cancer. This helps radiologists and clinicians identify and diagnose conditions early when treatment options are more effective. (4) Additionally, AI aids in interpreting genetic data, allowing for the identification of genetic markers associated with specific diseases or drug responses. This information guides clinicians in selecting the most suitable treatment options for individual patients, leading to improved therapeutic outcomes. (5) AI in medical diagnostics also faces hurdles with data privacy and accessibility, as patient information must be handled securely. Ensuring AI algorithms are unbiased, and fair is a challenge to avoid disparities in healthcare outcomes. The interpretability of AI models is vital to building trust with healthcare professionals and facilitating their adoption. Regulatory compliance and ethical considerations are also significant factors that need to be addressed for the responsible and effective integration of AI in the medical field. In conclusion, Artificial Intelligence (AI) is a transformative technology with the potential to revolutionize various industries, including medical diagnostics. Its ability to analyze vast amounts of data, recognize patterns, and make intelligent decisions has proven invaluable in improving diagnostic processes accuracy, efficiency, and effectiveness. ---Continue

Artificial intelligence in kidney transplant pathology

Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology. Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide afuture outlook. Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, aselection was made of studies that have ahistopathological focus and use AI to improve kidney transplant diagnostics. Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for akidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely.

Evaluating ChatGPT-4's Diagnostic Accuracy: Impact of Visual Data Integration.

In the evolving field of health care, multimodal generative artificial intelligence (AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant advancement, as they integrate visual data with text data. This integration has the potential to revolutionize clinical diagnostics by offering more comprehensive analysis capabilities. However, the impact on diagnostic accuracy of using image data to augment ChatGPT-4 remains unclear. This study aims to assess the impact of adding image data on ChatGPT-4's diagnostic accuracy and provide insights into how image data integration can enhance the accuracy of multimodal AI in medical diagnostics. Specifically, this study endeavored to compare the diagnostic accuracy between ChatGPT-4V, which processed both text and image data, and its counterpart, ChatGPT-4, which only uses text data. We identified a total of 557 case reports published in the American Journal of Case Reports from January 2022 to March 2023. After excluding cases that were nondiagnostic, pediatric, and lacking image data, we included 363 case descriptions with their final diagnoses and associated images. We compared the diagnostic accuracy of ChatGPT-4V and ChatGPT-4 without vision based on their ability to include the final diagnoses within differential diagnosis lists. Two independent physicians evaluated their accuracy, with a third resolving any discrepancies, ensuring a rigorous and objective analysis. The integration of image data into ChatGPT-4V did not significantly enhance diagnostic accuracy, showing that final diagnoses were included in the top 10 differential diagnosis lists at a rate of 85.1% (n=309), comparable to the rate of 87.9% (n=319) for the text-only version (P=.33). Notably, ChatGPT-4V's performance in correctly identifying the top diagnosis was inferior, at 44.4% (n=161), compared with 55.9% (n=203) for the text-only version (P=.002, χ2 test). Additionally, ChatGPT-4's self-reports showed that image data accounted for 30% of the weight in developing the differential diagnosis lists in more than half of cases. Our findings reveal that currently, ChatGPT-4V predominantly relies on textual data, limiting its ability to fully use the diagnostic potential of visual information. This study underscores the need for further development of multimodal generative AI systems to effectively integrate and use clinical image data. Enhancing the diagnostic performance of such AI systems through improved multimodal data integration could significantly benefit patient care by providing more accurate and comprehensive diagnostic insights. Future research should focus on overcoming these limitations, paving the way for the practical application of advanced AI in medicine.

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging.

Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in pulmonary diagnostics. This comprehensive review explores the impact of AI on revolutionizing lung imaging, focusing on its applications in detecting abnormalities, diagnosing pulmonary conditions, and predicting disease prognosis. We provide an overview of traditional pulmonary diagnosticmethods and highlight the importance of accurate and efficient lung imaging for early intervention and improved patient outcomes. Through the lens of AI, we examine machine learning algorithms, deep learning techniques, and natural language processing for analyzing radiology reports. Case studies and examples showcase the successful implementation of AI in pulmonary diagnostics, alongside challenges faced and lessons learned. Finally, we discuss future directions, including integrating AI into clinical workflows, ethical considerations, and the need for further research and collaboration in this rapidly evolving field. This review underscores the transformative potential of AI in enhancing the accuracy, efficiency, and accessibility of pulmonary healthcare.

Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study

BackgroundDiagnosing mediastinal tumours, including incidental lesions, using low-dose CT (LDCT) performed for lung cancer screening, is challenging. It often requires additional invasive and costly tests for proper characterisation and surgical...

Patients' Perceptions and Attitudes to the Use of Artificial Intelligence in Breast Cancer Diagnosis: A Narrative Review.

Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients' attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients' perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists' expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.

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

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

Ethics and artificial intelligence

The introduction of artificial intelligence (AI) into radiology promises to enhance efficiency and improve diagnostic accuracy, yet it also raises manifold ethical questions. These include data protection issues, the future role of radiologists, liability when using AI systems, and the avoidance of bias. To prevent data bias, the datasets need to be compiled carefully and to be representative of the target population. Accordingly, the upcoming European Union AI act sets particularly high requirements for the datasets used in training medical AI systems. Cognitive bias occurs when radiologists place too much trust in the results provided by AI systems (overreliance). So far, diagnostic AI systems are used almost exclusively as "second look" systems. If diagnostic AI systems are to be used in the future as "first look" systems or even as autonomous AI systems in order to enhance efficiency in radiology, the question of liability needs to be addressed, comparable to liability for autonomous driving. Such use of AI would also significantly change the role of radiologists.

Men's sociotechnical imaginaries of artificial intelligence for prostate cancer diagnostics – A focus group study

Artificial intelligence (AI) is increasingly used for diagnostic purposes in cancer care. Prostate cancer is one of the most prevalent cancers affecting men worldwide, but current diagnostic approaches have limitations in terms of specificity and sensitivity. Using AI to interpret MR images in prostate cancer diagnostics shows promising results, but raises questions about implementation, user acceptance, trust, and doctor-patient communication. Drawing on approaches from the sociology of expectations and theories about sociotechnical imaginaries, we explore men's expectations of artificial intelligence for prostate cancer diagnostics. We conducted ten focus groups with 48 men aged 54–85 in Norway with various experiences of prostate cancer diagnostics. Five groups of men had been treated for prostate cancer, one group was on active surveillance, two groups had been through prostate cancer diagnostics without having a diagnosis, and two groups of men had no experience with prostate cancer diagnostics or treatment. Data was subject to reflexive thematic analysis. Our analysis suggests that men's expectations of AI for prostate cancer diagnostics come from two perspectives: Technology-centered expectations that build on their conceptions of AI's form and agency, and human-centered expectations of AI that build on their perceptions of patient-professional relationships and decision-making processes. These two perspectives are intertwined in three imaginaries of AI: The tool imaginary, the advanced machine imaginary, and the intelligence imaginary – each carrying distinct expectations and ideas of technologies and humans' role in decision-making processes. These expectations are multifaceted and simultaneously optimistic and pessimistic; while AI is expected to improve the accuracy of cancer diagnoses and facilitate more personalized medicine, AI is also expected to threaten interpersonal and communicational relationships between patients and healthcare professionals, and the maintenance of trust in these relationships. This emphasizes how AI cannot be implemented without caution about maintaining human healthcare relationships.

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis,and data privacy.

Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilitiesand limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning-based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluateor lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP-based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AIand (3) data privacy. Deep learning-based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision-making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before. Level of Evidence: Level IV.

A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image

The potential of technology to revolutionize healthcare is exemplified by the synergy between artificial intelligence (AI) and early detection of cardiomegaly, demonstrating the power of proactive intervention in cardiovascular health. This paper presents an innovative approach that leverages advanced AI algorithms, specifically deep learning (DL) technology, for the early detection of cardiomegaly. The methodology consists of five key steps, including data collection, image preprocessing, data augmentation, feature extraction, and classification. Utilizing chest X-ray (CXR) images from the National Institutes of Health (NIH), the study applies rigorous image preprocessing operations, including color transformation and normalization. To enhance model generalization, data augmentation is employed, paving the way for two distinct DL models, a convolutional neural network (CNN) developed from scratch and a pretrained residual network with 50 layers (ResNet50), and adapted to the problem domain. Both models are systematically evaluated with five optimizers, revealing the AdaMax optimizer’s superiority for the CNN model and AdaGrad’s efficacy for the modified ResNet50. The proposed CNN with AdaMax achieves an impressive 99.91% accuracy, outperforming recent techniques in precision, recall, and F1−score. This research underscores the transformative potential of AI in cardiovascular health diagnostics, emphasizing the significance of timely intervention.

Transparency in AI Decision Making: A Survey of Explainable AI Methods and Applications

Artificial Intelligence (AI) systems have become pervasive in numerous facets of modern life, wielding considerable influence in critical decision-making realms such as healthcare, finance, criminal justice, and beyond. Yet, the inherent opacity of many AI models presents significant hurdles concerning trust, accountability, and fairness. To address these challenges, Explainable AI (XAI) has emerged as a pivotal area of research, striving to augment the transparency and interpretability of AI systems. This survey paper serves as a comprehensive exploration of the state-of-the-art in XAI methods and their practical applications. We delve into a spectrum of techniques, spanning from model-agnostic approaches to interpretable machine learning models, meticulously scrutinizing their respective strengths, limitations, and real-world implications. The landscape of XAI is rich and varied, with diverse methodologies tailored to address different facets of interpretability. Model-agnostic approaches offer versatility by providing insights into model behavior across various AI architectures. In contrast, interpretable machine learning models prioritize transparency by design, offering inherent understandability at the expense of some predictive performance. Layer-wise Relevance Propagation (LRP) and attention mechanisms delve into the inner workings of neural networks, shedding light on feature importance and decision processes. Additionally, counterfactual explanations open avenues for exploring what-if scenarios, elucidating the causal relationships between input features and model outcomes. In tandem with methodological exploration, this survey scrutinizes the deployment and impact of XAI across multifarious domains. Successful case studies showcase the practical utility of transparent AI in healthcare diagnostics, financial risk assessment, criminal justice systems, and more. By elucidating these use cases, we illuminate the transformative potential of XAI in enhancing decision-making processes while fostering accountability and fairness. Nevertheless, the journey towards fully transparent AI systems is fraught with challenges and opportunities. As we traverse the current landscape of XAI, we identify pressing areas for further research and development. These include refining interpretability metrics, addressing the scalability of XAI techniques to complex models, and navigating the ethical dimensions of transparency in AI decision-making.Through this survey, we endeavor to cultivate a deeper understanding of transparency in AI decision-making, empowering stakeholders to navigate the intricate interplay between accuracy, interpretability, and ethical considerations. By fostering interdisciplinary dialogue and inspiring collaborative innovation, we aspire to catalyze future advancements in Explainable AI, ultimately paving the way towards more accountable and trustworthy AI systems.

Intelligenza Artificiale per la cura del diabete mellito: opportunità e prospettive future

Diabetes mellitus is globally prevalent, with the International Diabetes Federation predicting 783 million cases by 2045. Type 2 diabetes (T2DM) is the seventh leading cause of death and poses a significant economic burden on global healthcare, demanding innovative approaches amid resource constraints. Moreover, the increasing volume of data requires advanced computational tools for analysis and therapeutic decision support. Digital technologies, particularly artificial intelligence (AI), offer promising solutions. AI, capable of complex tasks like reasoning and language recognition, leverages digitalization in diabetes care, handling electronic health records, imaging diagnostics, and laboratory data. Machine learning, an advanced form of AI, outperforms traditional data analysis by automatically identifying patterns and generating predictions without specific programming. Emerging technologies utilizing natural language processing and machine learning accelerate the digital transformation, enhancing cognitive capabilities. AI applications in diabetes span screening, classification, and therapy management. Predictive models utilizing machine learning algorithms achieve high accuracy in early diabetes risk identification and aids in understanding T2DM heterogeneity, crucial for precision medicine initiatives. Diagnostic AI tools, particularly for retinopathy, neuropathy, and diabetic foot, enhance accessibility and clinical decision-making. AI’s role extends to predicting and preventing complications, such as ulcers and hypoglycemic events. Challenges include data privacy concerns, algorithmic bias, and the need for transparent decision-making processes. Rigorous optimization of AI data inputs is crucial to avoid misleading outputs. While AI offers tremendous benefits, ethical considerations, regulatory frameworks, and human oversight remain critical for responsible and effective implementation in diabetes care.

Identification of Skin Lesions by Snapshot Hyperspectral Imaging.

This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain. The potential of AI and HSI in dermatological diagnostics was underscored, offering a noninvasive, efficient, and accurate alternative to traditional methods. The findings are particularly crucial for early-stage invasive lesion detection in MF, showcasing the model's robust performance in segmenting and classifying lesions and its superior predictive accuracy validated through k-fold cross-validation. The model attained its optimal performance with a k-fold cross-validation value of 7, achieving a sensitivity of 90.72%, a specificity of 96.76%, an F1-score of 90.08%, and an ROC-AUC of 0.9351. This study marks a substantial advancement in dermatological diagnostics, thereby contributing significantly to the early and precise identification of skin malignancies and inflammatory conditions.

Вплив сучасних тенденцій у медичному менеджменті на підбір і розвиток управлінського персоналу: аналіз і перспективи

The modern healthcare environment is subject to rapid transformations due to technological progress, the introduction of electronic management systems, changes in the funding structure, etc. These aspects significantly affect medical management and require appropriate personnel to effectively manage these changes. The dynamic nature of the development of the medical field makes it necessary to improve the qualifications of medical personnel at all levels. Management in the field of healthcare requires specific knowledge and skills. The growing volume of work and the constant challenges associated with the treatment of new diseases require qualified managers who can effectively cope with various tasks. The introduction of conceptions such as the patient-centered model or other innovative approaches may require a review of the structure and management strategies of healthcare organizations. As a result, staff are needed who can effectively adapt to the new realities. With the emergence of new technologies such as telemedicine, artificial intelligence in diagnostics, and the development of medical applications and management systems, managers need to be innovation-oriented and ensure that innovations are implemented in medical practice. Technological innovations in the healthcare sector are closely linked to globalization and the exchange of experience. The growing global interdependence of the healthcare industry requires managers to understand not only national but also international aspects of healthcare management. This approach allows to reveal current problems, propose effective strategies for responding to challenges and identify key areas for the development of management personnel in healthcare management. The purpose of the article is to identify current trends in various forms of personnel policy in medical management, their advantages and disadvantages, and to consider innovations in this area aimed at optimizing personnel management in a modern medical enterprise.