For couples encountering infertility challenges, assisted reproductive technologies (ARTs) offer a path to parenthood. ART procedures, such as in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and embryo implantation, involve the handling of sperm or embryos outside the body. However, the success of ART depends on the accurate selection of viable embryos. Artificial intelligence (AI) is a promising tool with the potential to revolutionize these procedures. This review explores the transformative potential of AI in ART, providing valuable insights into enhanced embryo selection and unlocking new possibilities for the field. Four electronic databases were systematically searched under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From an initial pool of 914 papers, 30 studies were selected for further evaluation. While noting the limitations inherent in the existing body of research, this review offers a broad analysis of AI’s transformative role in embryo selection. It highlights the significant potential of AI to enhance precision, consistency, and efficiency in ART. This review also emphasizes the importance of addressing technical, ethical, and regulatory aspects to ensure responsible and effective integration of these technologies. The findings indicate that AI-based models, such as the iDAScore v2.0, have demonstrated promising results in accurately predicting embryo viability and evaluating the effects of maternal age on embryo viability. Specifically, Bayesian network modeling, with an accuracy rate of 91.3%, aims to optimize IVF and ICSI procedures. In summary, AI stands at the forefront of innovation in ART, offering new hope through more accurate and efficient embryo selection.

Consciousness in humans is a state of awareness that encompasses both the self and the external environment, emerging from the intricate interplay of cortical and subcortical brain structures and neurotransmitter systems. The possibility that machines could possess consciousness has sparked ongoing debate. Proponents of strong artificial intelligence (AI) equate programmed computational processes with cognitive states, while advocates of weak AI argue that machines merely simulate thought without attaining genuine consciousness. This review critically examines neuroscience-inspired frameworks for artificial consciousness, exploring their alignment with prevailing theories of human consciousness. We investigate the fundamental cognitive functions associated with consciousness, including memory, awareness, prediction, learning, and experience, and their relevance to artificial systems. By analyzing neuroscience-based approaches to artificial consciousness, we identify key challenges and opportunities in the pursuit of machines capable of mimicking conscious states. Although present AI systems demonstrate advanced capabilities in intelligence and cognition, they fall short of achieving genuine consciousness, as defined in the context of human awareness. We discuss both the theoretical underpinnings and practical implications of creating artificial consciousness, addressing both weak and strong AI perspectives. Furthermore, we highlight the ethical and philosophical concerns that arise with the potential realization of machine consciousness. Our objective is to provide a comprehensive synthesis of the literature, fostering a deeper understanding of the interdisciplinary challenges involved in artificial consciousness and guiding future research directions.

Health data serves as a crucial foundation for artificial intelligence (AI) training in the healthcare sector. The pivotal procedure for acquiring numerous and effective health data lies in incentivizing participants to contribute their health data while adhering to privacy regulations like the General Data Protection Regulation. Federated learning achieves privacy protection by transmitting only parameters rather than data to the model. When integrated with blockchain smart contracts, this approach facilitates the automation of incentives according to health data quality, thereby mitigating human’s subjective intervention. Consequently, the synergy of these two methodologies offers new promise for the training of AI models in healthcare. However, this advantage encounters performance degradation due to the heterogeneity among diverse blockchains. This article posits the concept of Omnichain as a potential solution to this challenge by analyzing its operational mechanisms and future developmental trajectories and providing potential perspectives for its combination with hybrid federal learning solutions such as differential privacy and secure multi-party computation to promote its application in the sphere of AI in healthcare.

Artificial intelligence (AI) has become a transformative technology in medical diagnostics, enabling enhanced analysis of complex clinical data and supporting precise, efficient decision-making across diverse disease areas. This study explores the multi-disease application of AI in diagnosing cancer, cardiovascular diseases, neurological disorders, and infectious diseases, focusing on its role in improving diagnostic accuracy, speeding diagnostic processes, and facilitating early disease detection. By employing machine learning, deep learning, and neural network models, this study critically examines the performance of specific models - such as recurrent neural networks and support vector machines - in diverse healthcare contexts. Challenges addressed include data privacy, annotated dataset needs, overfitting risks, and ethical concerns such as AI bias and transparency, all of which are fundamental to ensuring patient safety and health equity. In addition, this study integrates security considerations, such as fault detection in cryptographic architectures, providing insights into the resilience of AI systems in healthcare. Future research directions, including the potential of AI in real-time patient monitoring, personalized medicine, and multispectral imaging, are proposed to expand AI’s utility in diagnostics. A comparative evaluation with traditional clinical diagnostics underscores AI’s validation potential, emphasizing its need for robust regulatory frameworks, particularly concerning global health standards (e.g., TRIPOD-AI and CONSORT-AI) and data privacy regulations such as Health Insurance Portability and Accountability Act and General Data Protection Regulation. Ultimately, AI-driven diagnostic systems show strong promise to revolutionize medical practice and improve patient outcomes, contingent on addressing the technical, ethical, and regulatory challenges involved. This research supports AI’s growing role in healthcare, providing a foundational understanding of both its current contributions and future potential across disease-specific applications.

Leukemia diagnosis traditionally depends on time-intensive examination of blood cell morphology, a process prone to human error. To address these challenges, this study explores the use of convolutional neural networks (CNNs) optimized with the Tversky loss function for automated, multilevel image classification in leukemia diagnostics. The model was designed to tackle binary classification for distinguishing normal from abnormal cells, and multiclass classification for identifying leukemia subtypes, while addressing the challenges of imbalanced datasets inherent in medical imaging. Trained on publicly available leukemia image datasets, the CNN achieved high accuracy in both tasks, effectively capturing subtle morphological variations critical for precise diagnosis. By incorporating performance metrics such as accuracy, precision, and recall, the study highlights the model’s reliability and robustness across classification tasks. The findings underscore the potential of CNN-based tools in enhancing diagnostic accuracy and efficiency, paving the way for future innovations in leukemia diagnostics and broader medical imaging applications.

Organizational culture (OC) affects every workplace, yet few studies have explored the relationship between OC and burnout using machine learning methods, which could provide new insights. This exploratory study employed a random forest algorithm to examine the relationship between OC and burnout among employees in health systems, aiming to determine whether OC can predict employee burnout. A 57-question survey assessing perceptions of OC and burnout was administered to employees across various health systems in the United States, yielding 67 responses. These survey results were used to train and test the random forest model. The findings indicated that several aspects of OC, such as job interference with home life, are predictive of burnout. Based on these preliminary results, employers should be aware of their organization’s culture and actively work to improve it to alleviate employee burnout. Leaders should implement strategies, such as allowing flexible work schedules to promote work-life balance and providing employees with the necessary resources to excel in their roles. The model also highlights the significant impact of OC on burnout, suggesting that a variety of burnout symptoms may signal the need for improvements in OC. This study serves as a starting point for future research to further explore how OC predicts burnout, while emphasizing the importance of cultivating a positive OC.

Chest radiography (CXR) is widely used for initial respiratory assessment, but its lesion detection capability is typically inferior to that of computed tomography. Several studies have reported that artificial intelligence (AI)-based bone suppression techniques can enhance the accuracy of lesion detection and disease classification. Previously, we developed an AI-based bone suppression system based on dual-energy subtraction principles. However, the subtraction process limited its versatility and introduced significant artifacts. To overcome these challenges, we improved the system to generate bone-suppressed images directly, eliminating the need for subtraction. This study demonstrates the utility of the updated bone suppression system as a pre-processing tool for regression analyses in assessing coronavirus disease 2019 severity. Four regression models - DenseNet, ResNet18, ResNet50, and RegNetY-120 - were employed to predict the severity based on scores annotated by radiologists. Except for DenseNet, all models showed statistically significant improvements in Pearson correlation coefficients (PCCs) when using bone-suppressed images generated by the updated model. The highest PCC, 0.895, was achieved by the ResNet18 model. The direct image generation process improved the clinical practicality of the bone suppression system while reducing artifacts. Furthermore, the significant improvement in linearity suggests that AI-driven bone suppression enhances the visibility of abnormalities and improves the accuracy for pulmonary condition assessments. These advancements could expand the application of bone suppression techniques in various regression analyses, including disease severity, progression, and recurrence risk. Nonetheless, further validation using larger and more diverse datasets, as well as a broader range of prediction models, is necessary.

Each day, one million people undergo electrocardiogram diagnostics. The diagnostic process is time-consuming and often yields incomplete or inconclusive results, placing significant strain on physicians. Artificial intelligence (AI)-assisted diagnosis can significantly alleviate this burden by enhancing diagnostic accuracy and efficiency, and its application is gaining traction across various fields. With the increasing number of patients and a growing backlog of diagnostic appointments, AI can offer physicians benefits such as accurate, timely, and reliable assistance in reviewing vital signs and conducting physical examinations for individual patients. As physicians face mounting pressure from insurance companies and government guidelines for consultation time, AI can help streamline the diagnostic process. In particular, with the growing global attention on cardiac health (and the overall decline thereof), the range of automated diagnostic opportunities is expanding rapidly. Additional mathematical processing tools can provide probabilistic assessments of various cardiac conditions, reducing physicians’ workload while enhancing treatment options. AI has already demonstrated success in expediting the detection of pathological cardiac depolarization abnormalities and shortening diagnostic time frames. However, AI-based diagnostics requires further validation and safeguards to minimize diagnostic inaccuracies, ensuring its reliability and safety in clinical practice.

Early and non-invasive detection of cervical malignancy holds great clinical significance. Diffuse reflectance (DR) spectroscopy has the capability to map tissue transformation at the biochemical, morphological, and cellular levels. We have developed a non-invasive, multimodal imaging system to map changes in tissue autofluorescence using DR for the screening and early detection of cervical cancer and cervical inflammation (cervicitis). The developed multispectral imaging device consists of light-emitting diodes (LED) emitting at 375, 545, 575, and 610 nm wavelengths, along with a 5-megapixel monochrome camera for image acquisition. Camera operation and image analysis are controlled using proprietary software installed on a Windows tablet. The 375 nm LED-excited autofluorescence, and the elastically backscattered light at 545, 575, and 610 nm originating from the cervix tissue are captured by the camera and processed to assess tissue abnormalities. A machine learning (ML) algorithm based on DR image intensity ratio values was developed for tissue classification. It was observed that the R610/R545 image ratio could discriminate malignant cervical sites from normal tissues, achieving a sensitivity of 100% and specificity of 93%. In comparison, cervicitis could be discriminated from normal tissues using the R610/R575 ratio, with a sensitivity of 91.6% and specificity of 94.4%. The study demonstrates the potential of DR imaging in conjunction with ML algorithm to non-invasively screen and detect cervical intraepithelial neoplasia and cervicitis in real time. As compared to the existing practice of Pap smear and colposcopy-directed biopsy, which are subjective and require a waiting period for results, objective screening using CerviScan would help reduce patient anxiety, unnecessary biopsies, and treatment costs. With increased patient screening, the accuracy of the ML algorithm would improve. When integrated into a cloud server, the system could address the needs of multiple users in a field setting.

For decades, efforts to standardize medical care have struggled to fundamentally reduce errors and unjustified variations in medical practice, largely due to the influence of the human factor. The formalization of clinical guidelines and computer-assisted interpretation makes it possible to provide decision-support tools to improve health-care quality. They can better influence clinician behavior than narrative guidelines. Medical ontologies and algorithms based on such ontologies allow the interpretation of formalized clinical documents (guidelines). To support health professionals as consultants, systems must provide reliable knowledge and rely on approaches explicitly explaining their recommendations. Integrating software engineering, knowledge engineering, and artificial intelligence advancements can provide health-care professionals with computer-interpretable clinical guidelines. These should be decision-support complexes combined under a common terminological framework capable of understanding patient health documents. The research focuses on an emerging concept of manufacturing systems working with digital clinical guidelines. The paper presents an architectural principle, a new technology for creating viable clinical decision support systems. It presents a development environment for constructing and controlling the system’s improvements. The main contributions of the study include the automation of multiple physician tasks by filling a single structured “medical history,” integration of formalized knowledge from clinical guidelines and other reliable sources to satisfy both the relevance of the methods used and personalization to patient, transparency of all applicable knowledge, explainability of advice based on the essence of the knowledge and linked to the source, and the integrability of decision-support complexes with neural network services, capable of inputting data from a structured medical history.

The current healthcare professionals require interdisciplinary training that integrates emotional intelligence (EI) and artificial intelligence (AI). EI equips healthcare professionals with the ability to communicate empathetically, provide patient care, and regulate emotions, while AI serves as a knowledge-based decision support system that improves decision-making and clinical efficiency. This study explores the integration of EI and AI in healthcare and examines their combined impact on both instructional methods and clinical practice. In addition, we evaluated the role of EI in fostering patient interaction, strengthening teamwork, and combating burnout, alongside the role of AI in advancing learning, improving diagnostic accuracy, and enabling personalized care. Moreover, existing literature on EI and AI is discussed in this study to highlight their complementary roles in enhancing healthcare practices. A combined EI and AI training approach can offer a holistic training model for preparing healthcare professionals. While EI enhances its ability to handle emotional challenges, AI provides data-driven information that can sharpen clinical thinking and improve efficiency. Together, EI and AI play a crucial role in enhancing patient care, decision-making, and teamwork. An integrated approach that combines both AI and EI, aimed at enhancing clinical skills and professional development, represents a promising advancement in healthcare practice. Integrating EI with AI tools optimizes both human and technological capabilities, fostering a more competent, compassionate, and productive healthcare workforce.