Intelligent Healthcare Research Articles

Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks.

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.

Artificial Intelligence in Healthcare : A Review

Artificial Intelligence in Healthcare : A Review

Multimodal explainability via latent shift applied to COVID-19 stratification

We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, which jointly learns modality reconstructions and sample classifications using tabular and imaging data. The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset, which contains multimodal data for the early identification of patients at risk of severe outcome. The results show that the proposed method provides meaningful explanations without degrading the classification performance.

Physical therapists’ perceptions and attitudes towards artificial intelligence in healthcare and rehabilitation: A qualitative study

Artificial intelligence (AI) is being introduced to rehabilitation practices, and it can optimize the patient's outcome through their ability to design personalized care strategies and interventions. To understand the attitudes and perceptions of physical therapy professionals on the use of AI in rehabilitation in regard to treatment planning, diagnosis, outcome prediction, and advantages and disadvantages. This paper followed an exploratory, qualitative research design. Semi-structured, one-to-one interviews were conducted with participants of different experience levels and specialties in physical therapy. Results were evaluated using thematic analysis. Four themes were identified: (i) perceptions of AI and its applications in healthcare services, (ii) impact on the workforce (iii) considerations around implementing AI within rehabilitation and (iv) AI, and the fast-approaching future. Participants shared views on the potential impact of AI on rehabilitation practices, such as aiding the decision-making process, saving time and effort of both the therapist and patients. Participants have stressed on potential pitfalls that still need to be considered, such as patient data privacy, potential loss of patient-healthcare practitioner relationship, ethical concerns regarding overreliance on these applications and how that might hinder effective patient care. The findings add to the literature about physical therapists' understanding regarding the use of AI in patient care. Several concerns were raised to the adoption of AI, including concerns about patient privacy, and ethical concerns. Based on the study findings, researchers emphasize the importance of establishing guidelines when incorporating AI in rehabilitation to improve the therapist's knowledge and skills.

Mapping Trust in Nurses with Dimensions of Trustworthy Artificial Intelligence: A Scoping Review.

This scoping review examines the concept of trust in nursing and its potential application in developing trustworthy Artificial Intelligence (AI) for healthcare. Recognizing nurses as highly trusted professionals, the study explores how attributes contributing to trust in nursing can inform AI development. Following the Joanna Briggs Institute framework, the review synthesizes literature on patients' perceptions of nurses' trustworthiness and compares these with desired qualities in trustworthy AI. Preliminary findings suggest that nursing's trust-inducing actions could offer valuable insights for implementing trust-enhancing features in AI. This approach aims to bring innovative insights into the nature of trust and contribute to creative solutions to develop trustworthy AI in healthcare. By aligning AI development with principles of trust observed in nursing, the review proposes novel strategies for creating more ethical and accepted AI systems in healthcare settings.

Phase change hydrogels with tunable adhesion for wearable thermal management and intelligent healthcare

Intelligent wearable devices integrating real-time health monitoring and on-demand treatment are obtaining widespread attention. However, precise monitoring and prolonged photothermal therapy of human bodies still face significant challenges. Herein, a phase change gel (PCG) with simultaneous strain sensing and photothermal therapy functions was fabricated using sodium sulfate decahydrate (SSD, Na2SO4·10H2O), polyacrylamide (PAM), and polydopamine-modified MXene (PDA@MXene). The PCGs possessed a suitable phase transition temperature and high enthalpy value (128.8 J/g), which provided sufficient thermal comfort. The PAM hydrogel effectively overcame the liquid leakage of SSD and achieved flexibility. PDA@MXene not only imparted the PCGs with excellent photothermal conversion efficiency (93.83 %), but also ensured strong adhesive properties to precisely monitor human motions. Interestingly, the PCGs exhibited temperature-sensitive adhesion ability by triggering the phase transition of SSD, thus obtaining high adhesion to the skin and painless detachment simultaneously. The temperature-induced adhesive PCGs exhibit great application prospects in portable medicine devices.

The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare

The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated into medicine to assist both clinicians and patients, the integration of LLMs within healthcare robots has not yet been explored for clinical settings. In this perspective paper, we investigate the groundbreaking developments in robotics and LLMs to uniquely identify the needed system requirements for designing health-specific LLM-based robots in terms of multi-modal communication through human–robot interactions (HRIs), semantic reasoning, and task planning. Furthermore, we discuss the ethical issues, open challenges, and potential future research directions for this emerging innovative field.

AI in the Field of Healthcare: A New Paradigm Examining Facilitators and Biases Through SEM-ANN

The present investigation aims to examine the influence of various facilitators and biases on the behavioral intention (BI), which in turn lead to actual implementation of Artificial Intelligence (AI) in healthcare. Further to get a comprehensive viewpoint, the study attempts to observe the impact of actual implementation on perceived sustainable performance. Data for the current study were collected from 600 healthcare practitioners who use AI tools to treat patients. To analyze the linkages between exogenous and endogenous constructs Structural Equation Modeling (through AMOS software) was performed. The results indicated that out of eleven facilitators, eight significantly influenced BI to adopt AI. The findings for biases depicted that out of six biases three (Historical, Evaluation and Algorithmic) were supported. Furthermore, findings suggest that BI has a direct positive influence on actual implementation of AI which in turn positively contributes to perceived sustainable performance. The observations of this study could be helpful for practitioners and academicians. It may be used to determine which AI-supported approaches should be used for certain designs in the healthcare sector. Though this research provides valuable insights for healthcare professionals, yet it has certain limitations which may be used by researchers to build frameworks.

Piezoelectric nanogenerator enabled fully self-powered instantaneous wireless sensor system

Self-powered real-time wireless sensor networks (WSNs) are vital for smart manufacturing, intelligent healthcare, and smart cities etc. Various nanogenerators have extensively exploited for powering these systems. Compared with triboelectric nanogenerators (TENGs), piezoelectric nanogenerators (PENGs) have smaller dimensions and can be easier to integrate into ICs, offering broader applications. However, fully self-powered instantaneous wireless sensor systems with PENGs are yet to be realized. Here, we present a PENG based, self-powered, instantaneous wireless (PSIW) sensor system. The integration of the PENG with an RLC sensing circuit facilitates the spontaneous generation of oscillating signals with encoded sensing information. To address the high internal impedance issue of PENGs, a piezoelectric voltage driven electronic switch is employed, ensuring that the oscillating signal's frequency and amplitude remain highly stable. The effects of variables on signal frequency are investigated in detail. The sensor system is utilized to sense bending (strain), and to monitor physical actions such as elbow swing, wrist bending, walking, running etc. Results showed that the PSIW sensor system can effectively monitor strain and bending angles, and judiciously detect body’s physical movements. By employing either frequency-based or amplitude-based sensing methods, the PSIW sensor system demonstrates versatility and adaptability for various sensing applications.

A Critical Study of Artificial Intelligence in Healthcare

The modern era has ushered the proliferation of new technologies, especially witnessed in the emergence of the nascent artificial intelligence (AI) sector. The use of AI is largely multifaceted, proving useful in various industries such as healthcare - however, it may also allow for deleterious effects to occur. The use of AI in healthcare settings can work to extend and augment the quality of patients’ lives. Notwithstanding this, health AI enshrines various perils including the lack of patient privacy, algorithm bias - particularly on marginalized and racialized communities. This is ultimately compounded by the absence of ethical framework governing the usage of AI in healthcare settings. Specifically, this article seeks to explore whether or not the use of health AI is a potential prospect or peril; considering its duality. To investigate this topic, this article will utilize an interdisciplinary approach – drawing from domains such as: sociology, socio-legal and socio-medical climates. Secondary data will be primarily sourced via peer-reviewed journal articles, textbooks, and reliable contemporary websites. This study finds that health AI remains a greater prospect - reinforcing the quality and elongates the duration of the human lifespan. It concludes with a call to action to inform the success of health AI in praxis: namely, the need to incorporate the aforementioned topics within medical pedagogy and ethical frameworks.

Organic Thermoelectric Materials for Wearable Electronic Devices.

Wearable electronic devices have emerged as a pivotal technology in healthcare and artificial intelligence robots. Among the materials that are employed in wearable electronic devices, organic thermoelectric materials possess great application potential due to their advantages such as flexibility, easy processing ability, no working noise, being self-powered, applicable in a wide range of scenarios, etc. However, compared with classic conductive materials and inorganic thermoelectric materials, the research on organic thermoelectric materials is still insufficient. In order to improve our understanding of the potential of organic thermoelectric materials in wearable electronic devices, this paper reviews the types of organic thermoelectric materials and composites, their assembly strategies, and their potential applications in wearable electronic devices. This review aims to guide new researchers and offer strategic insights into wearable electronic device development.

Advancing grading and outcome prediction in aneurysmal subarachnoid hemorrhage: Harnessing artificial intelligence and machine learning for precision healthcare.

Advancing grading and outcome prediction in aneurysmal subarachnoid hemorrhage: Harnessing artificial intelligence and machine learning for precision healthcare.

Defining change: Exploring expert views about the regulatory challenges in adaptive artificial intelligence for healthcare

ObjectiveContinuously learning or adaptive artificial intelligence (AI) applications for screening, diagnostic and other clinical services are yet to be widely deployed. This is partly due to existing device regulation mechanisms that are not fit for purpose regarding the adaptive features of AI. This study aims to identify the challenges in and opportunities for the regulation of adaptive features of AI. Materials and MethodsWe performed in-depth qualitative, semi-structured interviews with a diverse group of 72 experts in high-income countries (Australia, Canada, New Zealand, US, and UK) who are involved in the development, acquisition, deployment and regulation of healthcare AI systems. ResultsOur findings revealed perceived challenges in the regulation of adaptive features of machine learning (ML) systems. These challenges include the complexity of AI applications as products subject to regulation; lack of accepted definitions of adaptive changes; diverse approaches to defining significant adaptive change; and lack of clarity about regulation of adaptive change. Our findings reflect potentially competing interests among different stakeholders and diversity of approaches from regulatory bodies and legislators in different jurisdictions across the globe. In addition, our findings highlight the complex regulatory implications of adaptive AI that differ from traditional medical products, drugs or devices. ConclusionThe perceived regulatory challenges raised by adaptive features of AI applications require high-level coordination within a complex regulatory ecosystem that consists of medical device regulators, professional accreditation agencies, professional medical organisations, and healthcare service providers. Regulatory approaches should complement existing safety protocols with new governance mechanisms that specifically take into account the variety of roles and responsibilities that will be required to monitor, evaluate and oversee adaptive changes.

Discriminant Input Processing Scheme for Self-Assisted Intelligent Healthcare Systems.

Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method's scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management's accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations.

Integrating metaheuristics and artificial intelligence for healthcare: basics, challenging and future directions

Accurate and rapid disease detection is necessary to manage health problems early. Rapid increases in data amount and dimensionality caused challenges in many disciplines, with the primary issues being high computing costs, memory costs, and low accuracy performance. These issues will arise since Machine Learning (ML) classifiers are mostly used in these fields. However, noisy and irrelevant features have an impact on ML accuracy. Therefore, to choose the best subset of features and decrease the dimensionality of the data, Metaheuristics (MHs) optimization algorithms are applied to Feature Selection (FS) using various modalities of medical imaging or disease datasets with different dimensions. The review starts by giving a general overview of the many approaches to AI algorithms, followed by a general overview of the various MH algorithms for healthcare applications, an analysis of MHs boosted AI for healthcare applications, and using a wide range of research databases as a data source for access to numerous field publications. The final section of this review discusses the problems and challenges facing healthcare application development.

Artificial Intelligence in Health Care: Opportunities, Challenges, and the Road Ahead.

A comprehensive, collective approach to navigating the challenges of bias, privacy, and ethical considerations presented by the use of artificial intelligence in health care will require robust frameworks, continuous learning, and a commitment to equity. The insights and discussions presented in this issue are a testament to the ongoing efforts in North Carolina and beyond to find a balance between innovation with responsibility, ensuring that AI can deliver on its promise to enhance outcomes.

AI and Social Determinants of Health in Health Care: A Personal Perspective.

As a biomedical data scientist, when I think of the future of artificial intelligence in health care, the potential fills me with both excitement and caution. A promising area of innovation, AI can be used to assess the impact of social determinants of health on health outcomes, though more standardization is needed.

Challenges in application of artificial intelligence in healthcare field and response strategies

The rapid development of artificial intelligence in the field of healthcare has greatly improved diagnosis accuracy, disease prediction, personalized treatment and healthcare resource management. However, with the widespread application of medical artificial intelligence, challenges has emerged in the aspects of medical data, model development and evaluation, and societal considerations. Therefore, this study aims to explore challenges in the application of artificial intelligence in healthcare and suggest a series of feasible solutions to improve medical professional and researchers' understanding of medical artificial intelligence and enhance the quality of healthcare in clinical practice.

Recent advances in laser-induced-graphene-based soft skin electronics for intelligent healthcare

Skin is a rich source of invaluable information for healthcare management and disease diagnostics. The integration of soft skin electronics enables precise and timely capture of these cues at the skin interface. Leveraging attributes such as lightweight design, compact size, high integration, biocompatibility, and enhanced comfort, these technologies hold significant promise for advancing various applications. However, the fabrication process for most existing soft skin electronics typically requires expensive platforms and clean-room environments, potentially inflating production costs. In recent years, the emergence of laser-induced-graphene (LIG) has presented a practical solution for developing soft skin electronics that are both cost-effective and high-performing. This advancement paves the way for the widespread adoption of intelligent healthcare technologies. Here, we comprehensively review recent studies focusing on LIG-based soft skin electronics (LIGS2E) for intelligent healthcare applications. We first outline the preparation methodologies, fundamental properties of LIG, and standard regulation strategies employed in developing soft skin electronics. Subsequently, we present an overview of various LIGS2E designs and their diverse applications in intelligent healthcare. These applications encompass biophysical and biochemical sensors, bio-actuators, and power supply systems. Finally, we deliberate on the potential challenges associated with the practical implementation of LIGS2E in healthcare settings and offer insights into future directions for research and development. By elucidating the capabilities and limitations of LIGS2E, this review aims to contribute to advancing intelligent healthcare technologies.

Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.

With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.