Intelligent Healthcare Research Articles

Artificial Intelligence in Healthcare: Implications, Challenges, and Future Prospects

Artificial intelligence is a technology that enables machines, especially computer systems, to mimic human intelligence and problem-solving abilities. This technology is being used nowadays in many areas of the healthcare industry, from scheduling online appointments to robot-assisted, safer surgeries with better patient outcomes. Just like a physician, AI can record a patient's history, signs and symptoms, and lab results, leading to an accurate diagnosis and a proper treatment plan, but in no time. This is just a small example of the implications of AI in the sector. Other areas of medicine are also being benefited by this innovation, like gastroentology, radiology, surgery, and preventive medicine. The development of advanced algorithms has reduced the burden on radiologists by helping them identify abnormal images and pick up malignant lesions with minimal diagnostic errors. Similarly, AI-assisted colonoscopy can help identify malignant and benign polyps. AI-powered systems, like Google's DeepMind Health, may identify malignant growth in mammograms and diabetic retinopathy, which can aid in early detection and treatment.

Interlayer cross-linked MXene enables ultra-stable printed paper-based flexible sensor for real-time humidity monitoring

With an abundance of hydrophilic active sites present on the surfaces, MXene exhibits significant potential for application in the field of humidity monitoring. Nevertheless, achieving the high response intensity, excellent response sensitivity, structural stability, and oxidation resistance in the MXene-based humidity sensors remains a formidable challenge. Herein, we propose a humidity sensor made from the paper-based flexible sensor with copper electrode and polyethyleneimine/silver nitrate cross-linking modified MXene composite materials. The humidity sensor prepared in this work presents a satisfactory capacitance response rate (819.9 % in 97 % RH) and favorable sensitivity (6.9/11.9 s for response/recovery time). Meanwhile, the humidity sensor possesses outstanding structural stability (less than 3 % attenuation of the capacitance response after 100 bending cycles) and superior oxidation resistance. Moreover, the real-time humidity monitor assembled based on the humidity sensor has realized the effective monitoring of human skin humidity and dynamic respiration. Consequently, this study provides a viable approach for achieving reliable real-time humidity monitoring, demonstrating significant potential in the realms of intelligent actuators, sensing devices, and health care.

Application of big data technology in the medical field

This project aims to construct a knowledge graph system applied to the field of traditional Chinese medicine (TCM) by extracting entities (such as drugs, diseases, etc.) and their relationships from TCM medical case data and storing them in a Neo4j database. The project process includes data reading, entity recognition and extraction, data formatting, and data import into the database. The project not only improved the individual's proficiency in Python data processing techniques (including regular expressions and JSON parsing) but also enhanced their skills in knowledge graph construction and database operations. In the future, there is a desire to further improve technical capabilities, explore more cutting-edge technologies in the TCM field, and promote project progress through collaboration, contributing to the modernization of TCM and intelligent healthcare services.

A multi-center study on the adaptability of a shared foundation model for electronic health records

Foundation models are transforming artificial intelligence (AI) in healthcare by providing modular components adaptable for various downstream tasks, making AI development more scalable and cost-effective. Foundation models for structured electronic health records (EHR), trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness to distribution shifts. However, questions remain on the feasibility of sharing these models across hospitals and their performance in local tasks. This multi-center study examined the adaptability of a publicly accessible structured EHR foundation model (FMSM), trained on 2.57 M patient records from Stanford Medicine. Experiments used EHR data from The Hospital for Sick Children (SickKids) and Medical Information Mart for Intensive Care (MIMIC-IV). We assessed both adaptability via continued pretraining on local data, and task adaptability compared to baselines of locally training models from scratch, including a local foundation model. Evaluations on 8 clinical prediction tasks showed that adapting the off-the-shelf FMSM matched the performance of gradient boosting machines (GBM) locally trained on all data while providing a 13% improvement in settings with few task-specific training labels. Continued pretraining on local data showed FMSM required fewer than 1% of training examples to match the fully trained GBM’s performance, and was 60 to 90% more sample-efficient than training local foundation models from scratch. Our findings demonstrate that adapting EHR foundation models across hospitals provides improved prediction performance at less cost, underscoring the utility of base foundation models as modular components to streamline the development of healthcare AI.

Unveiling the Evolution of Virtual Reality in Medicine: A Bibliometric Analysis of Research Hotspots and Trends over the Past 12 Years.

Virtual reality (VR), widely used in the medical field, may affect future medical training and treatment. Therefore, this study examined VR's potential uses and research directions in medicine. Citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate VR in medicine in articles published between 1 January 2012 and 31 December 2023. These data were analyzed using CiteSpace 6.2. R2 software. Present limitations and future opportunities were summarized based on the data. A total of 2143 related publications from 86 countries and regions were analyzed. The country with the highest number of publications is the USA, with 461 articles. The University of London has the most publications among institutions, with 43 articles. The burst keywords represent the research frontier from 2020 to 2023, such as "task analysis", "deep learning", and "machine learning". The number of publications on VR applications in the medical field has been steadily increasing year by year. The USA is the leading country in this area, while the University of London stands out as the most published, and most influential institution. Currently, there is a strong focus on integrating VR and AI to address complex issues such as medical education and training, rehabilitation, and surgical navigation. Looking ahead, the future trend involves integrating VR, augmented reality (AR), and mixed reality (MR) with the Internet of Things (IoT), wireless sensor networks (WSNs), big data analysis (BDA), and cloud computing (CC) technologies to develop intelligent healthcare systems within hospitals or medical centers.

Legal implications of artificial intelligence in health care

The last few years have seen a boom in the popularity of artificial intelligence (AI) around the world, and the health care sector has not been immune from what has been perceived by some as a revolutionary technology. Although AI has been around for many years, including in the field of health care, the recent introduction of consumer-facing generative AI tools has put a spotlight on the technology that has drawn attention from governments, corporations, consumers and more. Health care systems, physician groups, health insurance companies, and others in the space have shown an eagerness to explore AI's potential to improve various aspects of health care, but new legal risks and challenges are unfolding every day. This contribution looks at the latest health care-related measures in the United States and international legal and regulatory landscapes, as well as data privacy implications and discrimination concerns coming out of AI-enabled solutions. It also discusses concerns that health care systems and physicians alike are monitoring, including the potential for medical errors resulting from AI, liability considerations, and malpractice insurance trends.

Healthtech: Fusion of Artificial Intelligence in Healthcare

The complexity and rise of data in healthcare means that artificial intelligence will be increasingly applied in that field. Several types of AI are already being used in the area and will flourish as soon as the world evolves. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.At the end the paper also concludes the limitations of AI and also the challenges faced in the making of this paper.

Correlation Between Scientific and Technological Innovation and Financial Performance of Seed Enterprises: Application of Principal Component Analysis Algorithm in Performance Evaluation

This study explores the relationship between scientific and technological innovations and the financial performance of startups, focusing on a representative Artificial Intelligence (AI) healthcare startup. Utilizing Principal Component Analysis (PCA), the research aims to dissect the complex interplay between innovation-related metrics—such as R&D spending, patent counts, and technology adoption rates—and financial outcomes like revenue growth, profitability, and market share. The PCA methodology enabled the reduction of high-dimensional data into PCA, which clearly illustrates how various dimensions of innovation impact financial metrics. The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments. The objective of the research is to assist the ecosystem of startup consumers (including investors, business owners, and policymakers) in making better decisions that balance technological progress with economic objectives.

Addressing ethical and policy challenges in integrating artificial intelligence in healthcare

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Public perceptions of artificial intelligence in healthcare: ethical concerns and opportunities for patient-centered care

BackgroundIn an effort to improve the quality of medical care, the philosophy of patient-centered care has become integrated into almost every aspect of the medical community. Despite its widespread acceptance, among patients and practitioners, there are concerns that rapid advancements in artificial intelligence may threaten elements of patient-centered care, such as personal relationships with care providers and patient-driven choices. This study explores the extent to which patients are confident in and comfortable with the use of these technologies when it comes to their own individual care and identifies areas that may align with or threaten elements of patient-centered care.MethodsAn exploratory, mixed-method approach was used to analyze survey data from 600 US-based adults in the State of Florida. The survey was administered through a leading market research provider (August 10–21, 2023), and responses were collected to be representative of the state’s population based on age, gender, race/ethnicity, and political affiliation.ResultsRespondents were more comfortable with the use of AI in health-related tasks that were not associated with doctor-patient relationships, such as scheduling patient appointments or follow-ups (84.2%). Fear of losing the ‘human touch’ associated with doctors was a common theme within qualitative coding, suggesting a potential conflict between the implementation of AI and patient-centered care. In addition, decision self-efficacy was associated with higher levels of comfort with AI, but there were also concerns about losing decision-making control, workforce changes, and cost concerns. A small majority of participants mentioned that AI could be useful for doctors and lead to more equitable care but only when used within limits.ConclusionThe application of AI in medical care is rapidly advancing, but oversight, regulation, and guidance addressing critical aspects of patient-centered care are lacking. While there is no evidence that AI will undermine patient-physician relationships at this time, there is concern on the part of patients regarding the application of AI within medical care and specifically as it relates to their interaction with physicians. Medical guidance on incorporating AI while adhering to the principles of patient-centered care is needed to clarify how AI will augment medical care.

Caching or not: An online cost optimization algorithm for geodistributed data analysis in cloud environments

With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease of scaling, and other characteristics. Organizations often rent StaaS (Storage-as-a-Service) storage products offered by cloud providers, such as OSS (Object Storage Service), for massive data storage, while building a big data cluster in cloud environments to analyze the collected data from various regions. However, when the data to be analyzed are not located in the rented cluster, how to efficiently and economically process the distributed input data stored in clouds becomes an urgent problem to be solved. A simple approach is to only cache frequently accessed data rather than all data from other regions into the cluster to reduce total traffic costs. However, it is generally very hard to predict future data access curves. Thus, a rash caching decision may incur more costs. To address this problem, in this paper we propose an online algorithm for guiding cloud users to make cost-effective caching decisions properly, while not requiring any future information. We prove theoretically that the competitive ratio of our online algorithm is less than 2. Finally we verify the effectiveness of our proposed algorithm through extensive experiments based on the real price of Alibaba’s public IaaS cloud products using both real-world Yahoo S2 data and synthesized datasets.

NFMPAtt-Unet: Neighborhood Fuzzy C-means Multi-scale Pyramid Hybrid Attention Unet for medical image segmentation

Medical image segmentation is crucial for understanding anatomical or pathological changes, playing a key role in computer-aided diagnosis and advancing intelligent healthcare. Currently, important issues in medical image segmentation need to be addressed, particularly the problem of segmenting blurry edge regions and the generalizability of segmentation models. Therefore, this study focuses on different medical image segmentation tasks and the issue of blurriness. By addressing these tasks, the study significantly improves diagnostic efficiency and accuracy, contributing to the overall enhancement of healthcare outcomes. To optimize segmentation performance and leverage feature information, we propose a Neighborhood Fuzzy c-Means Multiscale Pyramid Hybrid Attention Unet (NFMPAtt-Unet) model. NFMPAtt-Unet comprises three core components: the Multiscale Dynamic Weight Feature Pyramid module (MDWFP), the Hybrid Weighted Attention mechanism (HWA), and the Neighborhood Rough Set-based Fuzzy c-Means Feature Extraction module (NFCMFE). The MDWFP dynamically adjusts weights across multiple scales, improving feature information capture. The HWA enhances the network’s ability to capture and utilize crucial features, while the NFCMFE, grounded in neighborhood rough set concepts, aids in fuzzy C-means feature extraction, addressing complex structures and uncertainties in medical images, thereby enhancing adaptability. Experimental results demonstrate that NFMPAtt-Unet outperforms state-of-the-art models, highlighting its efficacy in medical image segmentation.

The Environmental Costs of Artificial Intelligence for Healthcare

Healthcare has emerged as a key setting where expectations are rising for the potential benefits of artificial intelligence (AI), encompassing a range of technologies of varying utility and benefit. This paper argues that, even as the development of AI for healthcare has been pushed forward by a range of public and private actors, insufficient attention has been paid to a key contradiction at the center of AI for healthcare: that its pursuit to improve health is necessarily accompanied by environmental costs which pose risks to human and environmental health—costs which are not necessarily directly borne by those benefiting from the technologies. This perspective paper begins by examining the purported promise of AI in healthcare, contrasting this with the environmental costs which arise across the AI lifecycle, to highlight this contradiction inherent in the pursuit of AI. Its advancement—including in healthcare—is often described through deterministic language that presents it as inevitable. Yet, this paper argues that there is need for recognition of the environmental harm which this pursuit can lead to. Given recent initiatives to incorporate stakeholder involvement into decision-making around AI, the paper closes with a call for an expanded conception of stakeholders in AI for healthcare, to include consideration of those who may be indirectly affected by its development and deployment.

Patients' perspectives on the use of artificial intelligence and robots in healthcare.

We aimed to evaluate the opinions of individuals aged 18 and above in our country regarding the use of artificial intelligence (AI) and robots in the field of healthcare. The growing population and patient load, coupled with increasing data, can expedite the diagnosis and treatment process for patients through faster, easier, and more accurate interpretation of information. The study encompasses voluntary participants aged 18 and above, who have either undergone surgery in ahospital or have accompanied afamily member during asurgical procedure and possess internet access as well as the capability to participate in online surveys. Atotal of 725 individuals participated in our study 61% (n=442) of respondents expressed trust in the operation of AI and robots in the hospital setting. 64.1% (n=465) of participants expressed trust in AI's contribution to disease diagnosis and laboratory tests. The confidence in AI's use in radiological examinations and its contribution reached 71.6% (n=519). This study demonstrates that the use of AI and robots in healthcare services is accepted by our society and would be appropriate in our society (Tab. 5, Fig. 1, Ref. 24).

The Dual Impact of Artificial Intelligence in Healthcare: Balancing Advancements with Ethical and Operational Challenges

The synchronic and diachronic study of the evolution of Artificial Intelligence (AI) unveils one prominent fact that its effect can be traced in almost all fields such as healthcare industry. The growth is perceived holistically in software, hardware implementation, or application in these various fields. As the title suggests, the review will highlight the impact of AI on healthcare possibly in all dimensions including precision medicine, diagnostics, drug development, automation of the process, etc., explicating whether AI is a blessing or a curse or both. With the availability of enough data and analysis to examine the topic at hand, however, its application is still functioning in quite early stages in many fields, the present work will endeavour to provide an answer to the question. This paper takes a close look at how AI is transforming areas such as diagnostics, precision medicine, and drug discovery, while also addressing some of the key ethical challenges it brings. Issues like patient privacy, safety, and the fairness of AI decisions are explored to understand whether AI in healthcare is a positive force, a potential risk, or perhaps both.

Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare

Validating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare

Analysis of trust factors for AI-assisted diagnosis in intelligent Healthcare: Personalized management strategies in chronic disease management

By utilizing various techniques to integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) model with the variables of personal innovation and task complexity, an initial trust mechanism is established. Based on the inherent motivation and demand of doctors to use AI-assisted diagnosis systems, a doctor adoption model for AI-assisted diagnosis systems is established. Using chronic disease management as an example, personalized management strategies based on reinforcement learning technology are discussed for a virtual management environment that provides intervention recommendations according to the medical condition. It is shown in experiments that the fitting goodness-of-fit value of the model for willingness to use, which excludes initial trust-related factors, is only 0.557, while the fitting goodness-of-fit value of the proposed model in this paper is 0.673. This indicates that the model that includes initial trust factors can more accurately explain the logic of the behavior evolution of doctors adopting AI-assisted diagnosis systems.

Leveraging Large Language Models in the delivery of post-operative dental care: a comparison between an embedded GPT model and ChatGPT

ObjectiveThis study underscores the transformative role of Artificial Intelligence (AI) in healthcare, particularly the promising applications of Large Language Models (LLMs) in the delivery of post-operative dental care. The aim is to evaluate the performance of an embedded GPT model and its comparison with ChatGPT-3.5 turbo. The assessment focuses on aspects like response accuracy, clarity, relevance, and up-to-date knowledge in addressing patient concerns and facilitating informed decision-making.Material and methodsAn embedded GPT model, employing GPT-3.5-16k, was crafted via GPT-trainer to answer postoperative questions in four dental specialties including Operative Dentistry & Endodontics, Periodontics, Oral & Maxillofacial Surgery, and Prosthodontics. The generated responses were validated by thirty-six dental experts, nine from each specialty, employing a Likert scale, providing comprehensive insights into the embedded GPT model’s performance and its comparison with GPT3.5 turbo. For content validation, a quantitative Content Validity Index (CVI) was used. The CVI was calculated both at the item level (I-CVI) and scale level (S-CVI/Ave). To adjust I-CVI for chance agreement, a modified kappa statistic (K*) was computed.ResultsThe overall content validity of responses generated via embedded GPT model and ChatGPT was 65.62% and 61.87% respectively. Moreover, the embedded GPT model revealed a superior performance surpassing ChatGPT with an accuracy of 62.5% and clarity of 72.5%. In contrast, the responses generated via ChatGPT achieved slightly lower scores, with an accuracy of 52.5% and clarity of 67.5%. However, both models performed equally well in terms of relevance and up-to-date knowledge.ConclusionIn conclusion, embedded GPT model showed better results as compared to ChatGPT in providing post-operative dental care emphasizing the benefits of embedding and prompt engineering, paving the way for future advancements in healthcare applications.

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare.

Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this. Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources. AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue. This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.

Regional Experience of Using Artificial Intelligence Services in the Healthcare Sector of the Russian Federation in 2023

Background. The using of artificial intelligence (AI) in healthcare has particular importance for primary medical care. The market for AI in healthcare is actively developing, and Russian companies offer their products and services in this area. The federal project to create a single digital contour in healthcare includes the implementation of medical devices based on AI technology in healthcare of the regions of the Russian Federation. The AI services may or may not be medical devices. It is necessary to understand the organizational and economic effects for the healthcare system and a certain medical organization in order to select a service. There is no consolidation of regional experience on AI-enabled services in healthcare in the Russian Federation today. Aims — consolidation of experience in the using of the AI services in healthcare sector in the regions of the Russian Federation. Methods. We conducted an online survey of representatives of regional executive authorities and medical information and analytical centers in the regions of the Russian Federation through the “Yandex Forms” platform and also they were interviewed. Results. We surveyed 84 regions of the Russian Federation to consolidate the experience of using the AI services, and the information on Moscow was obtained from open sources. The analysis showed that the AI services that are not medical devices are more common than the AI services that are medical devices. Based on the results of the study, we have formed a classification of the AI services that are not medical devices used in healthcare in the Russian Federation, identified the reasons why, according to the regions, it is difficult to implement the AI services, as well as determined the conditions of use of the AI services that are not medical devices. Conclusions. On the basis of the study, we determined the algorithm for the implementation of the AI services in the Russian healthcare.