National COVID-19 vaccination plan: using artificial spatial intelligence to overcome challenges in Brazil. / Plano nacional de vacinação contra a COVID-19: uso de inteligência artificial espacial para superação de desafios.

This article explores the use of spatial artificial intelligence to estimate the resources needed to implement Brazil's COVID-19 immu nization campaign. Using secondary data, we conducted a cross-sectional ecological study adop ting a time-series design. The unit of analysis was Brazil's primary care centers (PCCs). A four-step analysis was performed to estimate the popula tion in PCC catchment areas using artificial in telligence algorithms and satellite imagery. We also assessed internet access in each PCC and con ducted a space-time cluster analysis of trends in cases of SARS linked to COVID-19 at municipal level. Around 18% of Brazil's elderly population live more than 4 kilometer from a vaccination point. A total of 4,790 municipalities showed an upward trend in SARS cases. The number of PCCs located more than 5 kilometer from cell towers was largest in the North and Northeast regions. Innovative stra tegies are needed to address the challenges posed by the implementation of the country's National COVID-19 Vaccination Plan. The use of spatial artificial intelligence-based methodologies can help improve the country's COVID-19 response.

O objetivo deste artigo é analisar o uso da inteligência artificial espacial no contexto da imunização contra COVID-19 para a seleção adequada dos recursos necessários. Trata-se de estudo ecológico de caráter transversal baseado em uma abordagem espaço-temporal utilizando dados secundários, em Unidades Básicas de Saúde do Brasil. Foram adotados quatro passos analíticos para atribuir um volume de população por unidade básica, aplicando algoritmos de inteligência artificial a imagens de satélite. Em paralelo, as condições de acesso à internet móvel e o mapeamento de tendências espaço-temporais de casos graves de COVID-19 foram utilizados para caracterizar cada município do país. Cerca de 18% da população idosa brasileira está a mais de 4 quilômetros de distância de uma sala de vacina. No total, 4.790 municípios apresentaram tendência de agudização de casos de Síndrome Respiratória Aguda Grave. As regiões Norte e Nordeste apresentaram o maior número de Unidades Básicas de Saúde com mais de 5 quilômetros de distância de antenas de celular. O Plano nacional de vacinação requer o uso de estratégias inovadoras para contornar os desafios do país. O uso de metodologias baseadas em inteligência artificial espacial pode contribuir para melhoria do planejamento das ações de resposta à COVID-19.

Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients.

PURPOSE OF REVIEW: Immunocompromised patients are at high risk for infection. During the coronavirus disease (COVID-19) pandemic, immunocompromised patients exhibited increased odds of intensive care unit admission and death. Early pathogen identification is essential to mitigating infection related risk in immunocompromised patients. Artificial intelligence (AI) and machine learning (ML) have tremendous appeal to address unmet diagnostic needs. These AI/ML tools often rely on the wealth of data found in healthcare to enhance our ability to identify clinically significant patterns of disease. To this end, our review provides an overview of the current AI/ML landscape as it applies to infectious disease testing with emphasis on immunocompromised patients. RECENT FINDINGS: Examples include AI/ML for predicting sepsis in high risk burn patients. Likewise, ML is utilized to analyze complex host-response proteomic data to predict respiratory infections including COVID-19. These same approaches have also been applied for pathogen identification of bacteria, viruses, and hard to detect fungal microbes. Future uses of AI/ML may include integration of predictive analytics in point-of-care (POC) testing and data fusion applications. SUMMARY: Immunocompromised patients are at high risk for infections. AI/ML is transforming infectious disease testing and has great potential to address challenges encountered in the immune compromised population.

POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021).

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions.

[Literacy for Appropriate Use of Medical Big Data and Artificial Intelligence].

Recent developments have enabled daily accumulated medical information to be converted into medical big data, and new evidence is expected to be created using databases and various open data sources. Database research using medical big data was actively conducted in the coronavirus disease 2019 (COVID-19) pandemic and created evidence for a new disease. Conversely, the new term "infodemic" has emerged and has become a social problem. Multiple posts on social networking services (SNS) overly stirred up safety concerns about the COVID-19 vaccines based on the analysis results of the Vaccine Adverse Event Reporting System (VAERS). Medical experts on SNS have attempted to correct these misunderstandings. Incidents where research papers about the COVID-19 treatment using medical big data were retracted due to the lack of reliability of the database also occurred. These topics of appropriate interpretation of results using spontaneous reporting databases and ensuring the reliability of databases are not new issues that emerged during the COVID-19 pandemic but issues that were present before. Thus, literacy regarding medical big data has become increasingly important. Research related to artificial intelligence (AI) is also progressing rapidly. Using medical big data is expected to accelerate AI development. However, as medical AI does not resolve all clinical setting problems, we also need to improve our medical AI literacy.

Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review.

BACKGROUND: In the context of a deepening global shortage of health workers and, in particular, the COVID-19 pandemic, there is growing international interest in, and use of, online symptom checkers (OSCs). However, the evidence surrounding the triage and diagnostic accuracy of these tools remains inconclusive. OBJECTIVE: This systematic review aimed to summarize the existing peer-reviewed literature evaluating the triage accuracy (directing users to appropriate services based on their presenting symptoms) and diagnostic accuracy of OSCs aimed at lay users for general health concerns. METHODS: Searches were conducted in MEDLINE, Embase, CINAHL, Health Management Information Consortium (HMIC), and Web of Science, as well as the citations of the studies selected for full-text screening. We included peer-reviewed studies published in English between January 1, 2010, and February 16, 2022, with a controlled and quantitative assessment of either or both triage and diagnostic accuracy of OSCs directed at lay users. We excluded tools supporting health care professionals, as well as disease- or specialty-specific OSCs. Screening and data extraction were carried out independently by 2 reviewers for each study. We performed a descriptive narrative synthesis. RESULTS: A total of 21,296 studies were identified, of which 14 (0.07%) were included. The included studies used clinical vignettes, medical records, or direct input by patients. Of the 14 studies, 6 (43%) reported on triage and diagnostic accuracy, 7 (50%) focused on triage accuracy, and 1 (7%) focused on diagnostic accuracy. These outcomes were assessed based on the diagnostic and triage recommendations attached to the vignette in the case of vignette studies or on those provided by nurses or general practitioners, including through face-to-face and telephone consultations. Both diagnostic accuracy and triage accuracy varied greatly among OSCs. Overall diagnostic accuracy was deemed to be low and was almost always lower than that of the comparator. Similarly, most of the studies (9/13, 69 %) showed suboptimal triage accuracy overall, with a few exceptions (4/13, 31%). The main variables affecting the levels of diagnostic and triage accuracy were the severity and urgency of the condition, the use of artificial intelligence algorithms, and demographic questions. However, the impact of each variable differed across tools and studies, making it difficult to draw any solid conclusions. All included studies had at least one area with unclear risk of bias according to the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool. CONCLUSIONS: Although OSCs have potential to provide accessible and accurate health advice and triage recommendations to users, more research is needed to validate their triage and diagnostic accuracy before widescale adoption in community and health care settings. Future studies should aim to use a common methodology and agreed standard for evaluation to facilitate objective benchmarking and validation. TRIAL REGISTRATION: PROSPERO CRD42020215210; https://tinyurl.com/3949zw83.

Editorial: Infectious Disease Surveillance Using Artificial Intelligence (AI) and its Role in Epidemic and Pandemic Preparedness.

Artificial intelligence (AI), or machine learning, is an ancient concept based on the assumption that human thought and reasoning can be mechanized. AI techniques have been used in diagnostic medicine for several decades, particularly in image analysis and clinical diagnosis. During the COVID-19 pandemic, AI was critical in genome sequencing, drug and vaccine development, identifying disease outbreaks, monitoring disease spread, and tracking viral variants. AI-driven approaches complement human-curated ones, including traditional public health surveillance. Preparation for future pandemics will require the combined efforts of collaborative surveillance networks, which currently include the US Centers for Disease Control and Prevention (CDC) Center for Forecasting and Outbreak Analytics and the World Health Organization (WHO) Hub for Pandemic and Epidemic Intelligence, which will use AI combined with international cooperation to implement AI in surveillance programs. This Editorial aims to provide an update on the uses and limitations of AI in infectious disease surveillance and pandemic preparedness.

Tweeting for Health Using Real-time Mining and Artificial Intelligence-Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter.

BACKGROUND: Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time. OBJECTIVE: This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics. METHODS: U-MAS is a platform-independent ecosystem developed in Python that leverages the Twitter V2 application programming interface and the Elastic Stack. The U-MAS expert system has 5 major components: data extraction framework, latent Dirichlet allocation (LDA) topic model, sentiment analyzer, misinformation classification model, and Elastic Cloud deployment (indexing of data and visualizations). The data extraction framework queries the data through the Twitter V2 application programming interface, with queries identified by public health experts. The LDA topic model, sentiment analyzer, and misinformation classification model are independently trained using a small, expert-validated subset of the extracted data. These models are then incorporated into U-MAS to analyze and classify the remaining data. Finally, the analyzed data are loaded into an index in the Elastic Cloud deployment and can then be presented on dashboards with advanced visualizations and analytics pertinent to infodemiology and infoveillance analysis. RESULTS: U-MAS performed efficiently and accurately. Independent investigators have successfully used the system to extract significant insights into a fluoride-related health misinformation use case (2016 to 2021). The system is currently used for a vaccine hesitancy use case (2007 to 2022) and a heat wave-related illnesses use case (2011 to 2022). Each component in the system for the fluoride misinformation use case performed as expected. The data extraction framework handles large amounts of data within short periods. The LDA topic models achieved relatively high coherence values (0.54), and the predicted topics were accurate and befitting to the data. The sentiment analyzer performed at a correlation coefficient of 0.72 but could be improved in further iterations. The misinformation classifier attained a satisfactory correlation coefficient of 0.82 against expert-validated data. Moreover, the output dashboard and analytics hosted on the Elastic Cloud deployment are intuitive for researchers without a technical background and comprehensive in their visualization and analytics capabilities. In fact, the investigators of the fluoride misinformation use case have successfully used the system to extract interesting and important insights into public health, which have been published separately. CONCLUSIONS: The novel U-MAS pipeline has the potential to detect and analyze misleading information related to a particular topic or set of related topics.

Evolution of the newest diagnostic methods for COVID-19: a Chinese perspective. / ä¸­å›½è§†è§’ä¸‹çš„æ–°å† è‚ºç‚Žæœ€æ–°è¯Šæ–­æ–¹æ³•æ¼”è¿›.

Coronavirus disease 2019 (COVID-19) has continued to spread globally since late 2019, representing a formidable challenge to the world's healthcare systems, wreaking havoc, and spreading rapidly through human contact. With fever, fatigue, and a persistent dry cough being the hallmark symptoms, this disease threatened to destabilize the delicate balance of our global community. Rapid and accurate diagnosis of COVID-19 is a prerequisite for understanding the number of confirmed cases in the world or a region, and an important factor in epidemic assessment and the development of control measures. It also plays a crucial role in ensuring that patients receive the appropriate medical treatment, leading to optimal patient care. Reverse transcription-polymerase chain reaction (RT-PCR) technology is currently the most mature method for detecting viral nucleic acids, but it has many drawbacks. Meanwhile, a variety of COVID-19 detection methods, including molecular biological diagnostic, immunodiagnostic, imaging, and artificial intelligence methods have been developed and applied in clinical practice to meet diverse scenarios and needs. These methods can help clinicians diagnose and treat COVID-19 patients. This review describes the variety of such methods used in China, providing an important reference in the field of the clinical diagnosis of COVID-19.

A call for implementing augmented intelligence in pediatric dermatology.

Augmented intelligence (AI), the combination of artificial based intelligence with human intelligence from a practitioner, has become an increased focus of clinical interest in the field of dermatology. Technological advancements have led to the development of deep-learning based models to accurately diagnose complex dermatological diseases such as melanoma in adult datasets. Models for pediatric dermatology remain scarce, but recent studies have shown applications in the diagnoses of facial infantile hemangiomas and X-linked hypohidrotic ectodermal dysplasia; however, we see unmet needs in other complex clinical scenarios and rare diseases, such as diagnosing squamous cell carcinoma in patients with epidermolysis bullosa. Given the still limited number of pediatric dermatologists, especially in rural areas, AI has the potential to help overcome health disparities by helping primary care physicians treat or triage patients.

The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks.

Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows.

A machine learning analysis to evaluate the outcome measures in inflammatory myopathies.

OBJECTIVE: To assess the long-term outcome in patients with Idiopathic Inflammatory Myopathies (IIM), focusing on damage and activity disease indexes using artificial intelligence (AI). BACKGROUND: IIM are a group of rare diseases characterized by involvement of different organs in addition to the musculoskeletal. Machine Learning analyses large amounts of information, using different algorithms, decision-making processes and self-learning neural networks. METHODS: We evaluate the long-term outcome of 103 patients with IIM, diagnosed on 2017 EULAR/ACR criteria. We considered different parameters, including clinical manifestations and organ involvement, number and type of treatments, serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score), disability (HAQ-DI score), disease damage (MDI score), and physician and patient global assessment (PGA). The data collected were analysed, applying, with R, supervised ML algorithms such as lasso, ridge, elastic net, classification, and regression trees (CART), random forest and support vector machines (SVM) to find the factors that best predict disease outcome. RESULTS AND CONCLUSION: Using artificial intelligence algorithms we identified the parameters that best correlate with the disease outcome in IIM. The best result was on MMT8 at follow-up, predicted by a CART regression tree algorithm. MITAX was predicted based on clinical features such as the presence of RP-ILD and skin involvement. A good predictive capacity was also demonstrated on damage scores: MDI and HAQ-DI. In the future Machine Learning will allow us to identify the strengths or weaknesses of the composite disease activity and damage scores, to validate new criteria or to implement classification criteria.

Artificial intelligence in cardiovascular prevention: new ways will open new doors.

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

Personal Data for Public Benefit: The Regulatory Determinants of Social Licence for Technologically Enhanced Antimicrobial Resistance Surveillance.

Technologically enhanced surveillance systems have been proposed for the task of monitoring and responding to antimicrobial resistance (AMR) in both human, animal and environmental contexts. The use of these systems is in their infancy, although the advent of COVID-19 has progressed similar technologies in response to that pandemic. We conducted qualitative research to identify the Australian public's key concerns about the ethical, legal and social implications of an artificial intelligence (AI) and machine learning-enhanced One Health AMR surveillance system. Our study provides preliminary evidence of public support for AI/machine learning-enhanced One Health monitoring systems for AMR, provided that three main conditions are met: personal health care data must be deidentified; data use and access must be tightly regulated under strong governance; and the system must generate high-quality, reliable analyses to guide trusted health care decision-makers.

Medicine in the Era of Artificial Intelligence: Hey Chatbot, Write Me an H&P.

This Perspective envisions a world where artificial intelligence is integrated into health care.

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System.

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient's activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.