Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology

Artificial intelligence in global health: An unfair future for health in Sub-Saharan Africa?

Artificial intelligence (AI) holds transformative potential for global health, particularly in underdeveloped regions like Africa. However, the integration of AI into healthcare systems raises significant concerns regarding equity and fairness. This debate paper explores the challenges and risks associated with implementing AI in healthcare in Africa, focusing on the lack of infrastructure, data quality issues, and inadequate governance frameworks. It also explores the geopolitical and economic dynamics that exacerbate these disparities, including the impact of global competition and weakened international institutions. While highlighting the risks, the paper acknowledges the potential benefits of AI, including improved healthcare access, standardization of care, and enhanced health communication. To ensure equitable outcomes, it advocates for targeted policy measures, including infrastructure investment, capacity building, regulatory frameworks, and international collaboration. This comprehensive approach is essential to mitigate risks, harness the benefits of AI, and promote social justice in global health.

Accurately assessing congenital heart disease using artificial intelligence

Congenital heart disease (CHD) remains a significant global health challenge, particularly contributing to newborn mortality, with the highest rates observed in middle- and low-income countries due to limited healthcare resources. Machine learning (ML) presents a promising solution by developing predictive models that more accurately assess the risk of mortality associated with CHD. These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. Despite notable advancements, ongoing research continues to explore the full potential of ML in the identification of CHD. The proposed article provides a comprehensive analysis of the ML methods for the diagnosis of CHD in the last eight years. The study also describes different data sets available for CHD research, discussing their characteristics, collection methods, and relevance to ML applications. In addition, the article also evaluates the strengths and weaknesses of existing algorithms, offering a critical review of their performance and limitations. Finally, the article proposes several promising directions for future research, with the aim of further improving the efficacy of ML in the diagnosis and treatment of CHD.

Role of artificial intelligence in early detection of congenital heart diseases in neonates

In the domain of healthcare, most importantly pediatric healthcare, the role of artificial intelligence (AI) has significantly impacted the medical field. Congenital heart diseases represent a group of heart diseases that are known to be some of the most critical cardiac conditions present at birth. These heart diseases need a swift diagnosis as well as an intervention to ensure the wellbeing of newborns. Fortunately, with the help of AI, including the highly advanced algorithms, analytics and imaging involved, it provides us with a promising era for neonatal care. This article reviewed published data in PubMed, Science Direct, UpToDate, and Google Scholar between the years 2015-2023. To conclude The use of artificial intelligence in detecting congenital heart diseases has shown great promise in improving the accuracy and efficiency of diagnosis. Several studies have demonstrated the efficacy of AI-based approaches for diagnosing congenital heart diseases, with results indicating that the systems can achieve high levels of sensitivity and specificity. In addition, AI can help reduce the workload of healthcare professionals allowing them to focus on other critical aspects of patient care. Despite the potential benefits of using AI, in addition to detecting congenital heart disease, there are still some challenges to overcome, such as the need for large amounts of high-quality data and the requirement for careful validation of the algorithms. Nevertheless, with ongoing research and development, AI is likely to become an increasingly valuable tool for improving the diagnosis and treatment of congenital heart diseases.

Digital Transformation in the Diagnostics and Therapy of Cardiovascular Diseases: Comprehensive Literature Review

BACKGROUND

The digital transformation of our health care system has experienced a clear shift in the last few years due to political, medical, and technical innovations and reorganization. In particular, the cardiovascular field has undergone a significant change, with new broad perspectives in terms of optimized treatment strategies for patients nowadays.

OBJECTIVE

After a short historical introduction, this comprehensive literature review aimed to provide a detailed overview of the scientific evidence regarding digitalization in the diagnostics and therapy of cardiovascular diseases (CVDs).

METHODS

We performed an extensive literature search of the PubMed database and included all related articles that were published as of March 2022. Of the 3021 studies identified, 1639 (54.25%) studies were selected for a structured analysis and presentation (original articles: n=1273, 77.67%; reviews or comments: n=366, 22.33%). In addition to studies on CVDs in general, 829 studies could be assigned to a specific CVD with a diagnostic and therapeutic approach. For data presentation, all 829 publications were grouped into 6 categories of CVDs.

RESULTS

Evidence-based innovations in the cardiovascular field cover a wide medical spectrum, starting from the diagnosis of congenital heart diseases or arrhythmias and overoptimized workflows in the emergency care setting of acute myocardial infarction to telemedical care for patients having chronic diseases such as heart failure, coronary artery disease, or hypertension. The use of smartphones and wearables as well as the integration of artificial intelligence provides important tools for location-independent medical care and the prevention of adverse events.

CONCLUSIONS

Digital transformation has opened up multiple new perspectives in the cardiovascular field, with rapidly expanding scientific evidence. Beyond important improvements in terms of patient care, these innovations are also capable of reducing costs for our health care system. In the next few years, digital transformation will continue to revolutionize the field of cardiovascular medicine and broaden our medical and scientific horizons.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients' lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.

Artificial Intelligence in Congenital Heart Disease: Current State and Prospects

The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence-based deployment in congenital heart disease.

Study protocol for a pilot prospective, observational study investigating the condition suggestion and urgency advice accuracy of a symptom assessment app in sub-Saharan Africa: the AFYA-'Health' Study

INTRODUCTION

Due to a global shortage of healthcare workers, there is a lack of basic healthcare for 4 billion people worldwide, particularly affecting low-income and middle-income countries. The utilisation of AI-based healthcare tools such as symptom assessment applications (SAAs) has the potential to reduce the burden on healthcare systems. The purpose of the AFYA Study (AI-based Assessment oF health sYmptoms in TAnzania) is to evaluate the accuracy of the condition suggestions and urgency advice provided by a user on a Swahili language Ada SAA.

METHODS AND ANALYSIS

This study is designed as an observational prospective clinical study. The setting is a waiting room of a Tanzanian district hospital. It will include patients entering the outpatient clinic with various conditions and age groups, including children and adolescents. Patients will be asked to use the SAA before proceeding to usual care. After usual care, they will have a consultation with a study-provided physician. Patients and healthcare practitioners will be blinded to the SAA's results. An expert panel will compare the Ada SAA's condition suggestions and urgency advice to usual care and study provided differential diagnoses and triage. The primary outcome measures are the accuracy and comprehensiveness of the Ada SAA evaluated against the gold standard differential diagnoses.

ETHICS AND DISSEMINATION

Ethical approval was received by the ethics committee (EC) of Muhimbili University of Health and Allied Sciences with an approval number MUHAS-REC-09-2019-044 and the National Institute for Medical Research, NIMR/HQ/R.8c/Vol. I/922. All amendments to the protocol are reported and adapted on the basis of the requirements of the EC. The results from this study will be submitted to peer-reviewed journals, local and international stakeholders, and will be communicated in editorials/articles by Ada Health.

TRIAL REGISTRATION NUMBER

NCT04958577.

Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans

In such a brief period, the recent coronavirus (COVID-19) already infected large populations worldwide. Diagnosing an infected individual requires a Real-Time Polymerase Chain Reaction (RT-PCR) test, which can become expensive and limited in most developing countries, making them rely on alternatives like Chest X-Rays (CXR) or Computerized Tomography (CT) scans. However, results from these imaging approaches radiated confusion for medical experts due to their similarities with other diseases like pneumonia. Other solutions based on Deep Convolutional Neural Network (DCNN) recently improved and automated the diagnosis of COVID-19 from CXRs and CT scans. However, upon examination, most proposed studies focused primarily on accuracy rather than deployment and reproduction, which may cause them to become difficult to reproduce and implement in locations with inadequate computing resources. Therefore, instead of focusing only on accuracy, this work investigated the effects of parameter reduction through a proposed truncation method and analyzed its effects. Various DCNNs had their architectures truncated, which retained only their initial core block, reducing their parameter sizes to <1 M. Once trained and validated, findings have shown that a DCNN with robust layer aggregations like the InceptionResNetV2 had less vulnerability to the adverse effects of the proposed truncation. The results also showed that from its full-length size of 55 M with 98.67% accuracy, the proposed truncation reduced its parameters to only 441 K and still attained an accuracy of 97.41%, outperforming other studies based on its size to performance ratio.

Artificial intelligence in pediatric cardiology: taking baby steps in the big world of data

PURPOSE OF REVIEW

Artificial intelligence (AI) has changed virtually every aspect of modern life, and medicine is no exception. Pediatric cardiology is both a perceptual and a cognitive subspecialty that involves complex decision-making, so AI is a particularly attractive tool for this medical discipline. This review summarizes the foundational work and incremental progress made as AI applications have emerged in pediatric cardiology since 2020.

RECENT FINDINGS

AI-based algorithms can be useful for pediatric cardiology in many areas, including: (1) clinical examination and diagnosis, (2) image processing, (3) planning and management of cardiac interventions, (4) prognosis and risk stratification, (5) omics and precision medicine, and (6) fetal cardiology. Most AI initiatives showcased in medical journals seem to work well in silico, but progress toward implementation in actual clinical practice has been more limited. Several barriers to implementation are identified, some encountered throughout medicine generally, and others specific to pediatric cardiology.

SUMMARY

Despite barriers to acceptance in clinical practice, AI is already establishing a durable role in pediatric cardiology. Its potential remains great, but to fully realize its benefits, substantial investment to develop and refine AI for pediatric cardiology applications will be necessary to overcome the challenges of implementation.

A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement

Background: Precision medicine focuses on the identification of therapeutic strategies that are effective for a group of patients based on similar unifying characteristics. The recent success of precision medicine in non-critical care settings has resulted from the confluence of large clinical and biospecimen repositories, innovative bioinformatics, and novel trial designs. Similar advances for precision medicine in sepsis and in the acute respiratory distress syndrome (ARDS) are possible but will require further investigation and significant investment in infrastructure. Methods: This project was funded by the American Thoracic Society Board of Directors. A multidisciplinary and diverse working group reviewed the available literature, established a conceptual framework, and iteratively developed recommendations for the Precision Medicine Research Agenda for Sepsis and ARDS. Results: The following six priority recommendations were developed by the working group: 1) the creation of large richly phenotyped and harmonized knowledge networks of clinical, imaging, and multianalyte molecular data for sepsis and ARDS; 2) the implementation of novel trial designs, including adaptive designs, and embedding trial procedures in the electronic health record; 3) continued innovation in the data science and engineering methods required to identify heterogeneity of treatment effect; 4) further development of the tools necessary for the real-time application of precision medicine approaches; 5) work to ensure that precision medicine strategies are applicable and available to a broad range of patients varying across differing racial, ethnic, socioeconomic, and demographic groups; and 6) the securement and maintenance of adequate and sustainable funding for precision medicine efforts. Conclusions: Precision medicine approaches that incorporate variability in genomic, biologic, and environmental factors may provide a path forward for better individualizing the delivery of therapies and improving care for patients with sepsis and ARDS.

What to Do for Increasing Cancer Burden on the African Continent? Accelerating Public Health Diagnostics Innovation for Prevention and Early Intervention on Cancers

No other place illustrates the increasing burden of cancer than in Africa and in particular, sub-Saharan Africa. Many of the individuals to be diagnosed with cancer will be in low-resource settings in the future due to, for example, an increase in populations and aging, and high co-morbidity with infections with viruses such as human immunodeficiency virus (HIV) and human papillomavirus (HPV), as well as the presence of infectious agents linked to cancer development. Due to lack of prevention and diagnostic innovation, patients present with advanced cancers, leading to poor survival and increased mortality. HIV infection-associated cancers such as B cell lymphomas, Kaposi's sarcoma, and HPV-associated cancers such as cervical cancer are particularly noteworthy in this context. Recent reports show that a host of other cancers are also associated with viral infection and these include lung, oral cavity, esophageal, and pharyngeal, hepatocellular carcinoma, and anal and vulvar cancers. This article examines the ways in which diagnostic innovation empowered by integrative biology and informed by public health priorities can improve cancer prevention or early intervention in Africa and beyond. In addition, I argue that because diagnostic biomarkers can often overlap with novel therapeutic targets, diagnostics research and development can have broader value for and impact on medical innovation.

Coronavirus Disease 19 and Future Ecological Crises: Hopes from Epigenomics and Unraveling Genome Regulation in Humans and Infectious Agents

With coronavirus disease 19 (COVID-19), we have witnessed a shift from public health to planetary health and a growing recognition of the importance of systems science in developing effective solutions against pandemics in the 21st century. COVID-19 and the history of frequent infectious outbreaks in the last two decades suggest that COVID-19 is likely a dry run for future ecological crises. Now is the right time to plan ahead and deploy the armamentarium of systems science scholarship for planetary health. The science of epigenomics, which investigates both genetic and nongenetic traits regarding heritable phenotypic alterations, and new approaches to understanding genome regulation in humans and pathogens offer veritable prospects to boost the global scientific capacities to innovate therapeutics and diagnostics against novel and existing infectious agents. Several reversible epigenetic alterations, such as chromatin remodeling and histone methylation, control and influence gene expression. COVID-19 lethality is linked, in part, to the cytokine storm, age, and status of the immune system in a given person. Additionally, due to reduced human mobility and daily activities, effects of the pandemic on the environment have been both positive and negative. For example, reduction in environmental pollution and lesser extraction from nature have potential positive corollaries on water and air quality. Negative effects include pollution as plastics and other materials were disposed in unconventional places and spaces in the course of the pandemic. I discuss the opportunities and challenges associated with the science of epigenomics, specifically with an eye to inform and prevent future ecological crises and pandemics that are looming on the horizon in the 21st century. In particular, this article underscores that epigenetics of both viruses and the host may influence virus infectivity and severity of attendant disease.

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

Stem cells are primitive and precursor cells with the potential to reproduce into diverse mature and functional cell types in the body throughout the developmental stages of life. Their remarkable potential has led to numerous medical discoveries and breakthroughs in science. As a result, stem cell-based therapy has emerged as a new subspecialty in medicine. One promising stem cell being investigated is the induced pluripotent stem cell (iPSC), which is obtained by genetically reprogramming mature cells to convert them into embryonic-like stem cells. These iPSCs are used to study the onset of disease, drug development, and medical therapies. However, functional studies on iPSCs involve the analysis of iPSC-derived colonies through manual identification, which is time-consuming, error-prone, and training-dependent. Thus, an automated instrument for the analysis of iPSC colonies is needed. Recently, artificial intelligence (AI) has emerged as a novel technology to tackle this challenge. In particular, deep learning, a subfield of AI, offers an automated platform for analyzing iPSC colonies and other colony-forming stem cells. Deep learning rectifies data features using a convolutional neural network (CNN), a type of multi-layered neural network that can play an innovative role in image recognition. CNNs are able to distinguish cells with high accuracy based on morphologic and textural changes. Therefore, CNNs have the potential to create a future field of deep learning tasks aimed at solving various challenges in stem cell studies. This review discusses the progress and future of CNNs in stem cell imaging for therapy and research.

Pharmacogenomics at the center of precision medicine: challenges and perspective in an era of Big Data

Pharmacogenomics (PGx) is one of the core elements of personalized medicine. PGx information reduces the likelihood of adverse drug reactions and optimizes therapeutic efficacy. St Catherine Specialty Hospital in Zagreb/Zabok, Croatia has implemented a personalized patient approach using the RightMed^® Comprehensive PGx panel of 25 pharmacogenes plus Facor V Leiden, Factor II and MTHFR genes, which is interpreted by a special counseling team to offer the best quality of care. With the advent of significant technological advances comes another challenge: how can we harness the data to inform clinically actionable measures and how can we use it to develop better predictive risk models? We propose to apply the principles artificial intelligence to develop a medication optimization platform to prevent, manage and treat different diseases.