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Artificial intelligence (AI) algorithms are playing a crucial role in transforming healthcare by enhancing the quality, accessibility, and efficiency of medical care, research, and operations. These algorithms enable healthcare providers to offer more accurate diagnoses, predict outcomes, and customize treatments to individual patient needs. AI also improves operational efficiency by automating routine tasks and optimizing resource management. However, there are challenges to adopting AI in healthcare, such as data privacy concerns and potential biases in algorithms. Collaboration among stakeholders is necessary to ensure ethical use of AI and its positive impact on the field. AI also has applications in medical research, preventive medicine, and public health. It is important to recognize that AI should augment, not replace, the expertise and compassionate care provided by healthcare professionals. The ethical implications and societal impact of AI in healthcare must be carefully considered, guided by fairness, transparency, and accountability principles. Several research papers in this special issue explore the application of AI algorithms in various aspects of healthcare, such as gait analysis for Parkinson's disease diagnosis, human activity recognition, heart disease prediction, compliance assessment with clinical protocols, epidemic management, neurological complications identification, fall prevention, leukemia diagnosis, and genetic clinical pathways. These studies demonstrate the potential of AI in improving medical diagnostics, patient monitoring, and personalized care. [Extracted from the article]

The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making predictions, especially in image classification. These popular and well-documented successes use extremely deep CNNs such as VGG16, DenseNet121, and Xception. However, these well-known deep learning models use tens of millions of parameters based on a large number of pretrained filters that have been repurposed from previous data sets. Among these identified filters, a large portion contain no information yet remain as input features. Thus far, there is no effective method to omit these noisy features from a data set, and their existence negatively impacts prediction performance. In this paper, a novel interaction-based convolutional neural network (ICNN) is introduced that does not make assumptions about the relevance of local information. Instead, a model-free influence score (I-score) is proposed to directly extract the influential information from images to form important variable modules. This innovative technique replaces all pretrained filters found by trial-and-error with explainable, influential, and predictive variable sets (modules) determined by the I-score. In other words, future researchers need not rely on pretrained filters; the suggested algorithm identifies only the variables or pixels with high I-score values that are extremely predictive and important. The proposed method and algorithm were tested on real-world data set and a state-of-the-art prediction performance of 99.8% was achieved without sacrificing the explanatory power of the model. This proposed design can efficiently screen patients infected by COVID-19 before human diagnosis and can be a benchmark for addressing future XAI problems in large-scale data sets. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) algorithms can provide actionable insights for clinical decision-making and managing chronic diseases. The treatment and management of complex chronic diseases, such as diabetes, stands to benefit from novel AI algorithms analyzing the frequent real-time streaming data and the occasional medical diagnostics and laboratory test results reported in electronic health records (EHR). Novel algorithms are needed to develop trustworthy, responsible, reliable, and robust AI techniques that can handle the imperfect and imbalanced data of EHRs and inconsistencies or discrepancies with free-living self-reported information. The challenges and applications of AI for two problems in the healthcare domain were explored in this work. First, we introduced novel AI algorithms for EHRs designed to be fair and unbiased while accommodating privacy concerns in predicting treatments and outcomes. Then, we studied the innovative approach of using machine learning to improve automated insulin delivery systems through analyzing real-time information from wearable devices and historical data to identify informative trends and patterns in free-living data. Application examples in the treatment of diabetes demonstrate the benefits of AI tools for medical and health informatics. [ABSTRACT FROM AUTHOR]