# Artificial Intelligence Algorithms for Healthcare
###### Abstract
The human element remains irreplaceable in healthcare, and AI should be seen as a tool to enhance, not replace, the expertise and compassionate care provided by human professionals. As we move forward, AI's ethical implications and societal impact on healthcare must be carefully considered. The development and implementation of AI technologies should be guided by fairness, transparency, and accountability principles, ensuring that these innovations benefit all segments of society equitably [12]. Engaging in a continuous dialogue among technologists, healthcare professionals, ethicists, and the public is essential to navigating the complexities and realizing AI's full potential to improve human health. AI algorithms are crucial in advancing healthcare, offering benefits in patient care, operational efficiency, and medical research. As this area continues to grow, it is crucial to tackle the ethical and practical challenges that come with it. This Special Issue aims to educate the community about unique and new research on applying artificial intelligence methods and tools to various aspects of healthcare and medical research. The Special Issue includes original theoretical and empirical studies and reviews in machine learning, data mining, modeling, information technologies, and artificial intelligence in the healthcare context. We received 24 submissions, 9 of which, after a careful review process, were accepted for publication in this Special Issue. The paper \"Pose-Based Gait Analysis for Diagnosis of Parkinson's Disease\" explores the application of markerless motion capture technology, explicitly utilizing the AlphaPose system, for the early detection of Parkinson's Disease (PD) through gait analysis. The study is grounded in the premise that gait characteristics can provide critical insights into early PD symptoms. The methodology employs human pose estimation from video recordings to extract relevant gait features without physical markers, highlighting a non-invasive approach to diagnosing neurological conditions. The research identifies five key gait features indicative of PD, leveraging machine learning algorithms to analyze these features across different video views for robust PD detection. The results demonstrate the method's effectiveness in distinguishing PD patients from healthy controls with high accuracy, precision, and recall levels, underscoring the potential of computer vision and artificial intelligence in medical diagnostics. The study concludes that markerless motion capture and pose estimation technologies offer a promising direction for the early diagnosis of PD, suggesting a cost-effective, accessible approach to identifying and monitoring neurological conditions. This research contributes to the growing body of work on non-invasive diagnostic tools, opening avenues for further exploration and development in digital health and telemedicine. The authors of the paper \"Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform\" present a novel sensor-based Human Activity Recognition (HAR) approach utilizing a pre-trained deep learning model based on Continuous Wavelet Transform (CWT)-generated scalograms. The authors explore the impact of different CWT configurations on the performance of popular Convolutional Neural Network (CNN) architectures. After evaluating nine CNN architectures across various configurations, the study identifies the DenseNet121 architecture combined with Morlet wavelet and a scale value range of 0 to 256 as the optimal setup. This combination achieved a classification accuracy and F1 score of 97.48% and 97.52%, respectively, on the KU-HAR dataset, surpassing existing state-of-the-art models. The model's efficacy was further validated on the UCI-HAPT dataset, where it demonstrated enhanced performance, especially when employing layer-freezing techniques.