Medical and Life Sciences

Tele-Dentistry and Remote Consultations: The Future of Dental Care

Sai Ho Willie Chan — Wed, 05 Jun 2024 00:00:00 +0000

With social distancing measures in place, many dental practices (Chen et al., 2003) have had to close their doors or limit their services, leaving patients with limited access to dental care (Arora et al., 2014). As we know, dentistry is one of the top listed risky jobs in the COVID-19 situation (Goswami, 2016). However, one application of telehealth (Amtha et al., 2021, Torosyan et al., 2021) technology is dentistry. Hence, teledentistry and remote consultation offer a solution to this problem, allowing patients to receive dental care from the comfort of their own homes.

Integrating Artificial Intelligence in Precision Medicine: Enhancing Predictive Accuracy and Personalized Care in Chronic Disease Management

Muhammad Abdullah — Sun, 23 Jun 2024 00:00:00 +0000

This review examines the transformative potential of artificial intelligence (AI) in precision medicine, particularly within the context of chronic disease management. As chronic conditions, including cardiovascular diseases, diabetes, and cancer, remain the leading causes of mortality worldwide, there is an increasing need for innovative, individualized approaches to improve treatment outcomes. AI technologies—such as machine learning (ML), deep learning (DL), and natural language processing (NLP)—have been shown to enhance predictive accuracy, support real-time patient monitoring, and enable personalized care plans based on vast datasets and individual health profiles. This review synthesizes recent advancements in AI applications for predictive modeling, genomics, and patient-centered care in chronic disease, critically analyzing the benefits and challenges of AI integration. Ethical considerations surrounding data privacy and algorithmic bias are also discussed. The review concludes by identifying potential pathways for AI in enhancing precision medicine’s role in managing chronic disease and highlighting the need for regulatory frameworks and cross-disciplinary collaboration to overcome current limitations.

Exploring Machine Learning Algorithms in Precision Medicine: Revolutionizing Diagnostics and Treatment Personalization

Ramish Ali, Tanveer Iqbal — Mon, 28 Oct 2024 00:00:00 +0000

The integration of machine learning (ML) algorithms in precision medicine is transforming healthcare by enabling more accurate diagnostics and personalized treatment strategies. This review explores the current state of ML applications in precision medicine, highlighting capabilities in processing vast amounts of data to identify patterns that inform clinical decisions. We discuss various ML algorithms, including supervised and unsupervised learning techniques, and their role in genomics, imaging, and electronic health records. The advantages of ML in enhancing diagnostic accuracy and treatment personalization are contrasted with the challenges of data quality, algorithm transparency, and ethical considerations. Additionally, we examine case studies demonstrating successful implementations of ML in predicting disease outcomes and optimizing therapeutic regimens. Future research directions are proposed to address existing limitations, including the need for standardized protocols, validation of ML models, and the integration of diverse data sources. This review emphasizes the potential of ML algorithms to revolutionize precision medicine, ultimately leading to improved patient outcomes and more efficient healthcare systems.