Abstract
In contemporary healthcare, artificial intelligence (AI) and humanoid robotics are transformative forces, revolutionizing patient care and medical practices. AI algorithms analyze vast datasets to enhance diagnostic accuracy, enabling early disease detection and personalized treatment plans. Humanoid robots, equipped with AI, assist in repetitive tasks, patient monitoring, and even surgery, augmenting healthcare professionals' capabilities. This synergy between AI and robotics not only improves efficiency but also fosters patient engagement and empowers healthcare providers. These technologies streamline administrative processes, reduce errors, and facilitate remote patient monitoring. However, ethical considerations and the need for responsible AI deployment must be addressed. Despite challenges, the integration of AI and humanoid robotics marks a paradigm shift in healthcare, promising more precise diagnoses, efficient treatments, and ultimately, improved patient outcomes in the ever-evolving landscape of medical science.
Top1. Introduction
Based on the most recent updates, AI tools are increasingly applied in critical areas such as early detection, diagnosis, treatment, and outcome prediction for diseases like cancer, neurology, cardiology, and pathology (Denecke et al., 2022; Barua et al., 2023). These tools are designed to assist physicians, aiding in improved clinical decision-making or even replacing judgments in specific healthcare domains (Datta et al., 2019). Employing sophisticated algorithms, AI has the capacity for self-correction, ensuring enhanced accuracy (Kumar et al., 2021; Barua et al., 2024). The system employs advanced techniques to forecast health outcomes and alert on health risks by extracting vital information from a vast patient population. AI has evolved through the development of deep neural networks, natural language processing, computer vision, and robotics (Datta et al., 2019; Barua et al., 2022). Physicians find it highly beneficial as it provides comprehensive information from diverse sources such as journals, textbooks, and medical magazines, reducing diagnostic and therapeutic errors (Morrow et al., 2023). Although the term “Artificial Intelligence” was coined in 1956, its recent popularity and usage stem from advancements in algorithms, improved storage capacity, and computing power (Zeleňák et al., 2021; Waidi et al., 2023). Initially focused on basic problem-solving and symbolic methods, AI now emulates fundamental human reasoning.
Figure 1. Utilization of artificial intelligence across diverse healthcare domains
In contemporary times, personal assistants like Siri (iOS), Google Assistant (Android), Cortana, and Alexa have become ubiquitous, challenging the Hollywood portrayal of AI as menacing humanoid robots (Barua et al., 2023). AI is perceived as a force contributing positively to various industries. Notably, it continually adapts to progressive algorithms, learning to avoid obsolescence. AI is not a standalone application but an augmentation of intelligence added to existing objects, as seen with Siri in Apple products and Google Assistant in Android devices (Kumar et al., 2021). In healthcare, AI plays a pivotal role in diagnosing life-threatening diseases, including cancer, neuronal disorders, and cardiac ailments, emphasizing the significance of early detection (Morrow et al., 2023; Barua et al., 2024). Beyond these conditions, AI is applied in diagnosing congenital cataract disease and diabetic retinopathy. Industries, especially manufacturing, benefit from AI implementation, creating safer operational environments and improving production quality and quantity (Barua et al., 2023). IBM Watson, a pioneering AI deployment, brought about fundamental changes in the healthcare industry (Kumar et al., 2021). Figure 1 illustrates the widespread use of AI across various domains.
Key Terms in this Chapter
Machine Vision: Machine vision involves the use of computer vision technologies to enable machines to interpret and understand visual information from the world, similar to human vision. It encompasses image processing, pattern recognition, and artificial intelligence to extract valuable insights and make decisions based on visual data.
Artificial Intelligence: Artificial intelligence (AI) simulates human intelligence in machines, enabling them to learn, reason, and perform tasks. Applications span diverse fields, from speech recognition and autonomous vehicles to healthcare and finance. AI's impact on society, automation, and decision-making raises ethical concerns, requiring responsible development and deployment for positive societal outcomes.
Healthcare Applications: In healthcare, applications of technology include electronic health records, telemedicine, and medical imaging. Machine learning aids in disease diagnosis, treatment optimization, and drug discovery. Wearable devices and health monitoring apps enhance preventive care. These innovations improve patient outcomes, streamline processes, and contribute to the overall efficiency of healthcare delivery.
Machine Learning: A branch of artificial intelligence that enables systems to learn patterns from data, improving performance over time without explicit programming. It encompasses supervised learning, unsupervised learning, and reinforcement learning. Applications range from image recognition and natural language processing to personalized recommendations and autonomous vehicles, transforming various industries.
Precision Medicine: Precision medicine tailors healthcare interventions to individual characteristics, considering genetic, environmental, and lifestyle factors. By leveraging advanced technologies like genomics and data analytics, it enables personalized diagnosis, treatment, and prevention. This approach enhances medical outcomes, reduces adverse effects, and represents a significant stride towards more effective and individualized healthcare.