Transforming Healthcare with Artificial Intelligence: Innovations, Applications, and Future Challenges

Tufael^1*, Atikur Rahmen Sunnay²

This review shows AI’s transformative potential in healthcare, enhancing diagnostic accuracy, patient care, and operational efficiency across medical disciplines.

Abstract

Background: The integration of artificial intelligence (AI) in healthcare has significantly transformed clinical practices, offering substantial improvements in diagnosis, treatment planning, and patient outcome predictions. AI technologies, including artificial neural networks, fuzzy expert systems, and hybrid intelligent systems, are advancing the field of augmented medicine by combining AI with traditional healthcare practices. Methods: This study reviews the diverse applications of AI in healthcare, focusing on its impact on clinical procedures, disease detection, and healthcare management. The analysis covers the use of AI-driven tools such as surgical navigation systems, augmented reality for pain management, and machine learning algorithms for early disease detection and clinical documentation. Results: AI technologies like AccuVein and augmented reality headsets have enhanced clinical procedures such as intravenous placements and surgical interventions. Advances in machine learning, particularly neural networks and deep learning, have improved the detection of complex patterns in imaging data, facilitating early diagnosis of diseases like cancer and pneumonia. Natural language processing (NLP) has enhanced the analysis and classification of clinical documentation, while robotic process automation (RPA) has optimized administrative tasks. AI's role in managing infectious diseases, particularly during the COVID-19 pandemic, has been critical, demonstrating its potential in screening, diagnosis, and treatment surveillance. AI applications in oncology and laboratory medicine have also shown increased accuracy and efficiency in disease diagnosis and patient care. Conclusion: AI is revolutionizing healthcare by enhancing diagnostic accuracy, treatment efficacy, and patient care quality. Despite its transformative potential, challenges such as legal accountability and data bias must be addressed for successful integration into healthcare systems. Continued research and innovation in AI applications are essential to maximizing its benefits while minimizing associated risks.

Keywords: Artificial Intelligence, Augmented Medicine, Machine Learning, Deep Learning, Natural Language Processing, Robotic Process Automation.

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