06192nas a2200265   4500000000100000008004100001653002800042653002800070653002400098653002400122653002400146100001200170700001400182700001200196700001500208700001500223700001300238700001400251700001600265700001400281245011600295856026000411300001400671520524100685       2025                            d10aArtificial Intelligence10aMedical Care Automation10ahealthcare delivery10aDiagnostic Accuracy10aClinical efficiency1 aTanji E1 aParveen K1 aDutta S1 aShahrear S1 aRabbani HM1 aAkter MR1 aRahman MS1 aChowdhury R1 aZulfiqa R00aArtificial Intelligence in Medical Care Automation Transforming Healthcare Delivery through Intelligent Systems  uhttps://www.researchgate.net/profile/Mst-Akter-62/publication/394382332_Artificial_Intelligence_in_Medical_Care_Automation_Transforming_Healthcare_Delivery_through_Intelligent_Systems/links/68949cf99a3902639b8711a1/Artificial-Intelligence-in-Medical-Care-  a3103-31093 a<p>Artificial Intelligence (AI) is rapidly transforming healthcare delivery through automation of diagnostic, clinical, and administrative processes. Intelligent systems such as machine learning algorithms, decision support tools, and automated diagnostics are increasingly adopted to improve accuracy, efficiency, and patient-centered care. However, the effectiveness of AI in real-world clinical settings requires empirical validation. This study aimed to evaluate the impact of AI-based automation on clinical efficiency, diagnostic accuracy, patient satisfaction, system usability, and acceptance of AI among healthcare providers and patients. A quantitative, cross-sectional design was employed involving 240 participants (healthcare professionals and patients) from tertiary hospitals using AI technologies. Stratified random sampling ensured representation across departments. Two structured questionnaires (5-point Likert scale) were used. Data were coded and analyzed in SPSS 23 and STATA. Statistical tests included descriptive statistics, Cronbach's alpha for reliability, Pearson's correlation, and multiple linear regression to identify predictors of patient satisfaction and clinical efficiency. Descriptive analysis showed high ratings for clinical efficiency (M = 4.12), diagnostic accuracy (M = 3.95), and patient satisfaction (M = 4.03). Reliability coefficients were acceptable (α = 0.79-0.86). Correlation analysis revealed strong relationships among system usability, diagnostic accuracy, and satisfaction (r &gt; 0.60, p &lt; 0.01). Regression models confirmed system usability (β = 0.34) and diagnostic accuracy (β = 0.29) as significant predictors of patient satisfaction (R² = 0.54), and clinical efficiency (R² = 0.49). AI-based automation significantly enhances healthcare delivery when systems are user-friendly, accurate, and well-accepted. Future implementation should focus on optimizing usability and fostering trust among users. 1. INTRODUCTION The integration of Artificial Intelligence (AI) into medical care automation is transforming the landscape of healthcare delivery, heralding a new era of intelligent, efficient, and personalized medicine. AI technologies-ranging from machine learning algorithms to natural language processing and computer vision-are now at the forefront of optimizing clinical workflows, diagnosing diseases, and enhancing patient outcomes [5]. These intelligent systems are capable of analysing vast amounts of medical data with speed and accuracy that surpass human capabilities, thereby enabling faster decision-making and reducing the burden on healthcare professionals. The automation of medical care through AI has led to significant improvements in areas such as radiology, pathology, robotic surgery, and patient monitoring. For instance, AI-powered diagnostic tools can detect anomalies in imaging studies with high precision, supporting clinicians in identifying conditions like cancer or cardiovascular disease at earlier stages [2], [3]. Furthermore, AI-driven chatbots and virtual health assistants are increasingly used in telemedicine to triage patients, provide health information, and monitor chronic conditions, improving access and continuity of care [4]. Moreover, intelligent systems are being embedded into hospital management systems to streamline administrative tasks, predict patient admissions, and optimize resource allocation, contributing to more resilient and responsive health systems [1]. As AI continues to evolve, ethical considerations, data privacy, and algorithmic transparency remain critical to ensuring equitable and safe implementation across diverse healthcare settings. In sum, AI-driven automation is not merely augmenting medical care-it is fundamentally reshaping it. Through intelligent systems, healthcare delivery is becoming more proactive, predictive, and patient-centred, promising a future where technology enhances human expertise rather than replaces it. This quantitative study aims to evaluate the transformative impact of artificial intelligence (AI)-based automation on healthcare delivery by focusing on measurable outcomes such as clinical efficiency, diagnostic accuracy, and patient satisfaction. Specifically, the study seeks to assess how AI integration influences the speed and quality of clinical decision-making compared to traditional methods. It also aims to determine the diagnostic accuracy of AI-powered tools, such as image recognition systems and predictive algorithms, in relation to standard human-based diagnoses. Additionally, the study intends to explore the relationship between the use of AI-enabled communication platforms-such as virtual assistants and automated follow-up systems-and patient satisfaction levels. Another key objective is to analyze improvements in operational efficiency, including reduced patient wait times and optimized resource utilization, in healthcare settings where AI systems are deployed. Finally, the study will examine healthcare professionals' acceptance, trust, and readiness to adopt AI technologies through structured questionnaires, enabling a comprehensive understanding of the factors influencing successful AI implementation in medical care.</p>