The convergence of artificial intelligence (AI), the Internet of Things (IoT), robotics, and advanced data analytics was transforming healthcare delivery, giving rise to autonomous, AI-powered smart hospitals and precision medicine. This chapter explores the integration of AI-driven clinical decision support systems, multimodal data fusion, and adaptive treatment strategies to optimize patient outcomes and operational efficiency. Real-time incorporation of electronic health records (EHR), edge and cloud computing infrastructures, and intelligent robotic systems enables predictive, personalized, and responsive care while minimizing clinical errors and resource bottlenecks. Continuous learning AI models, combined with ethical deployment frameworks and explainable algorithms, ensure transparency, safety, and trust in high-stakes clinical environments. The chapter also examines the role of workflow automation, dynamic scheduling, and infrastructure optimization in creating resilient and sustainable hospital ecosystems. By highlighting emerging trends, technological challenges, and future research directions, this work provides a comprehensive perspective on the evolution of autonomous healthcare systems and their implications for precision medicine, operational efficiency, and patient-centric care.
The healthcare sector was undergoing a profound transformation driven by the convergence of artificial intelligence (AI), the Internet of Things (IoT), robotics, and big data analytics [1]. Traditional healthcare delivery models often struggle with inefficiencies, delayed interventions, and fragmented patient data, which limit both clinical and operational outcomes [2]. Autonomous AI-powered smart hospitals are emerging as a solution to these challenges, offering real-time decision support, workflow automation, and predictive healthcare management [3]. By integrating AI into clinical processes, hospitals can leverage large-scale patient data, including electronic health records (EHRs), imaging studies, biosignals, and genomic profiles, to enable precision medicine [4]. This shift from reactive to proactive care allows clinicians to anticipate patient deterioration, optimize treatment plans, and implement personalized interventions, ensuring higher accuracy and timeliness in patient management. AI-driven hospital systems facilitate efficient resource utilization by predicting equipment demand, optimizing staff schedules, and streamlining logistics, thereby reducing operational bottlenecks and enhancing overall patient throughput [5].
The integration of multimodal data was central to the development of autonomous smart hospitals. Hospitals generate vast amounts of heterogeneous data through wearable devices, bedside monitors, laboratory tests, and imaging systems, which often remain siloed in traditional infrastructures [6]. Advanced AI techniques, such as deep learning, reinforcement learning, and hybrid intelligent models, enable the synthesis of these disparate datasets, allowing for accurate disease prediction, risk assessment, and treatment personalization [7]. Multimodal data integration supports dynamic clinical decision-making, where patient responses to interventions are continuously monitored and therapies are adjusted in real time [8]. Edge and cloud computing architectures further enhance this capability by enabling low-latency processing at the point of care and scalable analytics for longitudinal data [9]. This combination ensures that both immediate clinical needs and long-term treatment outcomes are addressed effectively, promoting adaptive, patient-centered care in complex hospital environments [10].
Robotics and automation are also redefining hospital operations, complementing AI-driven analytics in enhancing efficiency and safety [11]. Robotic-assisted surgeries, autonomous medication delivery, patient transport systems, and automated sterilization workflows reduce human error, minimize operational delays, and optimize clinical outcomes [12]. Intelligent scheduling algorithms further enhance workflow management by predicting patient inflow, optimizing staff allocation, and coordinating equipment usage [13]. The integration of robotics with AI-powered decision support creates a cohesive operational ecosystem capable of responding dynamically to changes in clinical demand, emergency situations, and resource availability [14]. This integration not only supports efficient hospital management but also allows medical professionals to focus on critical decision-making, diagnostics, and patient interaction, fostering a more effective and responsive healthcare environment [15].
