AI in Healthcare: Uses, Examples & Benefits
This collaboration between human expertise and machine precision allows for meticulous monitoring and enhancement of surgical actions through AI55. Traditionally, orthopedists diagnose RCT by interpreting MRI data, but deep learning systems using 3D convolutional neural networks (CNN) have been created for automated, accurate diagnosis. In shoulder surgery, AI is also progressing in the diagnosis and treatment of conditions such as rotator cuff tears (RCT). The adoption of AI in shoulder surgery is more recent, with a growing number of reports but limited comprehensive studies43. The study described a case where AI-powered image guidance helped surgeons achieve a more complete tumor resection during delicate brain surgery, potentially leading to improved patient outcomes.
Meanwhile, low-quality conference papers provide preliminary insights into emerging trends and early applications of AI-robotics synergy, though they often lack rigorous empirical data [4, 21]. The synthesis integrates findings across 25 peer-reviewed sources, exploring the role, effectiveness, and implications of AI-assisted robotic surgery. Together, these studies paint a comprehensive picture of a healthcare paradigm that is rapidly transforming, balancing innovation with practical and ethical concerns, and setting the stage for more personalized, precise, and efficient surgical care [7, 24]. Most studies fall into narrative or systematic reviews, reflecting the rapid evolution of the field and the need to synthesize emerging evidence [1, 5]. Table 1 reveals a diverse landscape of research focused on the integration of robotics and AI in modern surgical practice.
Furthermore, transparency and interpretability in decision-making mechanisms enhance the traceability and accountability of clinical recommendations. Variations in hospital infrastructure necessitate customized solutions for each institution, often resulting in efficiency challenges in terms of both cost and time. In order for AI to deliver effective and reliable results, it requires high-quality, structured, and accessible data, thereby underscoring the need for more organized data management in clinical settings.
Many physiological and neurological factors affect how someone walks, given the complex interactions between the sense of touch, the brain, the nervous system, and the muscles involved. Autonomous systems respond to real world conditions, make decisions, and perform actions with minimal or no interaction with a human (19). New tools based on AI have been developed to predict disease recurrence and progression (4) or response to treatment; and robotics, often categorized as a branch of AI, plays an increasing role in patient care.
DNAXplore | Human DNA Research et al. demonstrate cost savings from reduced revision surgeries and shorter hospitalizations in spinal surgeries using robotic AI assistance, emphasizing tangible economic benefits alongside clinical gains. Thakre and Patel report that in implant dentistry, robotic AI assistance shortens procedure time by 30%, increasing patient throughput and clinic profitability. Liu et al. review the evolution of surgical robot systems and highlight that as AI algorithms mature and hardware becomes more affordable, the cost–benefit balance will increasingly favor robotic surgery. Conversely, Banbhrani et al. caution that without proper training and integration, costs may increase due to extended operative times and technology underutilization. They also emphasize AI’s role in predictive maintenance of robotic systems, reducing downtime and costly repairs. Lai et al. provide an in-depth economic evaluation showing that robotic-assisted surgery, when applied appropriately, leads to significant savings.
The Cleveland Clinic teamed up with IBM on the Discovery Accelerator, an AI-infused initiative focused on faster healthcare breakthroughs. The Robotics Institute at Carnegie Mellon University developed HeartLander, a miniature mobile robot designed to facilitate therapy on the heart. Findhelp builds technology designed to help organizations like health systems, government agencies and nonprofits identify social needs and efficiently connect people with the right care resources.
Additionally, the absence of standardized performance metrics complicates cross-study comparisons and limits the synthesis of meaningful evidence . Together, these developments reflect a paradigm shift in surgical practice, one increasingly augmented by intelligent automation. Economic studies also suggest these technologies can be cost-effective, particularly when accounting for better outcomes and shorter hospital stays . With global healthcare under pressure from aging populations, clinician shortages, and rising costs, AI-assisted robotic surgery presents a promising path forward.
The study reported that RARP was not cost-effective compared to ORP in either high- or middle-income countries. They noted that the primary advantage of robotic surgery over laparoscopy lies in the ability to perform the procedure through a single port. Studies based on early-stage, limited case series have generally concluded that robotic surgery is not cost-effective in fields such as urology, gynecology, and general surgery [41–45].
Nevertheless, AI-assisted digital pathology systems are now increasingly used to enhance diagnostic accuracy through the objective analysis of cellular morphology, tissue architecture, staining patterns, and proliferative activity. While image analysis in pathology has not progressed as rapidly as in radiology, recent developments have significantly advanced the field. Today, AI-based systems have received FDA and CE approvals and are actively utilized in clinical settings as radiologic decision support tools. The availability of large volumes of digitized data became a driving force behind AI research in medical imaging.
The initial integration of AI in robotic surgery focused on automating specific surgical tasks, such as suturing or tissue dissection. Early applications focused on tasks like analyzing medical images for cancer detection or predicting patient outcomes7–9. Subsequent advancements led to the creation of more sophisticated robotic arms with improved dexterity and control. Artificial intelligence (AI) encompasses a range of intelligent technologies that can learn, reason, and make decisions without explicit programming. Compared to traditional laparoscopic surgery, it offers enhanced dexterity, improved visualization, and reduced tremors, leading to several benefits for patients.
Meanwhile, AI quietly monitors thousands of patients, identifying those who may be at risk of developing serious complications long before symptoms become obvious. Learn about the latest advancements in health and life sciences technology and how technology is being used to improve care, deliver enhanced patient experiences, forward discovery, and optimize operations. Ongoing innovation and the discovery of new applications for AI and IoT technologies within the field of medical robotics will help increase automation, drive efficiencies, and solve some of our greatest healthcare challenges. These “friendly” AMRs can be used in long-term care environments to provide social interaction and monitoring. When equipped with computer vision or mapping capabilities, AMRs can self-navigate to patients in exam or hospital rooms, allowing clinicians to interact from afar as needed. These robots help surgeons achieve new levels of speed and accuracy while performing complex operations with AI- and computer vision‒capable technologies.