The rise of modern technologies surgery in healthcare systems: a research highlight

AI includes numerous variations which can be utilized in medical practice such as machine learning (ML) technology, ML analyzes the data and estimates outcomes which helps with diagnosis and severity assessments (2)(5). Computer vision (CV), which analyzes intra-operative videos and helps with decision making (2). Robotics, which is a structured system that functions as a remote arm for surgeons to do the procedure accurately and precisely (2). In fact, the AI-derived robotic systems have become highly dominant across multiple surgical specialties (figure1) with da Vinci Xi being the most recent and the most prominent robotic system nowadays (4). It is provided with robotic arm, surgeon-controlled console with high-definition 3D vision and instrument control interfaces which offer enhanced precision, stability, and dexterity (5)(4). AI was also used in developing many innovations that improve patient outcomes such as digital twin simulation, image-based tissue segmentation, and vision models,etc. (1). The digital twin simulation is a virtual copy of patient’s body or a specific organ which enables surgeons to practice and plan the procedure (1). AI also helps in simulating different scenarios on that model to see how the patient will respond (1). Image-based tissue segmentation is a technology that engages in many machines, it highlights and identifies different tissues for the surgeon (muscles, blood vessels, nerves...) which decreases the rate of mistakes and errors (1).

The AI-assisted robotic surgery highly improved patient outcomes and surgical care (1)(3). This improvement is evident in procedural performance with increased precision by 40% and decreased operation time by 25% (1). In addition, surgical outcomes have been enhanced through decreased intra-operative complications by 30% and accelerated patient recovery (1)(4). Besides supporting workforce wellness by decreasing their fatigue and providing clinical decision support (1). It is considered the best suitable technique for complex cases that need minimally invasive procedures (1)(4)(5). Many technologies contributed to these statistics; patient safety was ensured by AI-assisted intra-operative video analysis that provides real-time error correction and procedural guidance (1)(4)(5). Also, by the large vision models that enabled the surgeon to interpret complex surgical fields and understand their anatomy leading to decreasing damage to surrounding structures (1). And by AI-assisted monitors for postoperative recovery enabling early detection of complications like pain or infection (5). Clinical effectiveness is achieved through AI algorithms that enhanced robotic precision in procedures like prostatectomy, nephrectomy, and tumor resections (1)(4), and through neuro-adaptive control technology that help robotic arms move more accurately avoiding the human shaky movements and unintended errors (1). Even in orthopedics, AI-assisted approach was reported to have lower incidence of screw displacement 2.5% in thoraco-lumbar surgeries (1).

The majority of people identify this technology as an economic burden and there is no benefit from it financially and the benefit is only limited to healthcare. However, several research papers reported that AI robotic surgery is cost-effective and potentially rewarding (1)(2). It is reported that this technology decreased overall healthcare costs by 10% compared with conventional procedures (1). As increasing proficiency and surgical accuracy will decrease complications and errors which means decreased hospital stays and readmissions leading to more available beds and prevention of resources drainage (1). Facilitating a feature such as digital twin simulation providing realistic feedback will relieve the hospitals and doctors from paying for training programs and courses (1)(5)(2). In addition to administrative automation so the organizations do not have to pay substantial costs for databases processing (2). AI also facilitates remote, telesurgery and virtual assessments from highly trained doctors from different countries (4)(2). But in order to realize these economic gains, there should be an initial high investment in infrastructure (1). The high cost of acquisition and maintenance, the complex setup and the need for trained personnel make it very difficult for low middle income countries (LMIC) to acquire this technology (1)(4). Which leads AI to a larger debate.

It is reported that AI-assisted robotic surgery critically interferes with aim of global surgery which is international equitable surgical care (1) (2). The limitation of this technology to certain countries that are capable of its acquisition even increases the risk of discrimination bias and minority misrepresentation (1)(2)(3). As dealing with data from homogenous population increases risk of inequitable treatment outcomes especially with diverse patient groups (1)(2). Moreover, the countries that have this technology differ in the features they have and their degrees so there are no standardized protocols for validation, approval or clinical integration (1) which potentially risks patient safety and autonomy (1)(3).

The ambiguity of AI and unknowing black box nature of machine learning raise a lot of concerns towards patient data privacy and security (1)(2)(3)(5). And due to sensitive nature of these data, this increases risk of patient stress, identity theft, and unreliable record (3). Especially with uncertainty towards who owns the data collected and the varying laws regarding medical records ownership risking patient rights and privacy (3). Also, the doubt towards clinical outcomes transparency and how AI is working raised questions toward the validity of patient consent (3). It affects doctor-patient relationship especially after involvement of AI in decision making (1)(2)(3). Besides the rising dilemma of who will carry the responsibility of potential surgical error and whether it lies with the surgeon, the AI system, or its developers (1).

There is a concerted direction to overcome these challenges to ensure safe, efficient, and equal health service. Collective governmental cooperation is needed to develop AI tools and human resources to solve global disparity and disclose discrimination bias (3)(1)(2). Raising awareness among patients is crucial to ensure a valid informed consent explaining risks, benefits and alternative treatment options and respecting patients’ autonomy if they refused (3). AI health companies must follow HIPAA and start to deidentify the personal health information before storing it on databases to guarantee patient data privacy and safety (3). Health organizations should encourage doctors more to master AI-assisted technology besides their manual skills (1)(3). And most importantly, doctors should balance between AI recommendations and their own clinical judgment in decision making and ensure the dominance of human supervision to restore patients’ trust and safety (1)(3). And finally, doctors that have employment uncertainty and hesitancy should be reassured as AI usage is a tool to make the work easier for them not to replace them (2).