ABSTRACT
The use of AI and robotics in surgery and rehabilitation is transforming healthcare by improving accessibility, precision, and customization. Robotic devices provide for safer, more accurate, and shorter recovery periods during minimally invasive procedures. By offering real-time decision support and predictive analytics during procedures, AI enhances these developments. AI-powered solutions in rehabilitation use data from wearables and remote monitoring to generate customized treatment plans, increasing access to care, even in underprivileged areas. Through innovation, moral responsibility, and teamwork, robotics and AI are revolutionizing patient care and establishing new benchmarks for healthcare delivery. Robotic devices provide for safer, more accurate, and shorter recovery periods during minimally invasive procedures. By offering real-time decision support and predictive analytics during procedures, AI enhances these developments. AI-powered solutions in rehabilitation use data from wearables and remote monitoring to generate customized treatment plans, increasing access to care, even in underprivileged areas. Through innovation, moral responsibility, and teamwork, robotics, and AI are revolutionizing patient care and establishing new benchmarks for healthcare delivery.
KEYWORDS: AI, precision, real-time decision support, robotics
INTRODUCTION
The integration of robotics and artificial intelligence (AI) into surgery and rehabilitation is transforming modern healthcare by improving precision, personalization, and accessibility. These technologies are revolutionizing surgical procedures and rehabilitation therapies, ultimately improving patient outcomes, reducing recovery times, and enhancing healthcare efficiency.[1] Robotics has enabled minimally invasive surgery with greater accuracy, while AI offers real-time decision support and predictive analytics. Together, they are reshaping patient care and extending high-quality treatment to underserved regions, overcoming geographical barriers.[2]
Use of AI in surgery and rehabilitation
Robotics and AI have significantly impacted the fields of surgery and rehabilitation. Robotic systems, such as the da Vinci Surgical System, have enhanced minimally invasive procedures by offering unparalleled precision.[3] AI further complements these advancements by providing real-time data analysis and decision support, improving surgical outcomes.[1] In rehabilitation, AI-driven systems, including robotic exoskeletons and wearable devices, have revolutionized therapy for patients recovering from conditions like strokes or spinal injuries, providing personalized care and continuous support (Thompson et al., 2022).
Types of robotic systems
Teleoperated robots
By adopting tools like da Vinci, surgeons may operate robotic arms from a distance, improving their dexterity and vision during delicate surgeries (Smith et al., 2021).
Autonomous robots
These include the STAR robot for suturing jobs and the Mazor X system for spinal surgery, which carry out activities with little assistance from humans.[4]
Collaborative robots
Cobots help surgeons with certain activities, such as imaging guidance or suturing, which lessens physical strain and increases productivity.
Surgery-related applications
General surgery
By decreasing discomfort and recuperation durations, robotic devices enhance operations like as prostatectomies and colectomies.[3]
Orthopedic surgery
Better results are achieved with sub-millimeter accuracy in joint replacements thanks to systems like MAKO and NAVIO (Thompson et al., 2021).
Neurosurgery
Neurosurgery is a specialized branch of medicine focused on the diagnosis, surgical treatment, and rehabilitation of disorders affecting the nervous system, including the brain, spinal cord, peripheral nerves, and cerebrovascular system.
AI in surgery: Revolutionizing precision and outcomes
By improving preoperative planning, intraoperative support, and postoperative care, artificial intelligence has drastically changed the surgical profession, claim Anderson and Wang (2020).[1] AI’s ability to evaluate large datasets allows it to create detailed 3D models from CT or MRI images, which enhances surgical planning. By recognizing anatomical traits and offering real-time decision guidance, artificial intelligence aids in surgery. AI systems track patients’ recovery after surgery and predict problems, improving outcomes by enabling timely interventions.[5]
Role of AI in surgical planning
AI-driven imaging
AI generates detailed 3D models of patient anatomy, ensuring precise surgical planning, particularly in complex procedures.[2,6]
Predictive analytics
AI analyzes patient data to assess surgical risks, helping tailor individual treatment plans and optimize outcomes.[1,7]
Intraoperative assistance
Real-Time Image Recognition: AI helps identify tissues and organs, minimizing the risk of accidental damage.[2] AI-Guided Decision Support: AI systems recommend optimal surgical techniques and adjustments, improving precision during operations (Smith et al., 2021).
Challenges of AI in surgery
Ethical Considerations: The integration of AI raises questions about accountability in cases of error, especially in life-threatening situations.[5]
Data Privacy and Security: AI relies on vast patient data, raising concerns about data protection and breaches.[2]
Integration with Healthcare Systems: Many hospitals face challenges in adopting AI tools due to compatibility and infrastructure issues.[1,8]
Robotics in rehabilitation: Revolutionizing patient recovery
By providing patients recuperating from neurological conditions, spinal cord injuries,[9] and limb limitations with cutting-edge solutions, robotics is revolutionizing rehabilitation.[10,11] Exoskeletons and robotic arms are examples of robotic devices that offer precise, adaptable support for physical therapy, enhancing patient movement and encouraging neuroplasticity (Thompson et al., 2022).[12,13,14]
Types of robotic rehabilitation devices
Exoskeletons
Wearable robotic devices, such as ReWalk and Ekso Bionics, give patients who have had strokes or spinal cord injuries more movement and less muscle atrophy when they walk.[15]
Robotic arms
Upper-limb rehabilitation is supported by devices such as ARMin and MyoPro, which assist patients in regaining strength and mobility.[4] Advantages and drawbacks
Benefits and limitations
According to Thompson et al. (2022), robotic rehabilitation equipment facilitates at-home therapy and real-time progress tracking, which in turn promotes patient independence.
Accessibility is hampered by the high price of devices and their scarcity in developing nations. Some patients may need intensive training to utilize the devices efficiently, and customizing them for each patient can be expensive and time-consuming.[10]
AI in rehabilitation: Personalizing treatment plans and expanding access
AI is essential to improving patient outcomes, facilitating remote care, and customizing rehabilitation programs.[3] According to Williams et al.,[5] artificial intelligence (AI) systems generate customized treatment plans by evaluating data from wearables and sensors. This guarantees that patients receive the best possible care based on their development.
Challenges in AI-driven rehabilitation
For accurate evaluations and individualized treatment regimens, it is crucial to guarantee accurate data gathering from wearable sensors (Smith et al., 2021).
Patient compliance
AI can enhance patient engagement through gamification and progress tracking, but maintaining motivation is still a significant challenge.[2]
Integration with traditional care
AI cannot take the role of human therapists; instead, a hybrid strategy combining AI-powered data analysis with the individualized attention and emotional support of therapists is required.[3]
Future paths and research deficits
Significant progress is being made in the integration of robotics and AI in surgery and rehabilitation; nevertheless, issues with cost, training, data protection, and ethical considerations still exist. The challenges of integrating new technologies into current healthcare systems, removing these obstacles, and guaranteeing that all patients may use them should be the main goals of future research (Thompson et al., 2022). According to Williams et al.,[5] these technologies have the potential to revolutionize patient care by establishing new benchmarks for accuracy, customization, and accessibility as they develop further.
CONCLUSION
The integration of robotics and AI is transforming surgery and rehabilitation, reshaping the future of healthcare. Smith et al.[15,16] In surgery, robotic systems offer unparalleled precision, enabling minimally invasive procedures with faster recovery and reduced risks.[17] AI enhances decision-making, supports real-time monitoring, and improves surgical accuracy. Williams et al.[5] In rehabilitation, AI-powered devices and robotic technologies provide personalized, adaptive therapies, empowering patients to regain mobility and independence more effectively.[18] These advancements not only improve patient outcomes but also increase the efficiency and accessibility of medical care, marking a new era in healthcare innovation.[19,20]
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
REFERENCES
- 1.Anderson P, Wang Q. AI-driven decision support systems in surgery: Transforming healthcare. J Surg Innov. 2020;35:215–25. [Google Scholar]
- 2.Brown J, Smith A, Davis M. Real-time AI applications in surgery: Enhancing outcomes through data analysis. Med Robot J. 2022;18:120–34. [Google Scholar]
- 3.Jones H, Lee K. Integrating AI and robotics in healthcare: Challenges and opportunities. J Health Care Technol. 2023;22:45–58. [Google Scholar]
- 4.Smith E, Thompson R, Wilson D. Robotic exoskeletons in physical therapy: A new era for rehabilitation. J Phys Ther Inno. 2022;14:98–112. [Google Scholar]
- 5.Walker G. Autonomous robotic systems in surgical procedures: A review. Surg Robot Rev. 2022;10:58–67. [Google Scholar]
- 6.Dey N, Lim HW, Chong LS, Bundele A, Mukhopadhyay A, Kayambu G. AI in rehabilitation: Machine learning applications. Biomed Signal Process Control. 2019;50:377–84. [Google Scholar]
- 7.García-Redondo L, Martínez-Álvarez A, Gálvez-Pérez C, Rodríguez-Sanz D, Rodríguez-Fernández J, Díaz-Manzanares M. Exoskeletons for gait rehabilitation in spinal cord injury: Systematic review. J Neuroeng Rehabilitat. 2019;16:15. [Google Scholar]
- 8.Cikajlo I, Čižman-Štaba U, Vrhovac S, Larkin F, Roddy M. Robotic rehabilitation: Bridging technology and patient needs. International Journal of Rehabilitation Research. 2016;39:293–300. [Google Scholar]
- 9.Friedman B, Lechterman TM. Accountability in AI assisted surgery. Ethics Med Today. 2019;20:56–67. [Google Scholar]
- 10.Robotics in Rehabilitation. Robotic Rehabilitation Devices: Current Applications and Future Trends, 7th ed. 2022 [Google Scholar]
- 11.Clarke L, Fung LK. Training healthcare professionals for robotic surgery: Challenges and opportunities. Journal of Surgical Education. 2023;78:567–75. [Google Scholar]
- 12.Hara Y, Lo K, Stephenson M, Lockwood C, Y H. Cost effectiveness of robotic therapy in stroke rehabilitation. Stroke Rehabilitation Insights. 2016;7:33–45. [Google Scholar]
- 13.He H, Wang H, Jia S, Li Z, Duan Y, Tao G. AI in rehabilitation: A comprehensive review. Artificial Intelligence in Medicine. 2020;107:101920. [Google Scholar]
- 14.Herr H, Trigili E, Hirche S. Wearable robotics for rehabilitation and mobility enhancement. Robot Research Advances. 2015;12:198–214. [Google Scholar]
- 15.Williams J, Brown P, Davis K. AI in postoperative care: Optimizing recovery. J Med AI. 2023;11:144–59. [Google Scholar]
- 16.Brown T, Wilson P. Precision in orthopedic surgery: AI-driven implant selection and patient-specific solutions. J Orthop Innov. 2021;34:112–5. [Google Scholar]
- 17.Jones H, Lee K. Integrating AI and robotics in healthcare: Challenges and opportunities. J Healthc Technol. 2023;22:45–58. [Google Scholar]
- 18.Calabrò RS, De Cola MC, Leo A, Reitano S, Balletta T, Trombetta G, et al. Robotic neurorehabilitation in patients with chronic stroke: Psychological well-being beyond motor improvement. Int J Rehabil Res. 2015;38:219–25. doi: 10.1097/MRR.0000000000000114. [DOI] [PubMed] [Google Scholar]
- 19.Chen F, Cao Z, Grais EM, Zhao F. Contributions and limitations of using machine learning to predict noise-induced hearing loss. Int Arch Occup Environ Health. 2021;94:1097–111. doi: 10.1007/s00420-020-01648-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Cheng K, Wright J. Ethical considerations in robotic surgery. J Med Ethics. 2020;46:255–61. [Google Scholar]