Posted: April 30th, 2022

How Artificial Intelligence Can Improve Surgical Outcomes

How Artificial Intelligence Can Improve Surgical Outcomes

Artificial intelligence (AI) is the development of algorithms that give machines the ability to act with human-like rationality in complex tasks, such as problem-solving and decision-making . AI has the potential to reshape medicine and surgery by offering tools to examine high-volume data and inform predictive analytics that support complex decision-making processes . This is especially relevant for trauma and emergency contexts, where time constraints and high cognitive loads from high volumes of information demand reliance on cognitive shortcuts, leading to mistakes and preventable patient harm .

AI can also enhance surgical education and training by creating better training videos, 3D model reconstructions, or even virtual environments where surgeons can practice before operating on real patients. In this blog post, we will explore some of the ways that AI can improve surgical outcomes by providing decision support, enhancing surgical skills, and reducing variations in procedures.

AI for Decision Support

One of the main challenges in surgery is to make accurate and timely decisions based on multiple sources of information, such as patient history, physical examination, laboratory tests, imaging studies, and clinical guidelines. However, human decision-making is prone to errors, biases, and inconsistencies, which can affect patient outcomes . AI can help overcome these limitations by providing data-driven insights and recommendations that can assist surgeons in diagnosis, treatment planning, risk assessment, and outcome prediction.

For example, researchers from the University of Florida developed an AI platform that can predict surgical complications based on patient characteristics, comorbidities, surgical procedures, and postoperative events . Using nearly seven years of data from more than 74,000 procedures involving approximately 58,000 adult patients, the system was consistently able to match surgeons’ accuracy in predicting surgical outcomes, the researchers found. The system can also provide explanations for its predictions and suggest interventions to reduce the risk of complications.

Another example is the use of AI for computer vision, which is the ability of machines to process and understand visual information. Computer vision can help surgeons detect and segment anatomical structures, identify pathological lesions, measure distances and angles, and track surgical instruments . Computer vision can also enable augmented reality (AR) and mixed reality (MR) applications that can overlay digital information onto the real-world view of the surgeon. For instance, AR/MR can provide real-time guidance for surgical navigation, anatomical mapping, tumor localization, and blood vessel identification .

AI for Surgical Skills

Another challenge in surgery is to acquire and maintain adequate surgical skills that can ensure optimal performance and patient safety. Surgical skills depend on both technical and non-technical abilities, such as hand-eye coordination, dexterity, precision, speed, situational awareness, communication, and teamwork . AI can help improve surgical skills by providing feedback, assessment, and coaching based on objective metrics and benchmarks.

For example, researchers from Harvard Medical School developed an AI system that can analyze surgical videos and provide feedback on technical skills using natural language processing (NLP) . NLP is the ability of machines to understand and generate natural language. The system can automatically segment surgical videos into steps, identify errors and deviations from best practices,
and generate textual feedback that highlights strengths and weaknesses of the performance.

Another example is the use of AI for robotic surgery, which is the use of robots to assist surgeons in performing minimally invasive procedures. Robotic surgery can offer advantages such as improved visualization, magnification,
stabilization, dexterity, and ergonomics . However,
robotic surgery also requires specific skills that differ from conventional surgery,
such as spatial orientation,
instrument manipulation,
and human-machine interaction . AI can help enhance robotic surgery by providing adaptive control,
autonomy,
and collaboration between the surgeon
and the robot.

For instance,
researchers from Eindhoven University of Technology developed a robotic system that can perform microsurgery using AI . Microsurgery is a type of surgery that involves operating on very small structures,
such as blood vessels
and nerves,
using microscopes
and specialized instruments.
The robotic system can perform delicate tasks,
such as suturing
and anastomosis,
with high precision
and accuracy,
while adapting to tissue deformation
and motion.
The system can also cooperate with the surgeon
by sensing
and responding to verbal
and gestural commands.

AI for Reducing Variations

A third challenge in surgery is to reduce variations in procedures that can affect patient outcomes. Variations in surgery can arise from differences in patient characteristics,
surgeon preferences,
hospital protocols,
and resource availability . Some variations are inevitable
and necessary to tailor the treatment to the individual patient’s needs
and preferences.
However,
some variations are unwarranted
and undesirable,
as they can lead to inefficiencies,
inequalities,
and errors . AI can help reduce variations in surgery by providing standardization,
personalization,
and optimization of surgical procedures.

For example,
researchers from Stanford University developed an AI system that can generate personalized surgical plans for total knee arthroplasty (TKA) . TKA is a type of surgery that involves replacing the damaged knee joint with an artificial implant. The system can use preoperative imaging data to create a 3D model of the patient’s knee
and suggest the optimal implant size,
position,
and alignment for each patient.
The system can also provide visual
and quantitative feedback on the surgical plan
and compare it with historical data from previous cases.

Another example is the use of AI for surgical scheduling,
which is the process of allocating surgical resources,
such as operating rooms,
staff,
and equipment,
to perform surgical procedures . Surgical scheduling is a complex
and dynamic problem that involves multiple constraints,
objectives,
and uncertainties. AI can help improve surgical scheduling by providing predictive analytics,
simulation,
and optimization techniques that can enhance efficiency,
quality,
and satisfaction of surgical services.

For instance,
researchers from the University of Toronto developed an AI system that can optimize surgical scheduling using reinforcement learning (RL) . RL is a type of machine learning that involves learning from trial
and error
and maximizing a reward function. The system can learn from historical data
and real-time feedback to allocate operating rooms to surgical cases in a way that minimizes delays,
cancellations,
overtime,
and costs.

Conclusion

AI is a powerful technology that can improve surgical outcomes by providing decision support, enhancing surgical skills, and reducing variations in procedures. AI can also create new opportunities and challenges for surgeons, patients, and health care systems. Therefore, it is important to foster a collaborative and multidisciplinary approach to the development, evaluation, and implementation of AI applications in surgery. Moreover, it is essential to promote ethical, legal, and social implications of AI in surgery, such as transparency, accountability, privacy, and trust.

Works Cited

: Russell, Stuart J., and Peter Norvig. Artificial Intelligence: A Modern Approach. 4th ed., Pearson, 2021.

: Cobianchi, Lorenzo, et al. “Surgeons’ Perspectives on Artificial Intelligence to Support Clinical Decision-Making in Trauma and Emergency Contexts: Results from an International Survey.” World Journal of Emergency Surgery, vol. 18, no. 1, 2023, p. 1. https://doi.org/10.1186/s13017-022-00467-3

: Loftus, Tyler J., et al. “Predictive Analytics and Artificial Intelligence in Surgery—Opportunities and Risks.” JAMA Surgery, vol. 157, no. 2, 2022, pp. 113–114. https://doi.org/10.1001/jamasurg.2021.5088

: Gawande, Atul A. “The Checklist Manifesto: How to Get Things Right.” Metropolitan Books/Henry Holt & Co., 2009.

: Loftus TJ et al., “An Artificial Intelligence Platform for Predicting Surgical Complications.” Annals of Surgery, vol. 275, no. 6, 2022, pp. 1130–1137. https://doi.org/10.1097/SLA.0000000000004969

: Maier-Hein L et al., “Surgical Data Science for Next-Generation Interventions.” Nature Biomedical Engineering, vol. 1, no. 9, 2017, pp. 691–696. https://doi.org/10.1038/s41551-017-0132-7

: Nicolau SA et al., “Augmented Reality in Laparoscopic Surgical Oncology.” Surgical Oncology, vol. 20, no. 3, 2011, pp. 189–201.
https://doi.org/10.1016/j.suronc.2011.07.002

: Aggarwal R et al., “Training and Simulation for Patient Safety.” Quality & Safety in Health Care, vol. 19 Suppl 2(Suppl_2), 2010, pp.i34–i43.
https://doi.org/10.1136/qshc.2009.038562

: Hashimoto DA et al., “Artificial Intelligence–Enabled Analysis of Video Data: A Systematic Review of the Surgical Literature.” Annals of Surgery, vol.
271
no.
5
2020
pp.
837–844.
https://doi.org

Check Price Discount

Study Notes & Homework Samples: NURS1025 Person-centred Care Across the Lifespan »SOAP Note Nursing Assignment Examples | Case Study Homework Help