AI Has Potential to Transform Global Surgical Systems

In recent years, advances in artificial intelligence (AI) have augmented the delivery of healthcare services around the world. The integration of AI into health systems, however, has been concentrated in high-income countries (HICs) due to the relative ease of implementation with abundant resources and established infrastructure. In rural areas and low- and middle-income countries (LMICs), AI technologies are challenging to deploy due to limited resources, though they may hold significant promise for improving healthcare delivery and patient outcomes in such settings.

Different types of AI technologies include machine learning algorithms, predictive analytics, artificial neural networks, cloud-based language and signal processing, data mining, and virtual simulation. Each has the potential to provide innovative solutions for issues related to surgical care delivery in remote regions and LMICs, including education and training, collaboration and care delivery, and health policy and planning.¹

Given the staggering burden of global surgical disease, AI may offer novel, multifaceted approaches to surgical systems strengthening in resource-limited settings, although significant limitations do exist. While the intricacies of these innovations are beyond the scope of this article, the principles and applications of these tools are evaluated here within the context of healthcare delivery and extrapolated to surgical systems.

A surgical system is the concept of an integrated ecosystem dedicated to the provision of surgical care, and includes workforce (i.e., surgeons, obstetricians, anesthetists, nurses, and community health workers); infrastructure (i.e., facilities, electricity, water, laboratory capability, blood supply, sterilization capacity, referral, and prehospital systems); service delivery and quality improvement processes; health financing and budget allocation; and information and data management.

Applications of AI in Surgical Systems

Education and Training

Within the surgical system, human resources are the core tenet of the provision of surgical care. Rural regions and LMICs are disproportionately plagued, however, by the maldistribution of the specialist surgical workforce (e.g., the relative density of surgeons, obstetricians, and anesthetists per 100,000 population). In addition, the majority of medical schools and training programs worldwide are clustered in densely populated areas, rather than the rural regions where disease burden and unmet needs for surgical care are relatively higher.^2,3 Achieving the necessary expansion of international surgical systems relies heavily on preparing current and future trainees to fill the workforce void as they progress to practicing independent providers. Advances in AI have the potential to revolutionize surgical training to help meet this critical need.

Through the use of immersive and personalized learning experiences, AI can enhance surgical education, training, and performance improvement. AI-powered simulation platforms allow surgical trainees to engage in hands-on experiences in a safe environment without the need for cadavers or live patients, allowing them to practice various procedures repeatedly and refine skills.

With personalized learning and the creation of virtual mentoring, AI can assess a trainee’s technical strengths and weaknesses, and provide targeted feedback and guidance to improve specific skills. These virtual mentors can draw from vast repositories of surgical data and best practices, offering insights and advice based on real-world cases and expert knowledge, which is particularly beneficial in regions of the world where specialist training may be limited. By leveraging machine learning algorithms, virtual mentors can adapt their teaching approaches to suit the learning styles and progress of each trainee, maximizing the effectiveness of the training process.⁴

AI-driven simulation and augmented reality systems have the added ability to function remotely, which is especially advantageous in overcoming the geographical barriers to access to specialist education and training.⁴ Additionally, remote functionality obviates the need for in-person educators, which offloads the burden for those few practicing surgeons in a metaphorical “surgeon desert” that lack the time and bandwidth (or possibly, experience, as in the case of sparse laparoscopic expertise in many LMICs) to train their successors.⁵ Together, such AI applications have the potential to enhance the effectiveness of, increase access to, and reduce the cost of training programs and overall accelerate the development of competent surgeons to increase workforce density.

Collaboration and Care Delivery

Modeling estimates have shown a shortage of one million specialist surgical, anesthetic, and obstetric providers in 136 LMICs.² Concentrated in the world’s poorest regions, this burden underscores the need for access to specialist expertise to improve outcomes for patients affected by surgical conditions. Additionally, the low density of crucial collaborators (including radiologists, pathologists, and others not typically associated with surgical care), as well as associated diagnostic equipment such as computed tomography or nuclear medicine, are deficiencies often overlooked that greatly contribute to disparities in service delivery and access to timely care.⁵ AI-driven tools that facilitate this type of access have already been deemed transformative in supporting virtual collaboration to improve international surgical care.^6,7

Through AI-powered telemedicine platforms, surgeons in underserved regions can connect with other specialist surgeons from around the world to seek real-time guidance on complex cases. In the absence of direct expert consultation, data mining and augmented reality technologies can provide offline access to vast repositories of surgical scenarios and diagnostic datasets that can supplement decision-making. Through artificial neural networks, AI image and signal processing algorithms also can assist in image analysis and data classification, which allows for pattern recognition and the rapid interpretation of medical imaging in areas with lower densities of radiologists.

 Although human intelligence and processing cannot fully be replicated or replaced, the AI-driven assistance in detection and classification of abnormalities does have the potential to drastically increase individual throughput, and thereby aid in timely diagnosis and treatment planning.^5,6 Improvements in diagnostic efficiency may translate to lives saved, specifically in fields where timely diagnosis and expedient surgical intervention are crucial. Leveraging AI for remote consultation and diagnostic support can help to address accessibility barriers and, ultimately, improve global surgical patient outcomes.

Health Policy and Planning

The overarching consideration for surgical systems strengthening is the coalescence of policy and planning to address the lack of universal access to surgical care at regional, national, and international levels. Through the collaboration and shared governance of stakeholders, the current status of surgical disease burden must be evaluated and understood in order to effectively meet gaps in care and translate practice into policy. Emerging AI technologies can be employed to conduct surveillance of population patterns such as traumatic injury data and thereby inform health policymaking.

AI offers transformative potential in addressing public health challenges requiring surgical care, such as road traffic injuries. Algorithms that rely on machine learning and data mining have the power to use digital street imagery to identify patterns in road traffic collisions and trends in helmet use prevalence. This helps policymakers develop targeted interventions to reduce the burden of injuries.

AI-powered predictive models also can forecast future trends in road traffic, which enables proactive planning for emergency response and trauma care services. With the ability to predict traumatic injury severity based on pattern recognition, AI has the potential to aid policymakers in optimizing resource allocation and streamlining referral systems. As a result, patients in rural and remote regions would have better chances of receiving equitable and timely access to surgical services and tertiary care.

Through the integration of AI into health policy and planning, systems-based efforts to mitigate the impact of injuries on the global surgical burden can be significantly enhanced.^1,6 

Challenges of AI Integration in Global Surgery

Infrastructure and Resources

The implementation of AI in surgical systems strengthening faces numerous limitations and barriers, foremost among them is inadequate infrastructure to deploy and sustain the advanced technology.

Many LMICs and rural regions lack the necessary existing digital infrastructure, including reliable electricity, internet connectivity, and computing equipment. Not only is a physical resource framework required to support the use of AI, so are trained individuals adept in the use and maintenance of this technology and equipment. The lack of standardized data collection systems and electronic health records in resource-limited settings further complicates the integration of AI into surgical systems, as these advanced technologies rely heavily on robust, quality data for machine learning and data mining.⁶

On the other hand, the lack of digitized health record systems in these settings provides an opportunity for AI-driven tools, such as natural language processing and image and signal processing, to improve care delivery. With the ability to identify, understand, and categorize information, these computer-based technologies can digitize handwritten patient charts, and process and categorize the data into databases that can be used for coordinated patient care, as well as for research and quality improvement processes.⁵

Certainly, one of the looming barriers to integrating AI in resource-limited settings is cost. There is no doubt that addressing these infrastructure gaps requires significant financial investment to ensure that surgical systems are equipped with the physical and human resources necessary to leverage AI effectively.

In order to thoroughly inform cost-effectiveness analyses, economic modeling studies should be expanded to assess the integration of AI and needed resources in regions with inadequate infrastructure. Given the current staggering expenditures resulting from a lack of access to surgical care worldwide, however, the return on investment in terms of economic and welfare gains is likely to support the potential for scalability of AI interventions in LMICs and rural regions.

Ethical Implications

The ethical implications of AI applications in global surgical systems strengthening are as important to consider as the logistical and resource challenges. While AI holds promise in enhancing access to surgical care, there are concerns regarding equity, bias, and privacy.

AI-driven technologies may exacerbate existing disparities, as they are likely to disproportionately benefit populations with greater access to resources and digital infrastructure, widening the gap between affluent and marginalized communities. It is equally important to recognize that improving access to timely diagnosis does not necessarily equate access to treatment if the necessary surgical care for the diagnosed condition is not available to the patient, which creates its own ethical dilemma.⁶ 

As AI tools such as machine learning depend on training algorithms that are context-specific, there are inherent flaws in the generalizability of technologies developed using population-based datasets in HICs. The under- or misrepresentation, then, of regional and ethnic populations not included in the learning datasets can lead to discriminatory bias and limited use.⁷

Successful implementation depends on foundational tenets of ethical global surgery collaboration, which involves the inclusion of local stakeholders in the development of such interventions. It is crucial that any implementation is driven by local needs, with a recognition of system constraints and bandwidth, to avoid both biases and failure to launch.

Additional concerns regarding data management in AI-powered technologies involve the autonomy of individuals, particularly regarding informed consent and data privacy. As machine learning and data mining platforms require open access to datasets, including personal health information, ensuring the privacy and security of this data is vital to avoiding unauthorized access or misuse.

The concepts of telemedicine, remote consultation, and cloud computing for data management raise concerns about data sharing across international borders.^5,7 Given the nascent stage of integration of AI into healthcare delivery, further research and consideration of international data-sharing protocols and standardized, robust regulatory frameworks are needed to inform the implementation of AI technologies and to ensure equitable and ethical use.

As the next frontier of global surgery emerges, AI holds immense potential to improve healthcare outcomes in resource-limited settings through surgical systems strengthening. The possibilities of AI have been demonstrated in general healthcare delivery and can be extended to surgical care delivery in LMICs and rural regions, though further modeling and research is needed to inform investment.

AI technologies may help address shortfalls in surgical care by enhancing education and training to build workforce capacity, facilitating collaboration and care delivery to expand surgical infrastructure, and by providing data to guide resource allocation and policy development. Effective implementation of these technologies, however, requires addressing cost and infrastructure barriers and adherence to ethical principles to minimize bias and protect patient privacy and autonomy.

Dr. Erin Scott is a general surgery resident in the Department of Surgery at the University of Massachusetts Chan Medical School in Worcester. She also is the Chair of the RAS-ACS Global Surgery Committee.

References

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