What is the role of artificial intelligence (AI) in pediatric surgery?

Artificial Intelligence in Pediatric Surgery

Artificial intelligence (AI) is transforming pediatric surgery through applications in preoperative planning, intraoperative decision support, and postoperative care, with significant potential to improve patient outcomes through enhanced surgical precision and personalized care strategies. 1, 2

Current Applications of AI in Pediatric Surgery

Diagnostic and Predictive Models

• AI models in pediatric surgery are primarily focused on predictive applications (50%), including prediction of adverse events (25%), surgical outcomes (16%), and survival (9%), followed by diagnostic models (29%) and decision support systems (21%) 3
• Neural networks (44%) and ensemble learning algorithms (36%) are the most commonly used AI methods across pediatric surgical applications 3
• The main pediatric surgical subspecialties utilizing AI include general surgery (31%) and neurosurgery (25%) 3

Preoperative Applications

• AI enhances preoperative planning through advanced image analysis and segmentation, allowing for more precise surgical planning 1, 4
• In pediatric central nervous system cancers, deep learning techniques are being investigated to predict molecular subtypes from imaging scans, potentially improving classification and treatment planning 5
• Machine learning algorithms can predict surgical risk and optimize patient selection for specific procedures, improving informed consent processes 5

Intraoperative Applications

• AI provides real-time decision support during surgery, enhancing surgical precision and safety 2
• Computer vision applications assist with surgical navigation and identification of critical structures 1
• AI-based monitoring systems can predict intraoperative complications such as hypotension, arrhythmias, and hypoxemia minutes before they occur 5
• Reinforcement learning algorithms have been developed to manage complex control rules for continuous anesthetic dosing 5

Postoperative Applications

• AI systems can predict postoperative complications and monitor patient recovery 1, 4
• Natural language processing assists in expediting clinical documentation, identifying clinical indications, and facilitating quality improvement initiatives 1

Challenges and Limitations

Validation and Bias Concerns

• Only 6% of AI models in pediatric surgery are both interpretable and externally validated, with 40% having a high risk of bias 3
• A major challenge to current AI-based monitoring systems is the lack of rigorous prospective evaluation 5
• Few studies have demonstrated effects on clinical endpoints such as mortality or made predictions that directly inform clinical decision-making 5

Implementation Barriers

• Lack of formal AI training among pediatric surgeons is a significant barrier to adoption - in a recent study, none of the surveyed pediatric surgeons had formal AI training 6
• Data privacy, regulatory considerations, and the need for interdisciplinary collaboration present ongoing challenges 1
• AI tools may be limited in practice by the lack of standardized platforms to report predictions to clinicians and noise in collected data 5

Future Directions

Education and Training

• Targeted AI workshops have been shown to significantly increase pediatric surgeons' understanding of AI/machine learning and their readiness to adopt these technologies 6
• 83% of pediatric surgeons express interest in further AI training, with 91% believing AI will become more integrated into clinical practice 6
• Over 80% of pediatric surgeons anticipate that AI will improve patient outcomes 6

Research Priorities

• Future research needs to focus on developing AI models that are prospectively validated and ultimately integrated into clinical workflows 3
• Standardized reporting guidelines such as CONSORT-AI and SPIRIT-AI are essential for clinical trials involving AI interventions 5
• Identification of patients and disease types most amenable to AI-enabled monitoring is an important unaddressed theme 5

Emerging Technologies

• Foundation model architectures, wearable technologies, and improving surgical data infrastructures are enabling rapid advances in AI interventions 4
• Multimodal foundation models represent a promising direction for future advances in surgical AI 4
• Integration of AI with other technologies like robotics and augmented reality holds potential for further enhancing surgical precision 1, 2

Best Practices for Implementation

• AI algorithms should be tested in independent, external cohorts to ensure generalizability across different populations, equipment, and clinical workflows 5
• Human factors and usability evaluation should be integral parts of AI system development and implementation 5
• AI systems should be designed to reduce disparities of care rather than exacerbate them 5
• Transparent reporting of AI studies, including implementation environment, user characteristics, training provided, and algorithm identification is essential 5

AI has tremendous potential to transform pediatric surgery, but careful attention to validation, bias mitigation, and clinical integration is necessary to realize these benefits and improve patient outcomes.