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Medicine constantly evolves to improve the standard of patient care.1 Among the many advancements is artificial intelligence (AI), which is the use of technology and computers to simulate intelligent behaviour and critical thinking similar to that of a human.2 AI incorporates machine learning (ML) algorithms and deep learning networks. It has the ability to collect massive amounts of data and derive complex patterns from it, enhancing patient care with medical management optimisation and error reduction analyses.2 In general, artificially intelligent systems have played a critical role in medicine by improving diagnostic algorithms (ie, image interpretations), developing efficient and individualised treatment protocols, catalysing drug development, storing electronic medical records and increasing healthcare delivery productivity. As such, AI can reduce personnel demand while cooperatively increasing efficiency and decreasing potential errors.2
Neurosurgery resides at the nexus between technology and medicine. AI and ML can be applied to neurosurgery, which frequently employs high-tech medical equipment and information systems with complex data. AI can also improve high-resolution radiological imaging to eliminate invasive diagnostic procedures, as well as identify preoperative, perioperative and postoperative complications to better quantify risk factors and improve patient aftercare.2 AI has the potential to improve diagnostic accuracy and treatment access in low-income and middle-income countries (LMICs). Despite the benefits, several barriers limit AI access in LMICs. A study on neurosurgical access by Punchak et al revealed that out of the approximately 68 LMICs, 11 countries reported no practising neurosurgeons with over 5 million individuals in LMIC not being able to receive essential neurosurgical care annually.1 2 These results demonstrate the scarcity of neurosurgical services in LMICs which form a significant portion of the global population.This editorial discusses the state of current neurosurgical practice in LMICs and the role of AI in improving care efficacy, and forecasts how …
Twitter @rohanyarla, @Peculiarmed
Contributors WAA, AM, MK and JK conceptualised the topic and coordinated the reading, writing and editing of the manuscript. JK, AM, RY, MN, MK, A-RT and PPO contributed to various aspects of reading, data collection, writing the original draft and implementing changes for critical revision. AI and VS contributed to reviewing and edits. All authors approved the final draft.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.