Annals of medicine and surgery (2012) · 2025
Effectiveness and reliability of AI in diagnosis and robot-assisted spinal and cranial surgery: efficient outcomes and ethical worries.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Shahid Iqra, Raza Ahmed, Qureshi Inshrah, Cheema Umaima, Ul Muneer Siraj, Sehar Ayesha, Aqeel Maryam, Azeem Saleha, Hussain Ahmad, Wei Calvin R, Mbabazi Samuel, Kibukila Fabrice, Kakusu Dieudonné, Akilimali Aymar
Аннотация
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) have significantly advanced medical diagnostics and surgical procedures, particularly in spinal and cranial surgery. Robotic-assisted surgery has emerged as a transformative approach, offering increased precision, reduced intraoperative complications, and improved surgical outcomes. This review examines the effectiveness and reliability of AI in spinal and cranial diagnosis, its integration into robotic-assisted surgical interventions, and the associated ethical concerns.
METHOD: An extensive literature search was search was conducted on different search engines such as PubMed, Google Scholar, and Scopus to find relevant articles.
RESULT: Findings suggest that AI models exhibit high accuracy in detecting spinal and cranial pathologies. ML algorithms contribute to enhanced prognostic assessments and decision-making in neurosurgery. Robotic-assisted surgeries have superior accuracy, lower radiation exposure, and fewer postoperative complications compared to conventional methods. However, challenges such as data biases, lack of transparency in AI decision-making, regulatory hurdles, and the high costs of AI-driven interventions pose significant barriers to widespread adoption. Ethical concerns, including patient privacy, algorithmic bias, and the potential overreliance on AI, must be addressed to ensure responsible integration into clinical practice.
CONCLUSION: Use of AI and machine learning improves the diagnostic outcomes and decreases post op complications in the field of spinal and cranial surgery. But certain challenges such as ethical concerns and technical hurdles should be sorted out with effective planning. Further research is necessary to refine AI-driven interventions, enhance cost-effectiveness, and to make sure ethical and equitable implementation of AI and robotic surgery in neurosurgical care.
METHOD: An extensive literature search was search was conducted on different search engines such as PubMed, Google Scholar, and Scopus to find relevant articles.
RESULT: Findings suggest that AI models exhibit high accuracy in detecting spinal and cranial pathologies. ML algorithms contribute to enhanced prognostic assessments and decision-making in neurosurgery. Robotic-assisted surgeries have superior accuracy, lower radiation exposure, and fewer postoperative complications compared to conventional methods. However, challenges such as data biases, lack of transparency in AI decision-making, regulatory hurdles, and the high costs of AI-driven interventions pose significant barriers to widespread adoption. Ethical concerns, including patient privacy, algorithmic bias, and the potential overreliance on AI, must be addressed to ensure responsible integration into clinical practice.
CONCLUSION: Use of AI and machine learning improves the diagnostic outcomes and decreases post op complications in the field of spinal and cranial surgery. But certain challenges such as ethical concerns and technical hurdles should be sorted out with effective planning. Further research is necessary to refine AI-driven interventions, enhance cost-effectiveness, and to make sure ethical and equitable implementation of AI and robotic surgery in neurosurgical care.