Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Endoscopic spine surgery (ESS) is growing in popularity worldwide. An expanding body of literature demonstrates rapid functional recovery with reduced morbidity compared to open techniques. Both full endoscopic spine surgery, or uniportal endoscopy, and unilateral biportal endoscopy (UBE) can be employed in conjunction with various navigation and enabling technologies for assistance with localization of anatomic orientation and assessment of the intraoperative target spinal pathology. This review article describes various navigation technologies in ESS, including 2-dimensional (2D) fluoroscopic imaging, 2D fluoroscopic navigation, 3-dimensional C-arm navigation, augmented reality, and spinal robotics. Employment of enabling navigation and emerging technology with the registration of patient-specific anatomy enables clear delineation of anatomic landmarks and facilitation of a successful procedure. Additionally, avoidance of common pitfalls during use of navigation systems in ESS is discussed in this review.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.