Using Artificial Intelligence to Answer Common Patient-Focused Questions in Minimally Invasive Spine Surgery

Temporal Trends of Improvement After Minimally Invasive Transforaminal Lumbar Interbody Fusion

STUDY DESIGN

Retrospective review of prospectively collected data.

OBJECTIVE

To analyze temporal trends in improvement after minimally invasive transforaminal lumbar interbody fusion (MIS TLIF).

SUMMARY OF BACKGROUND DATA

Although several studies have shown that patients improve significantly after MIS TLIF, evidence regarding the temporal trends in improvement is still largely lacking.

METHODS

Patients who underwent primary single-level MIS TLIF for degenerative conditions of the lumbar spine and had a minimum of 2-year follow-up were included. Outcome measures were: 1) patient reported outcome measures (PROMs) (Oswestry Disability Index, ODI; Visual Analog Scale, VAS back and leg; 12-Item Short Form Survey Physical Component Score, SF-12 PCS); 2) global rating change (GRC); 3) minimal clinically important difference (MCID); and 4) return to activities. Timepoints analyzed were preoperative, 2 weeks, 6 weeks, 3 months, 6 months, 1 year, and 2 years. Trends across these timepoints were plotted on graphs.

RESULTS

236 patients were included. VAS back and VAS leg were found to have statistically significant improvement compared to the previous timepoint up to 3 months after surgery. ODI and SF-12 PCS were found to have statistically significant improvement compared to the previous timepoint up to 6 months after surgery. Beyond these timepoints, there was no significant improvement in PROMs. 80% of patients reported feeling better compared to preoperative by 3 months. >50% of patients achieved MCID in all PROMs by 3 months. Most patients returned to driving, returned to work, and discontinued narcotics at an average of 21, 20, and 10 days, respectively.

CONCLUSIONS

Patients are expected to improve up to 6 months after MIS TLIF. Back pain and leg pain improve up to 3 months and disability and physical function improve up to 6 months. Beyond these timepoints, the trends in improvement tend to reach a plateau. 80% of patients feel better compared to preoperative by 3 months after surgery.

ChatGPT as a Source of Patient Information for Lumbar Spinal Fusion and Laminectomy: A Comparative Analysis Against Google Web Search

STUDY DESIGN

Retrospective Observational Study.

OBJECTIVE

The objective of this study was to assess the utility of ChatGPT, an artificial intelligence chatbot, in providing patient information for lumbar spinal fusion and lumbar laminectomy in comparison with the Google search engine.

SUMMARY OF BACKGROUND DATA

ChatGPT, an artificial intelligence chatbot with seemingly unlimited functionality, may present an alternative to a Google web search for patients seeking information about medical questions. With widespread misinformation and suboptimal quality of online health information, it is imperative to assess ChatGPT as a resource for this purpose.

METHODS

The first 10 frequently asked questions (FAQs) related to the search terms "lumbar spinal fusion" and "lumbar laminectomy" were extracted from Google and ChatGPT. Responses to shared questions were compared regarding length and readability, using the Flesch Reading Ease score and Flesch-Kincaid Grade Level. Numerical FAQs from Google were replicated in ChatGPT.

RESULTS

Two of 10 (20%) questions for both lumbar spinal fusion and lumbar laminectomy were asked similarly between ChatGPT and Google. Compared with Google, ChatGPT's responses were lengthier (340.0 vs. 159.3 words) and of lower readability (Flesch Reading Ease score: 34.0 vs. 58.2; Flesch-Kincaid grade level: 11.6 vs. 8.8). Subjectively, we evaluated these responses to be accurate and adequately nonspecific. Each response concluded with a recommendation to discuss further with a health care provider. Over half of the numerical questions from Google produced a varying or nonnumerical response in ChatGPT.

CONCLUSIONS

FAQs and responses regarding lumbar spinal fusion and lumbar laminectomy were highly variable between Google and ChatGPT. While ChatGPT may be able to produce relatively accurate responses in select questions, its role remains as a supplement or starting point to a consultation with a physician, not as a replacement, and should be taken with caution until its functionality can be validated.

Outcomes of One Versus Two Level MIS Decompression With Adjacent Level Stenosis

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

Decompression for the treatment of lumbar spinal stenosis (LSS) has shown excellent clinical outcomes. In patients with symptomatic single level stenosis and asymptomatic adjacent level disease, it is unknown whether decompressing only the symptomatic level is sufficient. The objective of this study is to compare outcomes between single level and dual level minimally invasive (MIS) decompression in patients with adjacent level stenosis.

METHODS

The current study is a retrospective review of patients undergoing primary single or dual level MIS decompression for LSS. Radiographic stenosis severity was graded using the Schizas grading. Patients undergoing single level decompression (SLD) with moderate stenosis at the adjacent level were compared with patients undergoing dual level decompression (DLD) for multi-level LSS. Clinical outcomes, complications, and reoperations were compared. Subgroup analysis was performed on patients with the same Schizas grade at the adjacent level in the SLD group and the second surgical level in the DLD group.

RESULTS

148 patients were included (126 SLD, 76 DLD). There were no significant differences in patient reported outcomes between the two groups at any timepoint up to 2 years postoperatively, including in the matched stenosis severity subgroups. Operative time was longer in the DLD cohort (P < 0.001). There were no significant differences in complications or reoperation rates.

CONCLUSION

In patients with single level symptomatic LSS and adjacent level stenosis, decompression of only the symptomatic level provided equivalent clinical outcomes compared to dual level decompression. The additional operative time and potential incremental risk of dual level surgery may not be justified.

High accuracy but limited readability of large language model-generated responses to frequently asked questions about Kienböck's disease

BACKGROUND

This study aimed to assess the quality and readability of large language model-generated responses to frequently asked questions (FAQs) about Kienböck's disease (KD).

METHODS

Nineteen FAQs about KD were selected, and the questions were divided into three categories: general knowledge, diagnosis, and treatment. The questions were inputted into the Chat Generative Pre-trained Transformer 4 (ChatGPT4) webpage using the zero-shot prompting method, and the responses were recorded. Hand surgeons with at least 5 years of experience and advanced English proficiency were individually contacted over instant WhatsApp messaging and requested to assess the responses. The quality of each response was analyzed by 33 experienced hand surgeons using the Global Quality Scale (GQS). The readability was assessed with the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease Score (FRES).

RESULTS

The mean GQS score was 4.28 out of a maximum of 5 points. Most raters assessed the quality as good (270 of 627 responses; 43.1%) or excellent (260 of 627 responses; 41.5%). The mean FKGL was 15.5, and the mean FRES was 23.4, both of which are considered above the college graduate level. No statistically significant differences were found in the quality and readability of responses provided for questions related to general knowledge, diagnosis, and treatment.

CONCLUSIONS

ChatGPT-4 provided high-quality responses to FAQs about KD. However, the primary drawback was the poor readability of these responses. By improving the readability of ChatGPT's output, we can transform it into a valuable information resource for individuals with KD.

LEVEL OF EVIDENCE

Level IV, Observational study.

ChatGPT and Google Provide Mostly Excellent or Satisfactory Responses to the Most Frequently Asked Patient Questions Related to Rotator Cuff Repair

Purpose

To assess the differences in frequently asked questions (FAQs) and responses related to rotator cuff surgery between Google and ChatGPT.

Methods

Both Google and ChatGPT (version 3.5) were queried for the top 10 FAQs using the search term "rotator cuff repair." Questions were categorized according to Rothwell's classification. In addition to questions and answers for each website, the source that the answer was pulled from was noted and assigned a category (academic, medical practice, etc). Responses were also graded as "excellent response not requiring clarification" (1), "satisfactory requiring minimal clarification" (2), "satisfactory requiring moderate clarification" (3), or "unsatisfactory requiring substantial clarification" (4).

Results

Overall, 30% of questions were similar between what Google and ChatGPT deemed to be the most FAQs. For questions from Google web search, most answers came from medical practices (40%). For ChatGPT, most answers were provided by academic sources (90%). For numerical questions, ChatGPT and Google provided similar responses for 30% of questions. For most of the questions, both Google and ChatGPT responses were either "excellent" or "satisfactory requiring minimal clarification." Google had 1 response rated as satisfactory requiring moderate clarification, whereas ChatGPT had 2 responses rated as unsatisfactory.

Conclusions

Both Google and ChatGPT offer mostly excellent or satisfactory responses to the most FAQs regarding rotator cuff repair. However, ChatGPT may provide inaccurate or even fabricated answers and associated citations.

Clinical Relevance

In general, the quality of online medical content is low. As artificial intelligence develops and becomes more widely used, it is important to assess the quality of the information patients are receiving from this technology.

Practical Answers to Frequently Asked Questions in Anterior Cervical Spine Surgery for Degenerative Conditions

INTRODUCTION

Surgical counseling enables shared decision making and optimal outcomes by improving patients' understanding about their pathologies, surgical options, and expected outcomes. Here, we aimed to provide practical answers to frequently asked questions (FAQs) from patients undergoing an anterior cervical diskectomy and fusion (ACDF) or cervical disk replacement (CDR) for the treatment of degenerative conditions.

METHODS

Patients who underwent primary one-level or two-level ACDF or CDR for the treatment of degenerative conditions with a minimum of 1-year follow-up were included. Data were used to answer 10 FAQs that were generated from author's experience of commonly asked questions in clinic before ACDF or CDR.

RESULTS

A total of 395 patients (181 ACDF, 214 CDR) were included. (1, 2, and 3) Will my neck/arm pain and physical function improve? Patients report notable improvement in all patient-reported outcome measures. (4) Is there a chance I will get worse? 13% (ACDF) and 5% (CDR) reported worsening. (5) Will I receive a significant amount of radiation? Patients on average received a 3.7 (ACDF) and 5.5 mGy (CDR) dose during. (6) How long will I stay in the hospital? Most patients get discharged on postoperative day one. (7) What is the likelihood that I will have a complication? 13% (8% minor and 5% major) experienced in-hospital complications (ACDF) and 5% (all minor) did (CDR). (8) Will I need another surgery? 2.2% (ACDF) and 2.3% (CDR) of patients required a revision surgery. (9 & 10) When will I be able to return to work/driving? Most patients return to working (median of 16 [ACDF] and 14 days [CDR]) and driving (median of 16 [ACDF] and 12 days [CDR]).

CONCLUSIONS

The answers to the FAQs can assist surgeons in evidence-based patient counseling.

Ethical Incorporation of Artificial Intelligence into Neurosurgery: A Generative Pretrained Transformer Chatbot-Based, Human-Modified Approach

INTRODUCTION

Artificial intelligence (AI) has become increasingly used in neurosurgery. Generative pretrained transformers (GPTs) have been of particular interest. However, ethical concerns regarding the incorporation of AI into the field remain underexplored. We delineate key ethical considerations using a novel GPT-based, human-modified approach, synthesize the most common considerations, and present an ethical framework for the involvement of AI in neurosurgery.

METHODS

GPT-4, ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can artificial intelligence be ethically incorporated into neurosurgery?". Then, a layered GPT-based thematic analysis was performed. The authors synthesized the results into considerations for the ethical incorporation of AI into neurosurgery. Separate Pareto analyses with 20% threshold and 10% threshold were conducted to determine salient themes. The authors refined these salient themes.

RESULTS

Twelve key ethical considerations focusing on stakeholders, clinical implementation, and governance were identified. Refinement of the Pareto analysis of the top 20% most salient themes in the aggregated GPT outputs yielded 10 key considerations. Additionally, from the top 10% most salient themes, 5 considerations were retrieved. An ethical framework for the use of AI in neurosurgery was developed.

CONCLUSIONS

It is critical to address the ethical considerations associated with the use of AI in neurosurgery. The framework described in this manuscript may facilitate the integration of AI into neurosurgery, benefitting both patients and neurosurgeons alike. We urge neurosurgeons to use AI only for validated purposes and caution against automatic adoption of its outputs without neurosurgeon interpretation.

Chat Generative Pretraining Transformer Answers Patient-focused Questions in Cervical Spine Surgery

STUDY DESIGN

Review of Chat Generative Pretraining Transformer (ChatGPT) outputs to select patient-focused questions.

OBJECTIVE

We aimed to examine the quality of ChatGPT responses to cervical spine questions.

BACKGROUND

Artificial intelligence and its utilization to improve patient experience across medicine is seeing remarkable growth. One such usage is patient education. For the first time on a large scale, patients can ask targeted questions and receive similarly targeted answers. Although patients may use these resources to assist in decision-making, there still exists little data regarding their accuracy, especially within orthopedic surgery and more specifically spine surgery.

METHODS

We compiled 9 frequently asked questions cervical spine surgeons receive in the clinic to test ChatGPT's version 3.5 ability to answer a nuanced topic. Responses were reviewed by 2 independent reviewers on a Likert Scale for the accuracy of information presented (0-5 points), appropriateness in giving a specific answer (0-3 points), and readability for a layperson (0-2 points). Readability was assessed through the Flesh-Kincaid grade level analysis for the original prompt and for a second prompt asking for rephrasing at the sixth-grade reading level.

RESULTS

On average, ChatGPT's responses scored a 7.1/10. Accuracy was rated on average a 4.1/5. Appropriateness was 1.8/3. Readability was a 1.2/2. Readability was determined to be at the 13.5 grade level originally and at the 11.2 grade level after prompting.

CONCLUSIONS

ChatGPT has the capacity to be a powerful means for patients to gain important and specific information regarding their pathologies and surgical options. These responses are limited in their accuracy, and we, in addition, noted readability is not optimal for the average patient. Despite these limitations in ChatGPT's capability to answer these nuanced questions, the technology is impressive, and surgeons should be aware patients will likely increasingly rely on it.

A review of ophthalmology education in the era of generative artificial intelligence

PURPOSE

To explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions.

DESIGN

A literature review and analysis of current AI applications and educational programs in ophthalmology.

METHODS

Analysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies.

RESULTS

Generative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students' education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology.

CONCLUSIONS

Generative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.

Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Quality of ChatGPT Responses to Frequently Asked Questions in Carpal Tunnel Release Surgery

Background

Although demonstrating remarkable promise in other fields, the impact of artificial intelligence (including ChatGPT in hand surgery and medical practice) remains largely undetermined. In this study, we asked ChatGPT frequently asked patient-focused questions surgeons may receive in clinic from patients who have carpel tunnel syndrome (CTS) and evaluated the quality of its output.

Methods

Using ChatGPT, we asked 10 frequently asked questions that hand surgeons may receive in the clinic before carpel tunnel release (CTR) surgery. Included questions were generated from the authors' own experiences regarding conservative and operative treatment of CTS.

Results

Responses from the following 10 questions were included: (1) What is CTS and what are its signs and symptoms? (2) What are the nonsurgical options for CTS? (3) Should I get surgery for CTS? (4) What is a CTR and how is it preformed? (5) What are the differences between open and endoscopic CTR? (6) What are the risks associated with CTR and how frequently do they occur? (7) Does CTR cure CTS? (8) How much improvement in my symptoms can I expect after CTR? (9) How long is the recovery after CTR? (10) Can CTS recur after surgery?

Conclusions

Overall, the chatbot provided accurate and comprehensive information in response to most common and nuanced questions regarding CTS and CTR surgery, all in a way that would be easily understood by many patients. Importantly, the chatbot did not provide patient-specific advice and consistently advocated for consultation with a healthcare provider.

Recovery Kinetics After Cervical Spine Surgery

STUDY DESIGN

Retrospective review of a prospectively maintained multisurgeon registry.

OBJECTIVE

To study recovery kinetics and associated factors after cervical spine surgery.

SUMMARY OF BACKGROUND DATA

Few studies have described return to activities cervical spine surgery. This is a big gap in the literature, as preoperative counseling and expectations before surgery are important.

MATERIALS AND METHODS

Patients who underwent either anterior cervical discectomy and fusion (ACDF) or cervical disk replacement (CDR) were included. Data collected included preoperative patient-reported outcome measures, return to driving, return to working, and discontinuation of opioids data. A multivariable regression was conducted to identify the factors associated with return to driving by 15 days, return to working by 15 days, and discontinuing opioids by 30 days.

RESULTS

Seventy ACDF patients and 70 CDR patients were included. Overall, 98.2% of ACDF patients and 98% of CDR patients returned to driving in 16 and 12 days, respectively; 85.7% of ACDF patients and 90.9% of CDR patients returned to work in 16 and 14 days; and 98.3% of ACDF patients and 98.3% of CDR patients discontinued opioids in a median of seven and six days. Though not significant, minimal (odds ratio (OR)=1.65) and moderate (OR=1.79) disability was associated with greater odds of returning to driving by 15 days. Sedentary work (OR=0.8) and preoperative narcotics (OR=0.86) were associated with decreased odds of returning to driving by 15 days. Medium (OR=0.81) and heavy (OR=0.78) intensity occupations were associated with decreased odds of returning to work by 15 days. High school education (OR=0.75), sedentary work (OR=0.79), and retired/not working (OR=0.69) were all associated with decreased odds of discontinuing opioids by 30 days.

CONCLUSIONS

Recovery kinetics for ACDF and CDR are comparable. Most patients return to all activities after ACDF and CDR within 16 days. These findings serve as an important compass for preoperative counseling.

Quality of Spine Surgery Information on Social Media: A DISCERN Analysis of TikTok Videos

OBJECTIVE

The use of social media applications to disseminate information has substantially risen in recent decades. Spine and back pain-related hashtags have garnered several billion views on TikTok. As such, these videos, which share experiences, offer entertainment, and educate users about spinal surgery, have become increasingly influential. Herein, we assess the quality of spine surgery content TikTok from providers and patients.

METHODS

Fifty hashtags encompassing spine surgery ("#spinalfusion," "#scoliosissurgery," and "#spinaldecompression") were searched using TikTok's algorithm and included. Two independent reviewers rated the quality of each video via the DISCERN questionnaire. Video metadata (likes, shares, comments, views, length) were all collected; type of content creator (musculoskeletal, layperson) and content category (educational, patient experience, entertainment) were determined.

RESULTS

The overall DISCERN score was, on average, 24.4. #Spinalfusion videos demonstrated greater engagement, higher average likes (p = 0.02), and more comments (p < 0.001) compared to #spinaldecompression and #scoliosissurgery. #Spinaldecompression had the highest DISCERN score (p < 0.001), likely explained by the higher percentage of videos that were educational (p < 0.001) and created by musculoskeletal (MSK) professionals (p < 0.001). Compared to laypersons, MSK professionals had significantly higher quality videos (p < 0.001). Similarly, the educational category demonstrated higher quality videos (p < 0.001). Video interaction trended lower with MSK videos and educational videos had the lowest interaction of the content categories (likes: p = 0.023, comments: p = 0.005).

CONCLUSION

The quality of spine surgery videos on TikTok is low. As the influence of the new social media landscape governs how the average person consumes information, MSK providers should participate in disseminating high-quality content.