Artificial intelligence and treatment algorithms in spine surgery

Assessing the potential role of ChatGPT in spine surgery research

Purpose

Since its release in November 2022, Chat Generative Pre-Trained Transformer 3.5 (ChatGPT), a complex machine learning model, has garnered more than 100 million users worldwide. The aim of this study is to determine how well ChatGPT can generate novel systematic review ideas on topics within spine surgery.

Methods

ChatGPT was instructed to give ten novel systematic review ideas for five popular topics in spine surgery literature: microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement. A comprehensive literature search was conducted in PubMed, CINAHL, EMBASE and Cochrane. The number of nonsystematic review articles and number of systematic review papers that had been published on each ChatGPT-generated idea were recorded.

Results

Overall, ChatGPT had a 68% accuracy rate in creating novel systematic review ideas. More specifically, the accuracy rates were 80%, 80%, 40%, 70% and 70% for microdiscectomy, laminectomy, spinal fusion, kyphoplasty and disc replacement, respectively. However, there was a 32% rate of ChatGPT generating ideas for which there were 0 nonsystematic review articles published. There was a 71.4%, 50%, 22.2%, 50%, 62.5% and 51.2% success rate of generating novel systematic review ideas, for which there were also nonsystematic reviews published, for microdiscectomy, laminectomy, spinal fusion, kyphoplasty, disc replacement and overall, respectively.

Conclusions

ChatGPT generated novel systematic review ideas at an overall rate of 68%. ChatGPT can help identify knowledge gaps in spine research that warrant further investigation, when used under supervision of an experienced spine specialist. This technology can be erroneous and lacks intrinsic logic; so, it should never be used in isolation.

Level of Evidence

Not applicable.

The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review

Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.

Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review

This systematic review summarizes existing evidence and outlines the benefits of artificial intelligence-assisted spine surgery. The popularity of artificial intelligence has grown significantly, demonstrating its benefits in computer-assisted surgery and advancements in spinal treatment. This study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a set of reporting guidelines specifically designed for systematic reviews and meta-analyses. The search strategy used Medical Subject Headings (MeSH) terms, including "MeSH (Artificial intelligence)," "Spine" AND "Spinal" filters, in the last 10 years, and English- from January 1, 2013, to October 31, 2023. In total, 442 articles fulfilled the first screening criteria. A detailed analysis of those articles identified 220 that matched the criteria, of which 11 were considered appropriate for this analysis after applying the complete inclusion and exclusion criteria. In total, 11 studies met the eligibility criteria. Analysis of these studies revealed the types of artificial intelligence-assisted spine surgery. No evidence suggests the superiority of assisted spine surgery with or without artificial intelligence in terms of outcomes. In terms of feasibility, accuracy, safety, and facilitating lower patient radiation exposure compared with standard fluoroscopic guidance, artificial intelligence-assisted spine surgery produced satisfactory and superior outcomes. The incorporation of artificial intelligence with augmented and virtual reality appears promising, with the potential to enhance surgeon proficiency and overall surgical safety.

Contribution of MRI and imaging exams in the diagnosis of lumbar pseudarthrosis

INTRODUCTION

The diagnosis of pseudoarthrosis is based on imaging and clinical exam findings. The standard for pseudarthrosis diagnosis remains postoperative observation through computer tomography (CT) and patient's symptoms. This can be further augmented by dynamic X-ray imaging or nuclear positron emission tomography (PET) CT to demonstrate an absence of fusion by showing a persistence of mobility. However, there is not a uniform diagnostic approach that is a standard of care amongst spine practioners. The aim of this study is to describe the timeline and diagnostic analysis for pseudoarthrosis between the initial surgery and follow-up procedure.

METHODS

This is a single-center retrospective observational study. The aim was to enroll patients reoperated for pseudarthrosis after 1 or 2 level lumbar fusions, between August 1st, 2008 and August 1st, 2018. The exams were reviewed by one surgeon and one radiologist, defining a status either in favor of pseudarthrosis, or against it, or inconclusive, based on the radiological criteria mentioned below. We then investigated different combinations of exams and their specific chronology before a diagnosis was established.

RESULTS

Forty-four patients were included, 70.5% male and with a mean age of 47.3 years. The median time between the 2 surgeries was 23.7 months. Plain X-rays supported the diagnosis in 38.7% of cases, dynamic X-rays showed hypermobility in 50% of cases. The CT-scan demonstrated pseudarthrosis in 94,4% of cases. A MODIC 1 signal was observed in 87,2% of cases on MRI. SPECT-CT showed a tracer uptake in 70% of cases.

CONCLUSION

Reducing the time to reintervention is a key objective for improving the management and clinical outcomes of these patients. We suggest that MRI is an additional tool in combination with CT in the assessment of suspected mechanical pseudarthrosis, in order to optimize the diagnosis and shorten the time to revision surgery.

LEVEL OF EVIDENCE

IV.

Machine Learning in Spine Surgery: A Narrative Review

Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

What is the rate of text generated by artificial intelligence over a year of publication in Orthopedics & Traumatology: Surgery & Research? Analysis of 425 articles before versus after the launch of ChatGPT in November 2022

BACKGROUND

The use of artificial intelligence (AI) is soaring, and the launch of ChatGPT in November 2022 has accelerated this trend. This "chatbot" can generate complete scientific articles, with risk of plagiarism by mining existing data or downright fraud by fabricating studies with no real data at all. There are tools that detect AI in publications, but to our knowledge they have not been systematically assessed for publication in scientific journals. We therefore conducted a retrospective study on articles published in Orthopaedics & Traumatology: Surgery & Research (OTSR): firstly, to screen for AI-generated content before and after the publicized launch of ChatGPT; secondly, to assess whether AI was more often used in some countries than others to generate content; thirdly, to determine whether plagiarism rate correlated with AI-generation, and lastly, to determine whether elements other than text generation, and notably the translation procedure, could raise suspicion of AI use.

HYPOTHESIS

The rate of AI use increased after the publicized launch of ChatGPT v3.5 in November 2022.

MATERIAL AND METHODS

In all, 425 articles published between February 2022 and September 2023 (221 before and 204 after November 1, 2022) underwent ZeroGPT assessment of the level of AI generation in the final English-language version (abstract and body of the article). Two scores were obtained: probability of AI generation, in six grades from Human to AI; and percentage AI generation. Plagiarism was assessed on the Ithenticate application at submission. Articles in French were assessed in their English-language version as translated by a human translator, with comparison to automatic translation by Google Translate and DeepL.

RESULTS

AI-generated text was detected mainly in Abstracts, with a 10.1% rate of AI or considerable AI generation, compared to only 1.9% for the body of the article and 5.6% for the total body+abstract. Analysis for before and after November 2022 found an increase in AI generation in body+abstract, from 10.30±15.95% (range, 0-100%) to 15.64±19.8% (range, 0-99.93) (p < 0.04; NS for abstracts alone). AI scores differed between types of article: 14.9% for original articles and 9.8% for reviews (p<0.01). The highest rates of probable AI generation were in articles from Japan, China, South America and English-speaking countries (p<0.0001). Plagiarism rates did not increase between the two study periods, and were unrelated to AI rates. On the other hand, when articles were classified as "suspected" of AI generation (plagiarism rate ≥ 20%) or "non-suspected" (rate<20%), the "similarity" score was higher in suspect articles: 25.7±13.23% (range, 10-69%) versus 16.28±10% (range, 0-79%) (p < 0.001). In the body of the article, use of translation software was associated with higher AI rates than with a human translator: 3.5±5% for human translators, versus 18±10% and 21.9±11% respectively for Google Translate and DeepL (p < 0.001).

DISCUSSION

The present study revealed an increasing rate of AI use in articles published in OTSR. AI grades differed according to type of article and country of origin. Use of translation software increased the AI grade. In the long run, use of ChatGPT incurs a risk of plagiarism and scientific misconduct, and needs to be detected and signaled by a digital tag on any robot-generated text.

LEVEL OF EVIDENCE

III; case-control study.

Evaluation of the impact of large language learning models on articles submitted to Orthopaedics & Traumatology: Surgery & Research (OTSR): A significant increase in the use of artificial intelligence in 2023

INTRODUCTION

There has been an unprecedented rise is the use of artificial intelligence (AI) amongst medical fields. Recently, a dialogue agent called ChatGPT (Generative Pre-trained Transformer) has grown in popularity through its use of large language models (LLM) to clearly and precisely generate text on demand. However, the impact of AI on the creation of scientific articles is remains unknown. A retrospective study was carried out with the aim of answering the following questions: identify the presence of text generated by LLM before and after the increased usage of ChatGPT in articles submitted in OTSR; determine if the type of article, the year of submission, and the country of origin, influenced the proportion of text generated, at least in part by AI.

MATERIAL AND METHODS

A total of 390 English articles were submitted to OTSR in January, February and March 2022 (n=204) and over the same months of 2023 (n=186) were analyzed. All articles were analyzed using the ZeroGPT tool, which provides an assumed rate of AI use expressed as a percentage. A comparison of the average rate of AI use was carried out between the articles submitted in 2022 and 2023. This comparison was repeated keeping only the articles with the highest percentage of suspected AI use (greater than 10 and 20%). A secondary analysis was carried out to identify risk factors for AI use.

RESULTS

The average percentage of suspected LLM use in the entire cohort was 11%±6, with 160 articles (41.0%) having a suspected AI rate greater than 10% and 61 (15.6%) with an assumed AI rate greater than 20%. A comparison between articles submitted in 2022 and 2023 revealed a significant increase in the use of these tools after the launch of ChatGPT 3.5 (9.4% in 2022 and 12.6% in 2023 [p=0.004]). The number of articles with suspected AI rates of greater than 10 and 20% were significantly higher in 2023: >10%: 71 articles (34.8%) versus 89 articles (47.8%) (p=0.008) and >20%: 21 articles (10.3%) versus 40 articles (21.5%) (p=0.002). A risk factor analysis for LLLM use, demonstrated that authors of Asian geographic origin, and the submission year 2023 were associated with a higher rate of suspected AI use. An AI rate >20% was associated to Asian geographical origin with an odds ratio of 1.79 (95% CI: 1.03-3.11) (p=0.029), while the year of submission being 2023 had an odds ratio of 1.7 (95% CI: 1.1-2.5) (p=0.02).

CONCLUSION

This study highlights a significant increase in the use of LLM in the writing of articles submitted to the OTSR journal after the launch of ChatGPT 3.5. The increasing use of these models raises questions about originality and plagiarism in scientific research. AI offers creative opportunities but also raises ethical and methodological challenges.

LEVEL OF EVIDENCE

III; case control study.

Emerging Technologies within Spine Surgery

New innovations within spine surgery continue to propel the field forward. These technologies improve surgeons' understanding of their patients and allow them to optimize treatment planning both in the operating room and clinic. Additionally, changes in the implants and surgeon practice habits continue to evolve secondary to emerging biomaterials and device design. With ongoing advancements, patients can expect enhanced preoperative decision-making, improved patient outcomes, and better intraoperative execution. Additionally, these changes may decrease many of the most common complications following spine surgery in order to reduce morbidity, mortality, and the need for reoperation. This article reviews some of these technological advancements and how they are projected to impact the field. As the field continues to advance, it is vital that practitioners remain knowledgeable of these changes in order to provide the most effective treatment possible.