Artificial Intelligence-Enabled Evaluation of Pain Sketches to Predict Outcomes in Headache Surgery

Surgical Management of Headache Disorders - A Systematic Review of the Literature

PURPOSE OF REVIEW

This review article critically evaluates the latest advances in the surgical treatment of headache disorders.

RECENT FINDINGS

Studies have demonstrated the effectiveness of innovative screening tools, such as doppler ultrasound, pain drawings, magnetic resonance neurography, and nerve blocks to help identify candidates for surgery. Machine learning has emerged as a powerful tool to predict surgical outcomes. In addition, advances in surgical techniques, including minimally invasive incisions, fat injections, and novel strategies to treat injured nerves (neuromas) have demonstrated promising results. Lastly, improved patient-reported outcome measures are evolving to provide a framework for comparison of conservative and invasive treatment outcomes. Despite these developments, challenges persist, particularly related to appropriate patient selection, insurance coverage, delays in diagnosis and surgical treatment, and the absence of standardized measures to assess and compare treatment impact. Collaboration between medical/procedural and surgical specialties is required to overcome these obstacles.

Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study

The misdiagnosis of headache disorders is a serious issue, and AI-based headache model diagnoses with external validation are scarce. We previously developed an artificial intelligence (AI)-based headache diagnosis model using a database of 4000 patients' questionnaires in a headache-specializing clinic and herein performed external validation prospectively. The validation cohort of 59 headache patients was prospectively collected from August 2023 to February 2024 at our or collaborating multicenter institutions. The ground truth was specialists' diagnoses based on the initial questionnaire and at least a one-month headache diary after the initial consultation. The diagnostic performance of the AI model was evaluated. The mean age was 42.55 ± 12.74 years, and 51/59 (86.67%) of the patients were female. No missing values were reported. Of the 59 patients, 56 (89.83%) had migraines or medication-overuse headaches, and 3 (5.08%) had tension-type headaches. No one had trigeminal autonomic cephalalgias or other headaches. The models' overall accuracy and kappa for the ground truth were 94.92% and 0.65 (95%CI 0.21-1.00), respectively. The sensitivity, specificity, precision, and F values for migraines were 98.21%, 66.67%, 98.21%, and 98.21%, respectively. There was disagreement between the AI diagnosis and the ground truth by headache specialists in two patients. This is the first external validation of the AI headache diagnosis model. Further data collection and external validation are required to strengthen and improve its performance in real-world settings.

Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management

PURPOSE OF REVIEW

This review provides an overview of the current and future role of artificial intelligence (AI) and virtual reality (VR) in addressing the complexities inherent to the diagnosis, classification, and management of headache disorders.

RECENT FINDINGS

Through machine learning and natural language processing approaches, AI offers unprecedented opportunities to identify patterns within complex and voluminous datasets, including brain imaging data. This technology has demonstrated promise in optimizing diagnostic approaches to headache disorders and automating their classification, an attribute particularly beneficial for non-specialist providers. Furthermore, AI can enhance headache disorder management by enabling the forecasting of acute events of interest, such as migraine headaches or medication overuse, and by guiding treatment selection based on insights from predictive modeling. Additionally, AI may facilitate the streamlining of treatment efficacy monitoring and enable the automation of real-time treatment parameter adjustments. VR technology, on the other hand, offers controllable and immersive experiences, thus providing a unique avenue for the investigation of the sensory-perceptual symptomatology associated with certain headache disorders. Moreover, recent studies suggest that VR, combined with biofeedback, may serve as a viable adjunct to conventional treatment. Addressing challenges to the widespread adoption of AI and VR in headache medicine, including reimbursement policies and data privacy concerns, mandates collaborative efforts from stakeholders to enable the equitable, safe, and effective utilization of these technologies in advancing headache disorder care. This review highlights the potential of AI and VR to support precise diagnostics, automate classification, and enhance management strategies for headache disorders.

Sailing the Seven Seas: A Multinational Comparison of ChatGPT's Performance on Medical Licensing Examinations

PURPOSE

The use of AI-powered technology, particularly OpenAI's ChatGPT, holds significant potential to reshape healthcare and medical education. Despite existing studies on the performance of ChatGPT in medical licensing examinations across different nations, a comprehensive, multinational analysis using rigorous methodology is currently lacking. Our study sought to address this gap by evaluating the performance of ChatGPT on six different national medical licensing exams and investigating the relationship between test question length and ChatGPT's accuracy.

METHODS

We manually inputted a total of 1,800 test questions (300 each from US, Italian, French, Spanish, UK, and Indian medical licensing examination) into ChatGPT, and recorded the accuracy of its responses.

RESULTS

We found significant variance in ChatGPT's test accuracy across different countries, with the highest accuracy seen in the Italian examination (73% correct answers) and the lowest in the French examination (22% correct answers). Interestingly, question length correlated with ChatGPT's performance in the Italian and French state examinations only. In addition, the study revealed that questions requiring multiple correct answers, as seen in the French examination, posed a greater challenge to ChatGPT.

CONCLUSION

Our findings underscore the need for future research to further delineate ChatGPT's strengths and limitations in medical test-taking across additional countries and to develop guidelines to prevent AI-assisted cheating in medical examinations.

The Ethical Stewardship of Artificial Intelligence in Chronic Pain and Headache: A Narrative Review

PURPOSE OF REVIEW

As artificial intelligence (AI) and machine learning (ML) are becoming more pervasive in medicine, understanding their ethical considerations for chronic pain and headache management is crucial for optimizing their safety.

RECENT FINDINGS

We reviewed thirty-eight editorial and original research articles published between 2018 and 2023, focusing on the application of AI and ML to chronic pain or headache. The core medical principles of beneficence, non-maleficence, autonomy, and justice constituted the evaluation framework. The AI applications addressed topics such as pain intensity prediction, diagnostic aides, risk assessment for medication misuse, empowering patients to self-manage their conditions, and optimizing access to care. Virtually all AI applications aligned both positively and negatively with specific medical ethics principles. This review highlights the potential of AI to enhance patient outcomes and physicians' experiences in managing chronic pain and headache. We emphasize the importance of carefully considering the advantages, disadvantages, and unintended consequences of utilizing AI tools in chronic pain and headache, and propose the four core principles of medical ethics as an evaluation framework.

Turn Your Vision into Reality-AI-Powered Pre-operative Outcome Simulation in Rhinoplasty Surgery

BACKGROUND

The increasing demand and changing trends in rhinoplasty surgery emphasize the need for effective doctor-patient communication, for which Artificial Intelligence (AI) could be a valuable tool in managing patient expectations during pre-operative consultations.

OBJECTIVE

To develop an AI-based model to simulate realistic postoperative rhinoplasty outcomes.

METHODS

We trained a Generative Adversarial Network (GAN) using 3,030 rhinoplasty patients' pre- and postoperative images. One-hundred-one study participants were presented with 30 pre-rhinoplasty patient photographs followed by an image set consisting of the real postoperative versus the GAN-generated image and asked to identify the GAN-generated image.

RESULTS

The study sample (48 males, 53 females, mean age of 31.6 ± 9.0 years) correctly identified the GAN-generated images with an accuracy of 52.5 ± 14.3%. Male study participants were more likely to identify the AI-generated images compared with female study participants (55.4% versus 49.6%; p = 0.042).

CONCLUSION

We presented a GAN-based simulator for rhinoplasty outcomes which used pre-operative patient images to predict accurate representations that were not perceived as different from real postoperative outcomes.

LEVEL OF EVIDENCE III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A Novel Artificial Intelligence Model for Symmetry Evaluation in Breast Cancer Patients

INTRODUCTION

Artificial intelligence (AI) is a milestone for human technology. In medicine, AI is set to play an important role as we progress into a new era. In plastic surgery, AI can participate in breast symmetry assessment, which until now has been mainly subjective, allowing for inconsistencies. This study aims to improve this evaluation process by integrating a novel trained neural network with the breast symmetry calculator, BAS-Calc.

MATERIALS AND METHODS

We combined the BAS-Calc tool with a custom-made neural network trained to automatically detect key features of the breast. This integrated system was tested on 81 images of patients who had undergone breast reconstruction post-breast cancer treatment. Its performance was evaluated against two human observers using statistical analysis.

RESULTS

Our model successfully detected 399/405 (98.51%) of landmarks. Spearman and Pearson correlation indicated a strong positive relationship while Cohen's kappa demonstrated moderate to strong agreement between human observers and AI model. Notably, the average calculation time for the AI was 0.92 seconds, 16 times faster than the 14.09 seconds for humans.

CONCLUSIONS

Our AI model successfully calculated breast symmetry from images of patients who had undergone reconstructive oncological breast surgery, demonstrating high correlation with human assessments and a markedly reduced processing time. As AI continues to evolve, it is poised to become a pivotal tool in Medicine. Therefore, it is crucial for medical professionals to proactively engage in implementing AI technologies safely and effectively. Further studies are required to broaden our understanding and maximize the potential benefits in this area. Takeaway bullet points Artificial intelligence (AI) is an upcoming force to be reckoned with. AI should find its way into practical applications in plastic surgery. AI can be applied to improve patient care and evaluate aesthetic results. In this work, we present a novel AI model that automatically evaluates breast symmetry.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

The Clinical Relevance of Artificial Intelligence in Migraine

Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs' management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine.

Pure Wisdom or Potemkin Villages? A Comparison of ChatGPT 3.5 and ChatGPT 4 on USMLE Step 3 Style Questions: Quantitative Analysis

BACKGROUND

The United States Medical Licensing Examination (USMLE) has been critical in medical education since 1992, testing various aspects of a medical student's knowledge and skills through different steps, based on their training level. Artificial intelligence (AI) tools, including chatbots like ChatGPT, are emerging technologies with potential applications in medicine. However, comprehensive studies analyzing ChatGPT's performance on USMLE Step 3 in large-scale scenarios and comparing different versions of ChatGPT are limited.

OBJECTIVE

This paper aimed to analyze ChatGPT's performance on USMLE Step 3 practice test questions to better elucidate the strengths and weaknesses of AI use in medical education and deduce evidence-based strategies to counteract AI cheating.

METHODS

A total of 2069 USMLE Step 3 practice questions were extracted from the AMBOSS study platform. After including 229 image-based questions, a total of 1840 text-based questions were further categorized and entered into ChatGPT 3.5, while a subset of 229 questions were entered into ChatGPT 4. Responses were recorded, and the accuracy of ChatGPT answers as well as its performance in different test question categories and for different difficulty levels were compared between both versions.

RESULTS

Overall, ChatGPT 4 demonstrated a statistically significant superior performance compared to ChatGPT 3.5, achieving an accuracy of 84.7% (194/229) and 56.9% (1047/1840), respectively. A noteworthy correlation was observed between the length of test questions and the performance of ChatGPT 3.5 (ρ=-0.069; P=.003), which was absent in ChatGPT 4 (P=.87). Additionally, the difficulty of test questions, as categorized by AMBOSS hammer ratings, showed a statistically significant correlation with performance for both ChatGPT versions, with ρ=-0.289 for ChatGPT 3.5 and ρ=-0.344 for ChatGPT 4. ChatGPT 4 surpassed ChatGPT 3.5 in all levels of test question difficulty, except for the 2 highest difficulty tiers (4 and 5 hammers), where statistical significance was not reached.

CONCLUSIONS

In this study, ChatGPT 4 demonstrated remarkable proficiency in taking the USMLE Step 3, with an accuracy rate of 84.7% (194/229), outshining ChatGPT 3.5 with an accuracy rate of 56.9% (1047/1840). Although ChatGPT 4 performed exceptionally, it encountered difficulties in questions requiring the application of theoretical concepts, particularly in cardiology and neurology. These insights are pivotal for the development of examination strategies that are resilient to AI and underline the promising role of AI in the realm of medical education and diagnostics.

New management strategies for primary headache disorders: Insights from P4 medicine

Primary headache disorder is the main cause of headache attacks, leading to significant disability and impaired quality of life. This disorder is increasingly recognized as a heterogeneous condition with a complex network of genetic, environmental, and lifestyle factors. However, the timely diagnosis and effective treatment of these headaches remain challenging. Precision medicine is a potential strategy based on P4 (predictive, preventive, personalized, and participatory) medicine that may bring new insights for headache care. Recent machine learning advances and widely available molecular biology and imaging data have increased the usefulness of this medical strategy. Precision medicine emphasizes classifying headaches according to their risk factors, clinical presentation, and therapy responsiveness to provide individualized headache management. Furthermore, early preventive strategies, mainly utilizing predictive tools, are critical in reducing headache attacks and improving the quality of life of individuals with headaches. The current review comprehensively discusses the potential application value of P4 medicine in headache management.

Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review

Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies.

Man, or Machine? Artificial Intelligence Language Systems in Plastic Surgery

Artificial intelligence (AI) language models are computer programs trained to understand and generate human-like text. The latest AI language models available to the public have impressive language generation capability with immediate applications in both academia and private practice. Plastic surgeons can immediately leverage this technology to more efficiently allocate valuable human capital to higher-yield tasks. This can ultimately translate to higher patient volume, higher research output, and improved patient communication. Commercially available models offer business solutions that should not be ignored by plastic surgeons hoping to establish, optimize, or grow their practices. In this paper, the authors review the current state of AI language systems, discuss potential applications, and explore the risks and limitations of this technology.