Al-Zahrawi (936-1013 AD): On the Surgical Treatment of Neurological Disorders by the Father of Operative Surgery

BACKGROUND

Medical knowledge during the medieval ages flourished under the influence of great scholars of the Islamic Golden age such as Ibn Sina (Latinized as Avicenna), Abu Bakr al-Razi (Rhazes), and Abu al-Qasim Khalaf ibn al-Abbas al-Zahrawi, known as Albucasis. Much has been written on al-Zahrawi's innovation in various disciplines of medicine and surgery. In this article, we focus for on the contributions of al-Zahrawi toward the treatment of neurological disorders in the surgical chapters of his medical encyclopedia, Kitab al-Tasrif (The Method of Medicine).

METHODS

Excerpts from a modern copy of volume 30 of al-Zahrawi's Kitab al-Tasrif were reviewed and translated by the primary author from Arabic to English, to further provide specific details regarding his neurosurgical knowledge. In addition, a literature search was performed using PubMed and Google Scholar to review prior reports on al-Zahrawi's neurosurgical instructions.

RESULTS

In addition to what is described in the literature of al-Zahrawi's teachings in cranial and spine surgery, we provide insight into his diagnosis and management of cranial and spinal trauma, the devices he used, and prognostication of various traumatic injuries.

CONCLUSIONS

Al-Zahrawi was a renowned physician during the Islamic Golden age who made significant contributions to the diagnosis and treatment of neurological conditions, particularly cranial and spinal cord injuries. He developed innovative surgical techniques for trephination and spinal traction, which are still used in modern neurosurgery. His insights make him worthy of recognition as an important figure in the history of neurological surgery.

Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach

Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients' comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4's potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers' consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American's reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers.

Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators

Importance

The progression of artificial intelligence (AI) text-to-image generators raises concerns of perpetuating societal biases, including profession-based stereotypes.

Objective

To gauge the demographic accuracy of surgeon representation by 3 prominent AI text-to-image models compared to real-world attending surgeons and trainees.

Design, Setting, and Participants

The study used a cross-sectional design, assessing the latest release of 3 leading publicly available AI text-to-image generators. Seven independent reviewers categorized AI-produced images. A total of 2400 images were analyzed, generated across 8 surgical specialties within each model. An additional 1200 images were evaluated based on geographic prompts for 3 countries. The study was conducted in May 2023. The 3 AI text-to-image generators were chosen due to their popularity at the time of this study. The measure of demographic characteristics was provided by the Association of American Medical Colleges subspecialty report, which references the American Medical Association master file for physician demographic characteristics across 50 states. Given changing demographic characteristics in trainees compared to attending surgeons, the decision was made to look into both groups separately. Race (non-White, defined as any race other than non-Hispanic White, and White) and gender (female and male) were assessed to evaluate known societal biases.

Exposures

Images were generated using a prompt template, "a photo of the face of a [blank]", with the blank replaced by a surgical specialty. Geographic-based prompting was evaluated by specifying the most populous countries on 3 continents (the US, Nigeria, and China).

Main Outcomes and Measures

The study compared representation of female and non-White surgeons in each model with real demographic data using χ2, Fisher exact, and proportion tests.

Results

There was a significantly higher mean representation of female (35.8% vs 14.7%; P < .001) and non-White (37.4% vs 22.8%; P < .001) surgeons among trainees than attending surgeons. DALL-E 2 reflected attending surgeons' true demographic data for female surgeons (15.9% vs 14.7%; P = .39) and non-White surgeons (22.6% vs 22.8%; P = .92) but underestimated trainees' representation for both female (15.9% vs 35.8%; P < .001) and non-White (22.6% vs 37.4%; P < .001) surgeons. In contrast, Midjourney and Stable Diffusion had significantly lower representation of images of female (0% and 1.8%, respectively; P < .001) and non-White (0.5% and 0.6%, respectively; P < .001) surgeons than DALL-E 2 or true demographic data. Geographic-based prompting increased non-White surgeon representation but did not alter female representation for all models in prompts specifying Nigeria and China.

Conclusion and Relevance

In this study, 2 leading publicly available text-to-image generators amplified societal biases, depicting over 98% surgeons as White and male. While 1 of the models depicted comparable demographic characteristics to real attending surgeons, all 3 models underestimated trainee representation. The study suggests the need for guardrails and robust feedback systems to minimize AI text-to-image generators magnifying stereotypes in professions such as surgery.

Laser amygdalohippocampotomy reduces contralateral hippocampal sub-clinical activity in bitemporal epilepsy: A case illustration of responsive neurostimulator ambulatory recordings

Responsive neurostimulation (RNS) is a valuable tool in the diagnosis and treatment of medication refractory epilepsy (MRE) and provides clinicians with better insights into patients' seizure patterns. In this case illustration, we present a patient with bilateral hippocampal RNS for presumed bilateral mesial temporal lobe epilepsy. The patient subsequently underwent a right sided LITT amygdalohippocampotomy based upon chronic RNS data revealing predominance of seizures from that side. Analyzing electrocorticography (ECOG) from the RNS system, we identified the frequency of high amplitude discharges recorded from the left hippocampal lead pre- and post- right LITT amygdalohippocampotomy. A reduction in contralateral interictal epileptiform activity was observed through RNS recordings over a two-year period, suggesting the potential dependency of the contralateral activity on the primary epileptogenic zone. These findings suggest that early targeted surgical resection or laser ablation by leveraging RNS data can potentially impede the progression of dependent epileptiform activity and may aid in preserving neurocognitive networks. RNS recordings are essential in shaping further management decisions for our patient with a presumed bitemporal epilepsy.