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.

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.

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.

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.

An algorithmic approach to preoperative studies and patient selection for hemispheric disconnection surgery: a literature review.

Hemispheric disconnection surgery (HDS) is one of the most effective surgical options for appropriate candidates with medically-refractory epilepsy (MRE) in whom most or all seizures arise from diffuse areas within a single hemisphere. While there are several well-accepted indications for HDS, there are additional patients who may benefit from HDS. However, there are no standardized recommendations for how preoperative studies should be used to identify appropriate candidates for HDS. We aimed to propose an algorithmic approach for presurgical evaluation in order to guide appropriate implementation of HDS for either cure or palliation of severe MRE in infants, children, and adults. We performed a qualitative review of the literature using PubMed, the Cochrane Library, and Google Scholar to select primary articles addressing imaging modalities used for the presurgical evaluation of patients with MRE being considered for HDS. In total, we identified 126 articles that met our inclusion criteria. We propose a framework to guide candidate selection for HDS that incorporates various elements of the clinical presentation, electroencephalographic analysis, and neuroimaging. While this approach still requires prospective validation, the authors feel it is grounded in a synthesis of the best available evidence in the literature and informed by expert opinion. HDS is a powerful tool in the armamentarium of experienced multi-disciplinary epilepsy centers to treat patients with severe MRE arising from diffuse areas constrained to a single hemisphere. The under-utilization of epilepsy surgery may be, in part, remedied by establishing evidence-based pathways for presurgical analyses to determine surgical candidacy.

Differentiating Flail Limb Syndrome From Amyotrophic Lateral Sclerosis.

OBJECTIVE: The aim of the study was to investigate differences between flail limb syndrome and amyotrophic lateral sclerosis. DESIGN: A retrospective chart review identified 16 cases of amyotrophic lateral sclerosis and 16 of flail limb syndrome. Revised Amyotrophic Lateral Sclerosis Functional Rating Scale, compound muscle action potential amsplitudes, and rate of loss of vital capacity were compared. RESULTS: Comparing amyotrophic lateral sclerosis and flail limb syndrome patients, rate of loss of vital capacity was 5.26% ± 0.33% versus 0.54% ± 0.06%, respectively (P < 0.05). No patient in the flail limb syndrome group had a rate of loss of vital capacity more than 0.65% per month. No patient in the amyotrophic lateral sclerosis group had a rate of loss of vital capacity less than 4.6% per month. The average ulnar nerve compound muscle action potential amplitudes were significantly lower in flail limb syndrome (P < 0.05). No significant difference was observed in the rate of Revised Amyotrophic Lateral Sclerosis Functional Rating Scale decline or average peroneal, tibial, and median nerve compound muscle action potential amplitudes. CONCLUSIONS: In flail limb syndrome, an average monthly decrease in vital capacity exceeding 0.65% may suggest a spread of motor neuron loss to higher cervical anterior horn areas and raise the possibility of progression to amyotrophic lateral sclerosis. Larger prospective studies are needed to investigate the rate of VC decline in flail limb syndrome and limb-onset amyotrophic lateral sclerosis and to establish whether a cutoff score combining rate of loss of vital capacity and compound muscle action potential amplitude mainly of the ulnar nerve might predict progression of flail limb syndrome to amyotrophic lateral sclerosis, the knowledge of which can facilitate appropriate patient counseling.

Potential Uses of Isolated Toxin Peptides in Neuropathic Pain Relief: A Literature Review.

Neuropathic pain is a subset of chronic pain that is caused by neurons that are damaged or firing aberrantly in the peripheral or central nervous systems. The treatment guidelines for neuropathic pain include antidepressants, calcium channel α2 delta ligands, topical therapy, and opioids as a second-line option. Pharmacotherapy has not been effective in the treatment of neuropathic pain except in the treatment of trigeminal neuralgia with carbamazepine. The inability to properly treat neuropathic pain causes frustration in both the patients and their treating physicians. Venoms, which are classically believed to be causes of pain and death, have peptide components that have been implicated in pain relief. Although some venoms are efficacious and have shown benefits in patients, their side-effect profile precludes their more widespread use. This review identifies and explores the use of venoms in neuropathic pain relief. This treatment can open doors to potential therapeutic targets. We believe that further research into the mechanisms of action of these receptors as well as their functions in nature will provide alternative therapies as well as a window into how they affect neuropathic pain.