Faculty Bibliography

Artificial intelligence makes strides in specialized diagnostics but faces challenges in complex clinical scenarios, such as rare disease diagnosis and emergency condition identification. To address these limitations, we develop Meta General Practitioner (MetaGP), a 32-billion-parameter generative foundation model trained on extensive datasets, including over 8 million electronic health records, biomedical literature, and medical textbooks. MetaGP demonstrates robust diagnostic capabilities, achieving accuracy comparable to experienced clinicians. In rare disease cases, it achieves an average diagnostic score of 1.57, surpassing GPT-4's 0.93. For emergency conditions, it improves diagnostic accuracy for junior and mid-level clinicians by 53% and 46%, respectively. MetaGP also excels in generating medical imaging reports, producing high-quality outputs for chest X-rays and computed tomography, often rated comparable to or superior to physician-authored reports. These findings highlight MetaGP's potential to transform clinical decision-making across diverse medical contexts.

PURPOSE/OBJECTIVE:Immune checkpoint inhibition (ICI) has revolutionized the treatment of melanoma care. Stereotactic radiosurgery combined with ICI has shown promise to improve clinical outcomes in prior studies in patients who have metastatic melanoma with brain metastases. However, others have suggested that concurrent ICI with stereotactic radiosurgery can increase the risk of complications. METHODS:We present a retrospective, single-institution analysis of 98 patients with a median follow up of 17.1 months managed with immune checkpoint inhibition and stereotactic radiosurgery concurrently and non-concurrently. A total of 55 patients were included in the concurrent group and 43 patients in the non-concurrent treatment group. Cox proportional hazards models were used to assess the relation between concurrent or non-concurrent treatment and overall survival or local progression-free survival. The Wald test was used to assess significance. Significant differences between patients in both groups experiencing adverse events including adverse radiation effects, perilesional edema, and neurological deficits were tested for using the Chi-square or Fisher's exact test. RESULTS:Patients receiving concurrent versus non-concurrent ICI showed a significant increase in overall survival (median 37.1 months, 95% CI: 18.9 months - NA versus median 11.4 months, 95% CI: 6.4-33.2 months, p = 0.0056) but not local progression-free survival. There were no significant differences between groups with regards to adverse radiation effects (2% versus 3%), perilesional edema (20% versus 9%), neurological deficits (3% versus 20%). CONCLUSION/CONCLUSIONS:These results suggest that the timing of ICI does not increase risk of neurological complications when delivered within 4 weeks of SRS.

The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development. We find that replacement of just 0.001% of training tokens with medical misinformation results in harmful models more likely to propagate medical errors. Furthermore, we discover that corrupted models match the performance of their corruption-free counterparts on open-source benchmarks routinely used to evaluate medical LLMs. Using biomedical knowledge graphs to screen medical LLM outputs, we propose a harm mitigation strategy that captures 91.9% of harmful content (F1 = 85.7%). Our algorithm provides a unique method to validate stochastically generated LLM outputs against hard-coded relationships in knowledge graphs. In view of current calls for improved data provenance and transparent LLM development, we hope to raise awareness of emergent risks from LLMs trained indiscriminately on web-scraped data, particularly in healthcare where misinformation can potentially compromise patient safety.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Classical biomedical data science models are trained on a single modality and aimed at one specific task. However, the exponential increase in the size and capabilities of the foundation models inside and outside medicine shows a shift toward task-agnostic models using large-scale, often internet-based, data. Recent research into smaller foundation models trained on specific literature, such as programming textbooks, demonstrated that they can display capabilities similar to or superior to large generalist models, suggesting a potential middle ground between small task-specific and large foundation models. This study attempts to introduce a domain-specific multimodal model, Congress of Neurological Surgeons (CNS)-Contrastive Language-Image Pretraining (CLIP), developed for neurosurgical applications, leveraging data exclusively from Neurosurgery Publications. METHODS:We constructed a multimodal data set of articles from Neurosurgery Publications through PDF data collection and figure-caption extraction using an artificial intelligence pipeline for quality control. Our final data set included 24 021 figure-caption pairs. We then developed a fine-tuning protocol for the OpenAI CLIP model. The model was evaluated on tasks including neurosurgical information retrieval, computed tomography imaging classification, and zero-shot ImageNet classification. RESULTS:CNS-CLIP demonstrated superior performance in neurosurgical information retrieval with a Top-1 accuracy of 24.56%, compared with 8.61% for the baseline. The average area under receiver operating characteristic across 6 neuroradiology tasks achieved by CNS-CLIP was 0.95, slightly superior to OpenAI's Contrastive Language-Image Pretraining at 0.94 and significantly outperforming a vanilla vision transformer at 0.62. In generalist classification, CNS-CLIP reached a Top-1 accuracy of 47.55%, a decrease from the baseline of 52.37%, demonstrating a catastrophic forgetting phenomenon. CONCLUSION/CONCLUSIONS:This study presents a pioneering effort in building a domain-specific multimodal model using data from a medical society publication. The results indicate that domain-specific models, while less globally versatile, can offer advantages in specialized contexts. This emphasizes the importance of using tailored data and domain-focused development in training foundation models in neurosurgery and general medicine.

In many clinical and research settings, the scarcity of high-quality medical imaging datasets has hampered the potential of artificial intelligence (AI) clinical applications. This issue is particularly pronounced in less common conditions, underrepresented populations and emerging imaging modalities, where the availability of diverse and comprehensive datasets is often inadequate. To address this challenge, we introduce a unified medical image-text generative model called MINIM that is capable of synthesizing medical images of various organs across various imaging modalities based on textual instructions. Clinician evaluations and rigorous objective measurements validate the high quality of MINIM's synthetic images. MINIM exhibits an enhanced generative capability when presented with previously unseen data domains, demonstrating its potential as a generalist medical AI (GMAI). Our findings show that MINIM's synthetic images effectively augment existing datasets, boosting performance across multiple medical applications such as diagnostics, report generation and self-supervised learning. On average, MINIM enhances performance by 12% for ophthalmic, 15% for chest, 13% for brain and 17% for breast-related tasks. Furthermore, we demonstrate MINIM's potential clinical utility in the accurate prediction of HER2-positive breast cancer from MRI images. Using a large retrospective simulation analysis, we demonstrate MINIM's clinical potential by accurately identifying targeted therapy-sensitive EGFR mutations using lung cancer computed tomography images, which could potentially lead to improved 5-year survival rates. Although these results are promising, further validation and refinement in more diverse and prospective settings would greatly enhance the model's generalizability and robustness.

Developing real-world evidence from electronic health records (EHR) is vital to advance kidney transplantation (KT). We assessed the feasibility of studying KT using the Epic Cosmos aggregated EHR dataset, which includes 274 million unique individuals cared for in 238 U.S. health systems, by comparing it with the Scientific Registry of Transplant Recipients (SRTR). We identified 69,418 KT recipients transplanted between January 2014 and December 2022 in Cosmos (39.4% of all US KT transplants during this period). Demographics and clinical characteristics of recipients captured in Cosmos were consistent with the overall SRTR cohort. Survival estimates were generally comparable, although there were some differences in long-term survival. At 7 years post-transplant, patient survival was 80.4% in Cosmos and 77.8% in SRTR. Multivariable Cox regression showed consistent associations between clinical factors and mortality in both cohorts, with minor discrepancies in the associations between death and both age and race. In summary, Cosmos provides a reliable platform for KT research, allowing EHR-level clinical granularity not available with either the transplant registry or healthcare claims. Consequently, Cosmos will enable novel analyses to improve our understanding of KT management on a national scale.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Pediatric spine surgery has evolved considerably over the past century. No previous study conducted a bibliometric analysis of the corpus of pediatric spine surgery. We used big data and advanced bibliometric analyses to evaluate trends in the progression of pediatric spine surgery as a distinct field since the beginning of the 20th century. METHODS:A Web of Science query was designed to capture the representative corpus of pediatric spine literature. Statistical and bibliometric analyses were performed using various Python packages and the Bibliometrix R package. RESULTS:The collection, published from 1902 to 2023, comprised a total of 11 861 articles from 61 journals and 32 715 unique authors. The overall growth rate annually for publications was 5.08%. An upsurge in publications was seen in the 1980s, after the advent of specialty and subspecialty journals. Illustratively, over 90% of all articles pertaining to pediatric spine surgery were published in the past 3 decades. International and domestic collaboration also increased exponentially over this time period. Reference publication year spectroscopy allowed us to identify 75 articles that comprise the historical roots of modern pediatric spine surgery. There was a recent lexical evolution of topics and terms toward alignment, outcomes, and patient-centric terms. Coauthorship among under-represented groups increased since 1990, but remains low, with disparities persisting across journals. CONCLUSION/CONCLUSIONS:This comprehensive bibliometric analysis on the corpus of pediatric spine surgery offers insight into the evolving landscape of research, authorship, and publication trends over the past century. Advancements in the understanding of the natural history and technology have led the field to become increasingly outcomes focused, all of which have been fueled by pioneering authors. While diversity among authors improves, under-representation of various groups continues to persist, indicating a critical role for further outreach and promotion.

BACKGROUND:Kidney transplant (KT) candidates often experience hospitalizations, increasing their delirium risk. Hospitalizations and delirium are associated with worse post-KT outcomes, yet their relationship with pre-KT outcomes is less clear. Pre-KT delirium may worsen access to KT due to its negative impact on cognition and ability to maintain overall health. METHODS:Using a prospective cohort of 2374 KT candidates evaluated at a single center (2009-2020), we abstracted hospitalizations and associated delirium records after listing via chart review. We evaluated associations between waitlist mortality and likelihood of KT with hospitalizations and hospitalized delirium using competing risk models and tested whether associations differed by gerontologic factors. RESULTS: < 0.001), with those aged ≥65 having a 61% lower likelihood of KT. CONCLUSION/CONCLUSIONS:Hospitalization and delirium are associated with worse pre-KT outcomes and have serious implications on candidates' access to KT. Providers should work to reduce preventable instances of delirium.