Self-improving generative foundation model for synthetic medical image generation and clinical applications.

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.

CNS-CLIP: Transforming a Neurosurgical Journal Into a Multimodal Medical Model.

Background And Objectives: 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.

Generalizability of kidney transplant data in electronic health records - The Epic Cosmos database vs the Scientific Registry of Transplant Recipients.

Developing real-world evidence from electronic health records (EHR) is vital to advancing kidney transplantation (KT). We assessed the feasibility of studying KT using the Epic Cosmos aggregated EHR data set, which includes 274 million unique individuals cared for in 238 US health systems, by comparing it with the Scientific Registry of Transplant Recipients (SRTR). We identified 69 418 KT recipients who underwent transplants between January 2014 and December 2022 in Cosmos (39.

Economics and Equity of Large Language Models: Health Care Perspective.

Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway.

Hospitalization and Hospitalized Delirium Are Associated With Decreased Access to Kidney Transplantation and Increased Risk of Waitlist Mortality.

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.

ChatGPT Solving Complex Kidney Transplant Cases: A Comparative Study With Human Respondents.

Introduction: ChatGPT has shown the ability to answer clinical questions in general medicine but may be constrained by the specialized nature of kidney transplantation. Thus, it is important to explore how ChatGPT can be used in kidney transplantation and how its knowledge compares to human respondents.

Methods: We prompted ChatGPT versions 3.

Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark.

The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can address this challenge. We present NYUMets-Brain, the world's largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients.

Assessing superficial temporal artery-middle cerebral artery anastomosis patency using FLOW 800 hemodynamics.

Objective: The objective of this study was to investigate the use of indocyanine green videoangiography with FLOW 800 hemodynamic parameters intraoperatively during superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery to predict patency prior to anastomosis performance.

Methods: A retrospective and exploratory data analysis was conducted using FLOW 800 software prior to anastomosis to assess four regions of interest (ROIs; proximal and distal recipients and adjacent and remote gyri) for four hemodynamic parameters (speed, delay, rise time, and time to peak). Medical records were used to classify patients into flow and no-flow groups based on immediate or perioperative anastomosis patency.

Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy.

The most widely used fluorophore in glioma-resection surgery, 5-aminolevulinic acid (5-ALA), is thought to cause the selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumour cells. Here we show that the clinical detection of PpIX can be improved via a microscope that performs paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF). We validated the technique in fresh tumour specimens from 115 patients with high-grade gliomas across four medical institutions.

Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial.

Introduction: Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH using deep learning algorithms without using advanced radiological features.

Methods: Data from the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized.

Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review.

Topic: This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification.

Clinical Relevance: Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting.

Reliable Prediction of Discharge Disposition Following Cervical Spine Surgery With Ensemble Machine Learning and Validation on a National Cohort.

Study Design: A retrospective cohort study.

Objective: The purpose of this study is to develop a machine learning algorithm to predict nonhome discharge after cervical spine surgery that is validated and usable on a national scale to ensure generalizability and elucidate candidate drivers for prediction.

Summary Of Background Data: Excessive length of hospital stay can be attributed to delays in postoperative referrals to intermediate care rehabilitation centers or skilled nursing facilities.

A Century of Evolution in Spine Surgery Publications: A Bibliometric Analysis of the Field From 1900 to 2023.

Background And Objectives: Spine surgery has advanced in concert with our deeper understanding of its elements. Narrowly focused bibliometric analyses have been conducted previously, but never on the entire corpus of the field. Using big data and bibliometrics, we appraised the entire corpus of spine surgery publications to study the evolution of the specialty as a scholarly field since 1900.

Developing an Automated Registry (Autoregistry) of Spine Surgery Using Natural Language Processing and Health System Scale Databases.

Background And Objectives: Clinical registries are critical for modern surgery and underpin outcomes research, device monitoring, and trial development. However, existing approaches to registry construction are labor-intensive, costly, and prone to manual error. Natural language processing techniques combined with electronic health record (EHR) data sets can theoretically automate the construction and maintenance of registries.

Causes of Death in Patients With Brain Metastases.

Background And Objectives: Advances in targeted therapies and wider application of stereotactic radiosurgery (SRS) have redefined outcomes of patients with brain metastases. Under modern treatment paradigms, there remains limited characterization of which aspects of disease drive demise and in what frequencies. This study aims to characterize the primary causes of terminal decline and evaluate differences in underlying intracranial tumor dynamics in patients with metastatic brain cancer.

Federated AI, Current State, and Future Potential.

Artificial intelligence and machine learning applications are becoming increasingly popular in health care and medical devices. The development of accurate machine learning algorithms requires large quantities of good and diverse data. This poses a challenge in health care because of the sensitive nature of sharing patient data.

Digital Biomarkers and the Evolution of Spine Care Outcomes Measures: Smartphones and Wearables.

Over the past generation, outcome measures in spine care have evolved from a reliance on clinician-reported assessment toward recognizing the importance of the patient's perspective and the wide incorporation of patient-reported outcomes (PROs). While patient-reported outcomes are now considered an integral component of outcomes assessments, they cannot wholly capture the state of a patient's functionality. There is a clear need for quantitative and objective patient-centered outcome measures.

A Nationwide Study Characterizing the Risk and Outcome Profiles of Multilevel Fusion Procedures in Neuromuscular Scoliosis Patients with Neurofibromatosis Type 1.

Background: Spine abnormalities are a common manifestation of Neurofibromatosis Type 1 (NF1); however, the outcomes of surgical treatment for NF1-associated spinal deformity are not well explored. The purpose of this study was to investigate the outcome and risk profiles of multilevel fusion surgery for NF1 patients.

Methods: The National Inpatient Sample was queried for NF1 and non-NF1 patient populations with neuromuscular scoliosis who underwent multilevel fusion surgery involving eight or more vertebral levels between 2004 and 2017.