Improved Generalizability in Medical Computer Vision: Hyperbolic Deep Learning in Multi-Modality Neuroimaging.

Deep learning has shown significant value in automating radiological diagnostics but can be limited by a lack of generalizability to external datasets. Leveraging the geometric principles of non-Euclidean space, certain geometric deep learning approaches may offer an alternative means of improving model generalizability. This study investigates the potential advantages of hyperbolic convolutional neural networks (HCNNs) over traditional convolutional neural networks (CNNs) in neuroimaging tasks.

Perinatal Factors Associated with Successful Pharmacologic Closure of the Patent Ductus Arteriosus in Premature Infants.

The patent ductus arteriosus (PDA) is associated with significant morbidity and mortality in preterm infants. While pharmacologic closure of the PDA is common and effective, it can be difficult to identify which patients will respond. As such, the objective of this study was to identify factors associated with successful pharmacologic closure of the PDA.

The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study.

Background: Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a single task using labelled data, making deployment and generalisability challenging. How well a general-purpose AI language model performs diagnosis and triage relative to physicians and laypeople is not well understood.

Methods: We compared the predictive accuracy of Generative Pre-trained Transformer 3 (GPT-3)'s diagnostic and triage ability for 48 validated synthetic case vignettes (<50 words; sixth-grade reading level or below) of both common (eg, viral illness) and severe (eg, heart attack) conditions to a nationally representative sample of 5000 lay people from the USA who could use the internet to find the correct options and 21 practising physicians at Harvard Medical School.

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed.

Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022.

Background: Many individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin-related discourse is unknown.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years.

Safe and reliable transport of prediction models to new healthcare settings without the need to collect new labeled data.

How can practitioners and clinicians know if a prediction model trained at a different institution can be safely used on their patient population? There is a large body of evidence showing that small changes in the distribution of the covariates used by prediction models may cause them to fail when deployed to new settings. This specific kind of dataset shift, known as covariate shift, is a central challenge to implementing existing prediction models in new healthcare environments. One solution is to collect additional labels in the target population and then fine tune the prediction model to adapt it to the characteristics of the new healthcare setting, which is often referred to as localization.

Illuminating protein space with a programmable generative model.

Three billion years of evolution has produced a tremendous diversity of protein molecules, but the full potential of proteins is likely to be much greater. Accessing this potential has been challenging for both computation and experiments because the space of possible protein molecules is much larger than the space of those likely to have functions. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions.

Artificial intelligence in the neonatal intensive care unit: the time is now.

Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluence of technological progress, commercialization pathways, and rich data sets provides a unique opportunity for AI to make a lasting impact on the NICU. In this perspective article, we discuss four broad categories of AI applications in the NICU: imaging interpretation, prediction modeling of electronic health record data, integration of real-time monitoring data, and documentation and billing.

National Needs Assessment of Utilization of Common Newborn Clinical Decision Support Tools.

Objective: Clinical decision support tools (CDSTs) are common in neonatology, but utilization is rarely examined. We examined the utilization of four CDSTs in newborn care.

Study Design: A 72-field needs assessment was developed.

The Diagnostic and Triage Accuracy of the GPT-3 Artificial Intelligence Model.

Importance: Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a single task using labeled data, making deployment and generalizability challenging. Whether a general-purpose AI language model can perform diagnosis and triage is unknown.

Objective: Compare the general-purpose Generative Pre-trained Transformer 3 (GPT-3) AI model's diagnostic and triage performance to attending physicians and lay adults who use the Internet.

Prediction of extubation failure among low birthweight neonates using machine learning.

Objective: To develop machine learning models predicting extubation failure in low birthweight neonates using large amounts of clinical data.

Study Design: Retrospective cohort study using MIMIC-III, a large single-center, open-source clinical dataset. Logistic regression and boosted-tree (XGBoost) models using demographics, medications, and vital sign and ventilatory data were developed to predict extubation failure, defined as reintubation within 7 days.

A structural characterization of shortcut features for prediction.

With the rising use of machine learning for healthcare applications, practitioners are increasingly confronted with the limitations of prediction models that are trained in one setting but meant to be deployed in several others. One recently identified limitation is so-called shortcut learning, whereby a model learns to associate features with the prediction target that do not maintain their relationship across settings. Famously, the watermark on chest x-rays has been demonstrated to be an instance of a shortcut feature.

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis.

Long diagnostic wait times hinder international efforts to address antibiotic resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution.

Regional differences in utilization of 17α-hydroxyprogesterone caproate (17-OHP).

Objectives: To describe regional differences in utilization of 17α-hydroxyprogesterone caproate (17-OHP).

Methods: Retrospective cohort study of a large, US commercial managed care plan claims database with pharmacy coverage from 2008 to 2018. Singleton pregnancies with at least one prior spontaneous preterm birth (sPTB) were included.