Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution.

The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a computational algorithm that incorporates transcriptome size into scRNA-seq normalization and bulk deconvolution. ReDeconv introduces a scRNA-seq normalization approach, Count based on Linearized Transcriptome Size (CLTS), which corrects differential expressed genes typically misidentified by standard count per 10 K normalization, as confirmed by orthogonal validations.

A multimodal generative AI copilot for human pathology.

Computational pathology has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology.

A General-Purpose Self-Supervised Model for Computational Pathology.

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale.

Algorithmic fairness in artificial intelligence for medicine and healthcare.

In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how patients are diagnosed, treated and billed. In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities.

Technology Literacy in Undergraduate Medical Education: Review and Survey of the US Medical School Innovation and Technology Programs.

Background: Modern innovations, like machine learning, genomics, and digital health, are being integrated into medical practice at a rapid pace. Physicians in training receive little exposure to the implications, drawbacks, and methodologies of upcoming technologies prior to their deployment. As a result, there is an increasing need for the incorporation of innovation and technology (I&T) training, starting in medical school.

Social determinants of health and treatment decisions in head and neck cancer.

Background: This study compares select social determinants of health (SDOH) with treatment modality selection and treatment completion in head and neck cancer (HNC) patients, to better understand disparities in health outcomes.

Methods: A retrospective cohort study of HNC (n = 1428) patients was conducted. Demographic and disease-specific variables were recorded, including treatment modality selection and completion.

Geographic Barriers Affect Follow-Up Care in Head and Neck Cancer.

Objectives/hypothesis: Follow-up care in head and neck cancers (HNC) is critical in managing patient health. However, social determinants of health (SDOH) can create difficulties in maintaining follow-up care. The study goal is to explore how SDOH impacts maintenance of HNC follow-up care appointments.

The effect of a statewide prescription drug monitoring program on opioid prescribing patterns.

Purpose: Determine whether opioid prescribing patterns have changed as a result of implementation of a prescription drug monitoring program (PDMP) in the state of Massachusetts.

Materials And Methods: A multicentered retrospective study was performed including patients who received tonsillectomy, parotidectomy, thyroidectomy or direct laryngoscopy and biopsy with or without rigid esophagoscopy and/or rigid bronchoscopy at Lahey Hospital and Medical Center (Burlington, MA) or Boston Medical Center (Boston, MA). Opioid prescribing patterns were compared for the 12 months prior to implementation of the Massachusetts Prescription Awareness Tool (MassPAT) to 36 months of prescribing patterns post implementation.

Long-Term Opioid Use in Post-Surgical Management of Patients With Head and Neck Cancer.

Objective: This study aims to identify clinical and socioeconomic factors associated with long-term, post-surgical opioid use in the head and neck cancer population.

Methods: A single center retrospective study was conducted including patients diagnosed with head and neck cancer between January 1, 2014 and July 1, 2019 who underwent primary surgical management. The primary outcome measure was continued opioid use 6 months after treatment completion.

Changes in Care Provision During COVID-19 Impact Patient Well-Being.

The fast onset and extensive impact of COVID-19 necessitated strict public health measures and temporary diversion of personnel and resources from other types of medical care. This study examined the prevalence of such disruptions and their impacts on patient-perceived well-being using an untargeted survey. The majority of surveyed patients experienced changes in their routine medical care.

Spontaneous Labor Onset and Outcomes in Obese Women at Term.

Objective: The objective of this study was to compare the rates of spontaneous labor onset and its progression in obese and nonobese women after 37 weeks.

Study Design: We performed a secondary analysis of a retrospective cohort of all women who were admitted for delivery at ≥ 37 weeks of gestation at a university-based tertiary care center between 2004 and 2010. The cohort was stratified by weeks of gestation at which the patient presented for delivery.