Longitudinal Investigation of Neurobiological Changes Across Pregnancy.

Pregnancy is a period of profound biological transformation. However, we know remarkably little about pregnancy-related brain changes. To address this gap, we chart longitudinal changes in brain structure during pregnancy and explore potential mechanisms driving these changes.

Not the Models You Are Looking For: Traditional ML Outperforms LLMs in Clinical Prediction Tasks.

Objectives: To determine the extent to which current Large Language Models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors that can impact their adoption, including overall performance, calibration, fairness, and resilience to privacy protections that reduce data fidelity.

Materials And Methods: We evaluated GPT-3.5, GPT-4, and ML (as gradient-boosting trees) on clinical prediction tasks in EHR data from Vanderbilt University Medical Center and MIMIC IV.

Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process.

Objectives: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review.

Background: The launch of ChatGPT (OpenAI) in November 2022 attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including health care. Numerous studies have since been conducted regarding how to use state-of-the-art LLMs in health-related scenarios.

Societal implications of the Dobbs v Jackson Women's Health Organization decision.

On June 24, 2022, the US Supreme Court's decision in Dobbs v Jackson Women's Health Organization marked the removal of the constitutional right to abortion in the USA, introducing a complex ethical and legal landscape for patients and providers. This shift has had immediate health and equity repercussions, but it is also crucial to examine the broader impacts on states, health-care systems, and society as a whole. Restrictions on abortion access extend beyond immediate reproductive care concerns, necessitating a comprehensive understanding of the ruling's consequences across micro and macro levels.

Health-care workforce implications of the Dobbs v Jackson Women's Health Organization decision.

The Dobbs v Jackson Women's Health Organization Supreme Court decision, which revoked the constitutional right to abortion in the USA, has impacted the national medical workforce. Impacts vary across states, but providers in states with restrictive abortion laws now must contend with evolving legal and ethical challenges that have the potential to affect workforce safety, mental health, education, and training opportunities, in addition to having serious impacts on patient health and far-reaching societal consequences. Moreover, Dobbs has consequences on almost every facet of the medical workforce, including on physicians, nurses, pharmacists, and others who work within the health-care system.

A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare.

Background: The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators.

Ethical and Legal Issues Surrounding Genetic Testing in the NICU.

Clinicians practicing in a modern NICU are noticing an increase in the proportion of patients who undergo genetic testing as well as changes in the types of genetic testing patients receive. These trends are not surprising given the increasing recognition of the genetic causes of neonatal illness and recent advances in genetic technology. Yet, the expansion of genetic testing in the NICU also raises a number of ethical questions.

Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer.

Intellectual conflicts of interest among cardiology and pulmonology clinical practice guidelines.

Background: Intellectual conflicts of interest (COI), like financial COI, may threaten the validity and trustworthiness of clinical practice guidelines (CPGs). However, comparatively little is known about intellectual COI in CPGs. This study sought to estimate the prevalence of intellectual COI and corresponding management strategies among cardiology and pulmonology CPGs.

Studying the impact of translational genomic research: Lessons from eMERGE.

Two major goals of the Electronic Medical Record and Genomics (eMERGE) Network are to learn how best to return research results to patient/participants and the clinicians who care for them and also to assess the impact of placing these results in clinical care. Yet since its inception, the Network has confronted a host of challenges in achieving these goals, many of which had ethical, legal, or social implications (ELSIs) that required consideration. Here, we share impediments we encountered in recruiting participants, returning results, and assessing their impact, all of which affected our ability to achieve the goals of eMERGE, as well as the steps we took to attempt to address these obstacles.

Education and electronic medical records and genomics network, challenges, and lessons learned from a large-scale clinical trial using polygenic risk scores.

Polygenic risk scores (PRS) have potential to improve health care by identifying individuals that have elevated risk for common complex conditions. Use of PRS in clinical practice, however, requires careful assessment of the needs and capabilities of patients, providers, and health care systems. The electronic Medical Records and Genomics (eMERGE) network is conducting a collaborative study which will return PRS to 25,000 pediatric and adult participants.

Concordance of International Regulation of Pediatric Health Research.

Objective: To assess the comparability of international ethics principles and practices used in regulating pediatric research as a first step in determining whether reciprocal deference for international ethics review is feasible. Prior studies by the authors focused on other aspects of international health research, such as biobanks and direct-to-participant genomic research. The unique nature of pediatric research and its distinctive regulation by many countries warranted a separate study.

A Representativeness-informed Model for Research Record Selection from Electronic Medical Record Systems.

Scientific and clinical studies have a long history of bias in recruitment of underprivileged and minority populations. This underrepresentation leads to inaccurate, inapplicable, and non-generalizable results. Electronic medical record (EMR) systems, which now drive much research, often poorly represent these groups.

A game theoretic approach to balance privacy risks and familial benefits.

As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users' valuations of the service will affect a population's behavior.

Implicit Incentives Among Reddit Users to Prioritize Attention Over Privacy and Reveal Their Faces When Discussing Direct-to-Consumer Genetic Test Results: Topic and Attention Analysis.

Background: As direct-to-consumer genetic testing services have grown in popularity, the public has increasingly relied upon online forums to discuss and share their test results. Initially, users did so anonymously, but more recently, they have included face images when discussing their results. Various studies have shown that sharing images on social media tends to elicit more replies.

Human-Centered Design to Address Biases in Artificial Intelligence.

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles.

Managing re-identification risks while providing access to the All of Us research program.

Objective: The All of Us Research Program makes individual-level data available to researchers while protecting the participants' privacy. This article describes the protections embedded in the multistep access process, with a particular focus on how the data was transformed to meet generally accepted re-identification risk levels.

Methods: At the time of the study, the resource consisted of 329 084 participants.

Avoiding Liability and Other Legal Land Mines in the Evolving Genomics Landscape.

This article reviews evolving legal implications for clinicians and researchers as genomics is used more widely in both the clinic and in translational research, reflecting rapid changes in scientific knowledge as well as the surrounding cultural and political environment. Professionals will face new and changing duties to make or act upon a genetic diagnosis, address direct-to-consumer genetic testing in patient care, consider the health implications of results for patients' family members, and recontact patients when test results change over time. Professional duties in reproductive genetic testing will need to be recalibrated in response to disruptive changes to reproductive rights in the United States.