Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine.

This commentary explores the critical roles of health equity and ethical considerations in the deployment of artificial intelligence (AI) in public health and medicine. As AI increasingly permeates these fields, it promises substantial benefits but also poses risks that could exacerbate existing disparities and ethical challenges. This commentary delves into the current integration of AI technologies, underscores the importance of ethical social responsibility, and discusses the implications for practice and policy.

Disparity dashboards: an evaluation of the literature and framework for health equity improvement.

The growing recognition of differences in health outcomes across populations has led to a slow but increasing shift towards transparent reporting of patient outcomes. In addition, pay-for-equity initiatives, such as those proposed by the Centers for Medicare and Medicaid, will require the reporting of health outcomes across subgroups over time. Dashboards offer one means of visualising data in the health-care context that can highlight essential disparities in clinical outcomes, guide targeted quality-improvement efforts, and ultimately improve health equity.

Evaluating equitable care in the ICU: Creating a causal inference framework to assess the impact of life-sustaining interventions across racial and ethnic groups.

Background: Variability in the provision of intensive care unit (ICU)-interventions may lead to disparities between socially defined racial-ethnic groups.

Research Question: We used causal inference to examine the use of invasive mechanical ventilation (IMV), renal replacement therapy (RRT), and vasopressor agents (VP) to identify disparities in outcomes across race-ethnicity in patients with sepsis.

Study Design And Methods: Single-center, academic referral hospital in Boston, Massachusetts, USA.

Strategies and solutions to address Digital Determinants of Health (DDOH) across underinvested communities.

Healthcare has long struggled to improve services through technology without further widening health disparities. With the significant expansion of digital health, a group of healthcare professionals and scholars from across the globe are proposing the official usage of the term "Digital Determinants of Health" (DDOH) to explicitly call out the relationship between technology, healthcare, and equity. This is the final paper in a series published in PLOS Digital Health that seeks to understand and summarize current knowledge of the strategies and solutions that help to mitigate the negative effects of DDOH for underinvested communities.

A new tool for evaluating health equity in academic journals; the Diversity Factor.

Current methods to evaluate a journal's impact rely on the downstream citation mapping used to generate the Impact Factor. This approach is a fragile metric prone to being skewed by outlier values and does not speak to a researcher's contribution to furthering health outcomes for all populations. Therefore, we propose the implementation of a Diversity Factor to fulfill this need and supplement the current metrics.

Screening Health-Related Social Needs in Hospitals: A Systematic Review of Health Care Professional and Patient Perspectives.

Health outcomes are markedly influenced by health-related social needs (HRSN) such as food insecurity and housing instability. Under new Joint Commission requirements, hospitals have recently increased attention to HRSN to reduce health disparities. To evaluate prevailing attitudes and guide hospital efforts, the authors conducted a systematic review to describe patients' and health care providers' perceptions related to screening for and addressing patients' HRSN in US hospitals.

Evaluating Social Determinants of Health Variables in Advanced Analytic and Artificial Intelligence Models for Cardiovascular Disease Risk and Outcomes: A Targeted Review.

Introduction/purpose: Predictive models incorporating relevant clinical and social features can provide meaningful insights into complex interrelated mechanisms of cardiovascular disease (CVD) risk and progression and the influence of environmental exposures on adverse outcomes. The purpose of this targeted review (2018-2019) was to examine the extent to which present-day advanced analytics, artificial intelligence, and machine learning models include relevant variables to address potential biases that inform care, treatment, resource allocation, and management of patients with CVD.

Methods: PubMed literature was searched using the prespecified inclusion and exclusion criteria to identify and critically evaluate primary studies published in English that reported on predictive models for CVD, associated risks, progression, and outcomes in the general adult population in North America.

The development and implementation of a new hospital performance measure to assess hospital contributions to community health and equity.

Objective: To develop and implement a measure of how US hospitals contribute to community health with a focus on equity.

Data Sources: Primary data from public comments and hospital surveys and secondary data from the IBM Watson Top 100 Hospitals program collected in the United States in 2020 and 2021.

Study Design: A thematic analysis of public comments on the proposed measure was conducted using an iterative grounded approach for theme identification.

Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity.

Artificial intelligence (AI) and machine learning (ML) technologies have not only tremendous potential to augment clinical decision-making and enhance quality care and precision medicine efforts, but also the potential to worsen existing health disparities without a thoughtful, transparent, and inclusive approach that includes addressing bias in their design and implementation along the cancer discovery and care continuum. We discuss applications of AI/ML tools in cancer and provide recommendations for addressing and mitigating potential bias with AI and ML technologies while promoting cancer health equity.

For the Commercially Insured, Equitable Health Benefits Begin With Equitable Health Insurance Design.

Equitable health benefit design is central to addressing the health inequities of individuals with commercial health insurance in the United States. To do so, employers and other plan sponsors must take action to identify and address unmet health and well-being priorities among racialized groups and low-income workers. These historically underrepresented subpopulations will also benefit from more equitable approaches to healthcare benefits design that recognize and meaningfully address access and affordability concerns.

Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.

Background: The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media.

TechQuity is an imperative for health and technology business: Let's work together to achieve it.

Open discussions of social justice and health inequities may be an uncommon focus within information technology science, business, and health care delivery partnerships. However, the COVID-19 pandemic-which disproportionately affected Black, indigenous, and people of color-has reinforced the need to examine and define roles that technology partners should play to lead anti-racism efforts through our work. In our perspective piece, we describe the imperative to prioritize TechQuity-equity and social justice as a technology business strategy-through collaborating in partnerships that focus on eliminating racial and social inequities.

Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care.

The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes.

Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression.

Importance: The lack of standards in methods to reduce bias for clinical algorithms presents various challenges in providing reliable predictions and in addressing health disparities.

Objective: To evaluate approaches for reducing bias in machine learning models using a real-world clinical scenario.

Design, Setting, And Participants: Health data for this cohort study were obtained from the IBM MarketScan Medicaid Database.

Accuracy of an Artificial Intelligence System for Cancer Clinical Trial Eligibility Screening: Retrospective Pilot Study.

Background: Screening patients for eligibility for clinical trials is labor intensive. It requires abstraction of data elements from multiple components of the longitudinal health record and matching them to inclusion and exclusion criteria for each trial. Artificial intelligence (AI) systems have been developed to improve the efficiency and accuracy of this process.

Variations in breast cancer surgical treatment and timing: determinants and disparities.

Purpose: To describe clinical and non-clinical factors associated with receipt of breast conserving surgery (BCS) versus mastectomy and time to surgical intervention.

Methods: Cross-sectional retrospective study of January 1, 2012 through March 31, 2018 data from the IBM MarketScan Commercial Claims and Encounter and Medicare Supplemental Databases. Area Health Resource Files provided non-clinical characteristics and sociodemographic data.

Comparison of an oncology clinical decision-support system's recommendations with actual treatment decisions.

Objective: IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice.

Methods: This study included patients treated between 1/2017 and 7/2018 for breast, colon, lung, and rectal cancers at Bumrungrad International Hospital (BIH), Thailand.

Rapid review: Identification of digital health interventions in atherosclerotic-related cardiovascular disease populations to address racial, ethnic, and socioeconomic health disparities.

Disparities in cardiovascular disease (CVD) and associated health and healthcare delivery outcomes have been partially attributed to differential risk factors, and to prevention and treatment inequities within racial and ethnic (including language) minority groups and low socioeconomic status (SES) populations in urban and rural settings. Digital health interventions (DHIs) show promise in promoting equitable access to high-quality care, optimal utilization, and improved outcomes; however, their potential role and impact has not been fully explored. The role of DHIs to mitigate drivers of the health disparities listed above in populations disproportionately affected by atherosclerotic-related CVD was systematically reviewed using published literature (January 2008-July 2020) from multiple databases.

Clinical Decision Support for High-Risk Stage II Colon Cancer: A Real-World Study of Treatment Concordance and Survival.

Background: Prognostic and pathologic risk factors typically guide clinicians and patients in their choice of surveillance or adjuvant chemotherapy when managing high-risk stage II colon cancer. However, variations in treatment and outcomes in patients with stage II colon cancer remain.

Objective: This study aimed to assess the survival benefits of treatments concordant with suggested therapeutic options from Watson for Oncology, a clinical decision support system.

Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients.

Objective: The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital.

Methods: A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I-III clinical trials registered on clinicaltrials.

Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center.

Purpose: Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials.

Patients And Methods: Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016.

Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here.

An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers.

Setting a National Agenda for Surgical Disparities Research: Recommendations From the National Institutes of Health and American College of Surgeons Summit.

Health care disparities (differential access, care, and outcomes owing to factors such as race/ethnicity) are widely established. Compared with other groups, African American individuals have an increased mortality risk across multiple surgical procedures. Gender, sexual orientation, age, and geographic disparities are also well documented.