Infections simulating immune checkpoint inhibitor toxicities: uncommon and deceptive

Use of immune checkpoint inhibitors (ICIs), a revolutionary treatment in modern oncology, is frequently complicated by immune-related adverse events (irAEs), which can be confused with infections, and vice versa, thus complicating management decisions. In this study, we review the published cases of infections as simulators of irAEs in cancer patients

Interventions Reducing Racial/Ethnic Disparities Across the Cancer Care Continuum: A Scoping Review

https://openworks.mdanderson.org/sumexp23/1126/thumbnail.jp

Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review.

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools

Bias in artificial intelligence algorithms and recommendations for mitigation

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent literature has raised concerns about the propagation of biases and healthcare disparities through implementation of these algorithms. Thus, it is critical to understand the sources of bias inherent in AI-based algorithms. This review aims to highlight the potential sources of bias within each step of developing AI algorithms in healthcare, starting from framing the problem, data collection, preprocessing, development, and validation, as well as their full implementation. For each of these steps, we also discuss strategies to mitigate the bias and disparities. A checklist was developed with recommendations for reducing bias during the development and implementation stages. It is important for developers and users of AI-based algorithms to keep these important considerations in mind to advance health equity for all populations. Author summary Though artificial intelligence (AI) algorithms were initially proposed as a means to improve healthcare and promote health equity, recent literature suggests that such algorithms are associated with bias and disparities. Therefore, we outline the various elements of potential bias in the development and implementation of AI algorithms and discuss strategies to mitigate them

Epidemiology and organ specific sequelae of post-acute COVID19: A Narrative Review

Objectives: “Long COVID”, a term coined by COVID-19 survivors, describes persistent or new symptoms in a subset of patients who have recovered from acute illness. Globally, the population of people infected with SARS-CoV-2 continues to expand rapidly, necessitating the need for a more thorough understanding of the array of potential sequelae of COVID-19. The multisystemic aspects of acute COVID-19 have been the subject of intense investigation, but the long-term complications remain poorly understood. Emerging data from lay press, social media, commentaries, and emerging scientific reports suggest that some COVID-19 survivors experience organ impairment and/or debilitating chronic symptoms, at times protean in nature, which impact their quality of life. Methods/Results: In this review, by addressing separately each body system, we describe the pleiotropic manifestations reported post COVID-19, their putative pathophysiology and risk factors, and attempt to offer guidance regarding work-up, follow-up and management strategies. Long term sequelae involve all systems with a negative impact on mental health, well-being and quality of life, while a subset of patients, report debilitating chronic fatigue, with or without other fluctuating or persistent symptoms, such as pain or cognitive dysfunction. Although the pathogenesis is unclear, residual damage from acute infection, persistent immune activation, mental factors, or unmasking of underlying co-morbidities are considered as drivers. Comparing long COVID with other post viral chronic syndromes may help to contextualize the complex somatic and emotional sequalae of acute COVID-19. The pace of recovery of different aspects of the syndrome remains unclear as the pandemic began only a year ago. Conclusions: Early recognition of long-term effects and thorough follow-up through dedicated multidisciplinary outpatient clinics with a carefully integrated research agenda are essential for treating COVID-19 survivors holistically. (C) 2021 The British Infection Association. Published by Elsevier Ltd. All rights reserved

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. Through a search of English-language Medline, Scopus, and Google Scholar articles published since 2010, 345 articles were identified that discussed the application of digital health technology among underinvested communities. A group of 8 reviewers assessed 132 articles selected at random for the mention of solutions that minimize differences in DDOH. Solutions were then organized by categories of policy; design and development; implementation and adoption; and evaluation and ongoing monitoring. The data were then assessed by category and the findings summarized. The reviewers also looked for common themes across the solutions and evidence of effectiveness. From this limited scoping review, the authors found numerous solutions mentioned across the papers for addressing DDOH and many common themes emerged regardless of the specific community or digital health technology under review. There was notably less information on solutions regarding ongoing evaluation and monitoring which corresponded with a lack of research evidence regarding effectiveness. The findings directionally suggest that universal strategies and solutions can be developed to address DDOH independent of the specific community under focus. With the need for the further development of DDOH measures, we also provide a framework for DDOH assessment

PRISMA checklist.

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.</div