The role and use of race within health - related artificial intelligence and machine learning (AI/ML) models has sparked increasing attention and controversy. Despite the complexity and breadth of related issues, a robust and holistic framework to guide stakeholders in their examination and resolution remains lacking . This perspective provides a broad - based, systematic, and cross - cutting landscape analysis of race - related challenges, structured around the AI/ML lifecycle and framed through " p oints to c onsider " to support inquiry and decision - making. INTRODUCTION The role and use of the social construct of race within health - related artificial intelligence and machine learning (AI/ML) models has become a subject of increased attention and controversy. As noted in the National Academies recent report " Ending Unequal Treatment ", it is increasingly clear that race in all its complexity is a powerful predictor of unequal treatment and health care outcomes.

Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell subtyping often requires expert knowledge and the use of specialized stains. To reduce the annotation burden, AI has been proposed for the classification of cells on H&E. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on H&E of the colon. However, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We use inter-modality learning to label previously un-labelable cell types on H&E. We take advantage of multiplexed immunofluorescence (MxIF) histology to label 14 cell subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on private virtual H&E and public real H&E. On virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively, when using ground truth centroid information. On real H&E we could classify helper T cells and epithelial progenitors with upper bound positive predictive values of $0.43 \pm 0.03$ (parent class prevalence 0.21) and $0.94 \pm 0.02$ (parent class prevalence 0.49) when using ground truth centroid information. This is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. A chemotherapy alternative called immunotherapy is showing promise in treating cancer -- and a new artificial intelligence tool could help ensure that patients have the best possible experience. Immunotherapy, first approved in 2011, uses the cancer patient's own immune system to target and fight cancer. While it doesn't work for everyone, for the 15% to 20% who do see results, it can be life-saving.

Melax Tech, a world leader in AI-powered software provider of biomedical natural language processing (NLP) technology, announced the partnership with Vanderbilt University Medical Center (VUMC), one of the largest academic medical centers in the Southeast as their official NLP technology provider. This partnership will provide the de-identification of VUMC clinical notes to promote the secondary use of EHRs for the Vanderbilt research community. "We are grateful to VUMC for the trust they have placed in our organization and look forward to a long and fruitful relationship," said Andre Pontin, CEO of Melax Tech. Melax Tech empowers businesses, laboratories, and other life sciences organizations to use natural language processing (NLP) technology to unlock unstructured textual data. Clients use our AI-powered software to uncover insights, make decisions, and research breakthroughs.

A machine learning algorithm that predicts suicide attempt recently underwent a prospective trial at the institution where it was developed, Vanderbilt University Medical Center. Over the 11 consecutive months concluding in April 2020, predictions ran silently in the background as adult patients were seen at VUMC. The algorithm, dubbed the Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) model, uses routine information from electronic health records (EHRs) to calculate 30-day risk of return visits for suicide attempt, and, by extension, suicidal ideation. Suicide has been on the rise in the U.S. for a generation and is estimated to claim the lives of 14 in 100,000 Americans each year, making it the nation's tenth leading cause of death. Nationally, some 8.5% of suicide attempts end in death.

A machine learning algorithm that predicts suicide attempt recently underwent a prospective trial at the institution where it was developed, Vanderbilt University Medical Center. Over the 11 consecutive months concluding in April 2020, predictions ran silently in the background as adult patients were seen at VUMC. The algorithm, dubbed the Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) model, uses routine information from electronic health records (EHRs) to calculate 30-day risk of return visits for suicide attempt, and, by extension, suicidal ideation. Suicide has been on the rise in the U.S. for a generation and is estimated to claim the lives of 14 in 100,000 Americans each year, making it the nation's tenth leading cause of death. Nationally, some 8.5% of suicide attempts end in death.

The role of artificial intelligence, or machine learning, will be pivotal as the industry wrestles with a gargantuan amount of data that could improve -- or muddle -- health and cost priorities, according to a National Academy of Medicine Special Publication on the use of AI in health care. Yet, the current explosion of investment and development is happening without an underpinning of consensus of responsible, transparent deployment, which potentially constrains its potential. The new report is designed to be a comprehensive reference for organizational leaders, health care professionals, data analysts, model developers and those who are working to integrate machine learning into health care, said Vanderbilt University Medical Center's Michael Matheny, MD, MS, MPH, Associate Professor in the Department of Biomedical Informatics, and co-editor of AI in Healthcare: The Hope, The Hype, The Promise, The Peril. It's critical for the health care community to learn from both the successes, ...

Diabetic nephropathy (DN) is defined by elevated urine albumin excretion or reduced glomerular filtration rate (GFR), or both. While DN may be diagnosed clinically, pathology is often needed to confirm the diagnosis and establish the severity of the injury. "In addition to making the correct diagnosis of diabetic nephropathy, we want to be able to assess the severity of the injury." Pathologists usually classify DN based on a visual assessment of glomerular pathology using immunofluorescence microscopy and electron microscopy. Although diagnostic guidelines have been well established, scoring of severity of the lesions may vary among pathologists.

Breaking up with your first love is hard to do, but at the age of 18, it was a particularly traumatic experience for Nikki Mattocks. Rather than the clean break she had hoped for, she found herself being bombarded with hateful messages on social media from her ex-boyfriend's friends. One even urged her to kill herself. The messages made me so depressed and led to me taking an overdose," says Mattocks. She is just one of millions of people around the world who have found themselves the victim of bullying. Even in our modern, progressive society, it is too often overlooked and commonly dismissed as a rite of passage, but bullying affects between a fifth and a third of children at school.

Advances in cancer care such as immunotherapy promise greater success in treatment but also currently come with higher risks and uncertain outcomes. GE Healthcare and Vanderbilt University Medical Center are partnering to develop AI applications which will examine anonymized patient data to help physicians determine better courses of treatment. By retroactively analyzing demographic, genomic, tumor, cellular, proteomic and imaging data from anonymized patient records, these AI apps will assist clinicians in determining the best course of treatment for current and future patients. "This partnership is a great example of the increasing convergence of the tools, technologies and data used by therapy innovators and healthcare providers," said Kieran Murphy, president and CEO of GE Healthcare, in a statement. WHY IT MATTERS Because immunotherapies are expensive and have the potential to cause damage if the wrong therapy is chosen, they are still associated with increased mortalities and cost.