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Consider the problematic outcome if, for example, an algorithm is programmed to maximize accuracy by ignoring incomplete records: the patients with fewer tests or whose medical histories are scattered across multiple EHRs often are those with mental health conditions or lower incomes. The team was then able to advise researchers with less technical expertise on how to assess missingness in their own data and when and how to conduct repairs, such as imputation, on EHRs.

In EHRs, potential confounders abound in the guise of not only missing data but also patient zip codes, differences in which patients get lab tests, diagnostic codes that are chosen for insurance reimbursement, and date of visit standing in for date of onset of condition.

Some turn to legislation and advocacy. Cecchi-Dimeglio joins many others in wanting more regulation in this country around ethics and transparency. Samuel Volchenboum, an associate professor of pediatrics and director of the Center for Research. Informatics at the University of Chicago, argued in a coauthored Harvard Business Review article that government institutions such as the U. Department of Health and Human Services need to prevent medical data sets from being privatized, as credit scores were.

Others, including Beaulieu-Jones and Yu, are trying to pry open black boxes to uncover hidden biases or other issues that could affect patient safety or research quality.

Yu is part of an international research consortium training machine-learning algorithms to interpret histology slides.

The National Center for Health Statistics monitors the adoption of electronic health records by individuals and groups that deliver health care. In a fact sheet issued in May, it reported that in NCHS data are used to study care delivery, services rendered, and patients served across diverse settings. Many neural networks consist of more than one hundred so-called layers that together process tens of millions to hundreds of millions of parameters, says Yu.

He might look at the inputs and outputs between the first few layers to see how they detect lines, edges, circles, and dots in an image, but the higher-level features remain out of reach. On the opposite end of the spectrum from black-box engineering, many AI developers practice open-source coding. Accessible or opaque, simple or convoluted, the key to reducing AI bias is to test, test, test. A basic education in how algorithms work can help users understand where bias might creep in, how to ask an AI application the right questions, how to apply the results in appropriate ways, and when to use or forego a particular piece of software in their work.

Creating more interdisciplinary teams that mix computer scientists, bioinformaticians, clinicians, researchers, and epidemiologists would raise the likelihood of unbiased AI results, wrote the UCSF team. Others call for broadening the demographic diversity of the teams that collect data and recruit for studies, and for funding agencies to more equitably award grants to such researchers.

The teams that build AI tools could use improvement as well; the technology sector lags behind other industries in diversity, according to the U. Government Accountability Office. Cecchi-Dimeglio uses AI when she consults with organizations, including those in the health care and pharmaceutical industries, to improve diversity inclusion. In September, Google released an open-source, visually based algorithm called the What-If Tool that allows users to assess the fairness of machine-learning models without needing to write code; Microsoft and IBM report that they are also working on automated bias detectors.

More broadly, Beaulieu-Jones points out that since algorithms learn from current practice, they can surface existing biases. There are even murmurings about training algorithms on human ethics alongside technical tasks. As medical AI evolves, the community faces difficult questions. Who decides what is fair? How much bias is acceptable? Do algorithms need to be perfect, simply better than people, or merely as good?

AI models that perform as well as or better than an average practitioner could benefit regions that are short on specialists, wrote Yu and colleagues in Nature Biomedical Engineering in October. Nor could they, says Yu, who rests easy knowing that physicians will always have empathy and human touch on their side. Artificial Intelligence Algorithms. Stephanie Dutchen In , researchers in Heidelberg, Germany, built a sophisticated computer model, called a neural network, to identify melanomas based on clinical images.

Kun-Hsing Yu. An all-purpose tool With its increasing power to analyze enormous data sets and make accurate predictions, artificial intelligence is poised to sweep through medicine.

Scientists Brett Beaulieu-Jones and Kun-Hsing Yu above work to remove or identify bias in medical artificial intelligence. Just ask AI researchers really buckled down to address the problem of bias within the past few years, say Yu and Beaulieu-Jones.

Paola Cecchi-Dimeglio uses artificial intelligence to aid organizations in their efforts to improve diversity inclusion. In , administrators realized that the National Residency Match Program was unfairly handling couples looking to complete their medical residencies together. Multiple choice or essay Accessible or opaque, simple or convoluted, the key to reducing AI bias is to test, test, test.

How good is good enough? Autumn It is the first thing people notice about us when we walk into a room. To keep your skin smooth and glowing, there are things that you can do on a daily, weekly or even monthly basis. Try one or all 18 tips for beautiful skin:.

Unless you have oily skin, you only need to use a cleanser before bed. Use mild cleanser and cleanse the skin for a full minute.

What About Oily Skin? If you have oily skin, cleanse your skin in the morning and before bed with a foaming cleanser. Stop Scrubbing. Harsh scrubbing or over-exfoliating leads to increased inflammation, causing your skin to break out and look puffy and red. Stay Hydrated. Drink at least 8 glasses of water daily, some research suggests a gallon daily, to make sure your skin stays soft and supple.

Apply sunscreen before leaving your house and reapply often during the day. Use SPF 30 or greater and use mineral powder for extra protection. Your blemishes will not go away and additional irritation may lead to more redness, swelling and possible scarring.

Include a Retinoid into your Nightly Regimen Retinoids help to boost collagen, reducing the appearance of fine lines, wrinkles and discoloration.

Foods like salmon, berries, and nuts are full of antioxidants which help decrease inflammation. Exercise Regularly. Exercising helps your body stay in shape and your skin looking beautiful. Sweating helps to get the blood circulating and accelerates the cleansing process of your entire body.

Get Plenty of Sleep.

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