When asked to explain how they arrived at a diagnosis, a good doctor can retrace their steps back to the moment the patient walked into the hospital. Sometimes they can go even further, back to the moment that brought the patient to the hospital in the first place. Diagnostics is a combination of test results, medical knowledge, and intuition (often referred to as clinical gestalt), and while your doctor may not always be right, the process of creating a diagnosis makes it possible to pinpoint the moment where the process went wrong.

However, defining this moment, and the diagnostic process in general, has presented a unique challenge for researchers developing machine learning for medicine. Developers rely on metrics like accuracy, sensitivity, and specificity to make sure that their models are performing correctly, only to encounter confusion from clinicians when they cannot explain how their model reached a particular conclusion. We saw this in 2018, when a report from cancer doctors working with an early version of IBM Watson Oncology’s software found themselves dissatisfied with Watson’s performance in the clinic and confused as to how it created cancer treatment plans. …


On Tuesday, a video of Alexandria Ocasio-Cortez discussing bias in machine learning made its way around the Twitterverse. Specifically, she said that “[Algorithms] always have these racial inequities that get translated, because algorithms are still made by human beings.”

Ocasio-Cortez is one of the first politicians to highlight on the national stage the importance of the issues of fairness and bias in the development of machine learning algorithms. The challenge of AI governance continues to stump industry and academia alike. With no long-term solutions, a technology that would historically be regulated by the government is instead regulated by peer pressure. …


In January, we saw a wide range of predictions on how AI would effect our lives in 2018, from people continuing to adopt virtual assistants (Alexa, Siri, Google Assistant, and more), to increased use of facial recognition for security, to better and more personalized media recommendations for the average person. On the other hand, we were worried about how AI would be used in healthcare, and whether decisions made using AI were always fair, or even right.

https://www.forbes.com/sites/fridapolli/2018/01/03/predictions-for-artificial-intelligence-in-2018/#24b898453bd4

https://towardsdatascience.com/artificial-intelligence-ai-in-2018-and-beyond-e06f05167f9c

https://www.cnbc.com/2018/01/05/how-artificial-intelligence-will-affect-your-life-and-work-in-2018.html

https://becominghuman.ai/where-ai-is-headed-in-2018-1f8913fd420e

In February, we started defining AI for the public. This was likely in anticipation of increased adoption of AI by the average person, as we saw in January. …


Image for post
Me, as a freshman at Cornell, during one of my first few days in my undergrad lab.

Are you interested in doing science alongside academic and industry researchers?

No, you don’t need to have a background in science. All you need to participate is to enjoy playing games.

Chances are, you would be interested. In fact, roughly 60% of Americans expressed interest in science and technology in 2015. For reference, 44% of Americans expressed interest in sports, so more Americans want to know about new and developing technology than want to know who won the last Superbowl (No, that’s probably not true, but you get the point).

Citizen science allows anyone to become involved in scientific research. In fact, it’s been around for a while, dating back to the 17th century, and has gained steam over the past several years as scientists learn to leverage the Internet and apps to reach wider audiences. Typically, citizen science allows the average person to help researchers collect and analyze data that they may not have access to otherwise. As exciting as data analysis likely sounds (Hint: It doesn’t), recent iterations of citizen science have focused on adapting data analysis techniques to online games. People have discovered protein structures by playing FoldIt, have identified new genes using Phylo, and have sped up Alzheimer’s research on Stall Catchers. …


The paper can be found here: Extraordinary plasticity of an inorganic semiconductor in darkness

*Disclaimer: I am far from a materials scientist. In fact, I’m probably closer to a layperson when it comes to inorganic materials and semiconductors. Comments and corrections are welcome. I’m always looking to learn!

Welcome to Paper of the Week!

Every week, I’m going to pick a paper that caught my eye (usually from Twitter if I’m being honest) and tell you about it!

This week’s paper is titled “Extraordinary plasticity of an inorganic semiconductor in darkness,” and it comes from a lab in the Department of Materials Physics, Nagoya University in Japan. This is definitely the first time I’ve ever seen the term “extraordinary” used in the title of a journal article, so this paper would have been a serious contender for that alone. …


The paper of the week can be found here: Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms

Note: For the first time, this paper is open source! Special thanks to arxiv.org for existing and making it possible for the average person to stay up to date on scientific research.

Coming soon to a hospital near you — The iBaby Monitor!

This week, we’re talking about baby monitoring. Specifically, on your phone. This is probably not the baby monitor that you grew up with (unless you were born in the last couple months. If you were, you should probably alert an adult to the fact that you are an infant prodigy). It is also probably not one that you would use on your current/future children unless you want to find a nursery rhyme for when you have to connect EEG electrodes to their heads. However, it is a very simple solution to a problem that most people don’t realize exists that could become a real product without much effort, which is not something that we can usually say about recently published research. …


This week’s paper can be found here: The Dermal Abyss: Interfacing with the Skin by Tattooing Biosensors

I’m on Week 2 of Massive’s Storytelling 101 class, and this week was on taking the narrative style that we learned in the first week and using it on an actual paper. I chose a paper from the MIT Media Lab, which is always coming out with some odd or interesting research. The featured image on this post is a figure from the paper (all credit goes to the authors), so I’d definitely recommend checking it out. …


This week’s paper can be found here: Adversarial Attacks Against Medical Deep Learning Systems

Healthcare costs have been increasing to the point where medical care may become unaffordable for a significant portion of the US population in the near future. Many solutions have been proposed to slow down or reverse these cost increases, including passing federal healthcare programs (such as the ACA) that make medical care more efficient to limiting medical appointments to absolute emergencies.

One of these solutions is the idea of implementing artificial intelligence into medical practice. Ideally, this would reduce time spent on menial or recurring tasks, prevent inefficiency in the healthcare system, predict chronic conditions before they become irreversible, and provide better medical care to patients across the board. …


This week’s paper can be found here: Scientists’ Ethical Obligations and Social Responsibility for Nanotechnology Research

Note: Yes, I’m late this week. Normally, I try to have things out by Wednesday, but this week got a little crazy with planning for the Student Summit (if this is your first time on my blog, WELCOME and more details on the student summit can be found here), so I’m just now getting to all the other things. Planning to get back on track this week!

Also, this is another paper that is behind a paywall. In the future, I’m going to do my best to pick papers that are open sourced, but you don’t need the paper to get the idea for this one. …


This week’s paper can be found here: A high-impedance detector-array glove for magnetic resonance imaging of the hand

Note: This paper is behind a paywall on the Nature Biomedical Engineering website. Unfortunately, this is all too common for scientific literature, making it difficult for non-scientists (or even scientists who are not from the same field) to access current research. If you do have access through your institution, I’d encourage you to check it out! If you don’t, I’d encourage you to join the discussion over open access publications that the academic community is currently having. …

About

Jordan Harrod

PhD Student at Harvard/MIT. Teaching you about AI via youtube.com/jordanharrod, Writing about Science + Policy on Medium.

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