Health Tech Hub Vol. 20 — Your Weekly Reads in Medicine and Health Tech

1. Understanding the possibility of global universal health care — What would the world look like if everyone had free access to healthcare? A comprehensive look at the history, benefits, and barriers to global universal healthcare. A dramatic shift in resources and mindset changes will need to take place in order to make global universal healthcare a more realistic possibility.

The promise and peril of universal health care

2. Meat Consumption, health, and the environment — Meat produces more emissions per unit of energy compared with that of plant-based foods because energy is lost at each trophic level. Meat consumption has been steadily increasing resulting in increased colorectal, cardiovascular disease, and environmental consequences.

More interestingly to me, this article discusses the idea of shaping consumer-demand.

How do we affect conscious and reflective-decision making so that consumers can make better choices for themselves? This is a cultural question that can be asked about many of the lifestyle choices that are made at a macro level. Is it the government’s responsibility to intervene by introducing fiscal interventions and certification programs? Or is it society’s responsibility to create labeling schemes and other health norms to keep each other healthy?

My guess is that it will take a little bit of both.

Meat consumption, health, and the environment

3. First-of-its-Kind AI Tool for Diabetic Retinopathy Detection Approved by FDA —

The FDA today approved what it’s calling “the first medical device to use artificial intelligence to detect greater than a mild level of the eye disease diabetic retinopathy.” The AI-powered, cloud-based system will be available for use by primary care providers.

“The healthcare system desperately needs a more efficient and cost-effective way to detect diabetic retinopathy. Too many patients go blind needlessly because they aren’t diagnosed in time”

I can foresee AI becoming more heavily used in prevention medicine going forward. Those familiar with AI know that there are still vasts improvements to be made in current models including newer tools like GANS (Generative Adversarial Neural Networks). Using AI as a preventive tool against diabetes-related blindness is a great first step.

First-of-its-Kind AI Tool for Diabetic Retinopathy Detection Approved by FDA

Bonus: Here is a video on General Adversarial Neural Networks (GANS) — A machine learning tool pioneered by Stanford’s/Google’s Ian Goodfellow that will likely gain traction move forward.

Previously, neural networks used in machine learning could only base outputs from the simulations/inputs from one model. However, with GANS, model algorithms can “compete” with each other to come up with the most correct solutions for more nuanced problems that have less obvious solutions. This is analogous to how humans debate with each other to come up with the best answer.