Artificial intelligence is the hottest topic in medical informatics. The promises of an intelligent automation in medicine’s future are equal parts optimism, hype, and fear.
In this post, Mike Hearn struggles to reconcile the paradox surrounding the supposedly objective, data-driven approaches to AI and the incredibly opinion-charged, ultra-political world from which AI draws its data source.
The post focuses on broader applications, but in medicine, a similar problem exists. If AI is expected to extract insight from the text of original research articles, statistical analyses, and systematic reviews, its “insights” are marred by human biases.
The difference, of course, is that AI may bury such biases into a machine learning black box. We have an increasing body of research on latent human biases, but machine biases are much harder to discover, particularly when it reflects the inherent biases in the data from which it draws its conclusions. Our own biases.
AI acts as a mirror. Sometimes we don’t like the face staring back at us.
Source: The most dangerous AI – Mike’s blog
This month’s Harvard Business Review has an article highlighting one of the most fascinating emerging trends in quality improvement: that a “root cause” exists may be a myth. As healthcare QI/QA moves towards eliminating errors and improving metric-based performance, the increasing obsession towards solving a quality problem is laudable but sometimes misguided.
This excellent HBR article focuses on reframing. In short, what you say after discovering a complex problem is important. Before saying “Let’s start making a pareto chart and collect some data!” try inserting a 30-second pause with, “Is that the right problem we should be solving?”
Without spoiling the fun of reading the article, try thinking through this issue before reading – You have received multiple complaints about the speed of your building’s elevators. How would you address this problem?
In fact, the very idea that a single root problem exists may be misleading; problems are typically multicausal and can be addressed in many ways.
Source: Are You Solving the Right Problems?
If you have been paying attention to data science in healthcare you will have noticed the gradual shift from 2016’s Big Data to 2017’s Machine Learning. Specifically, deep learning techniques attract much of the attention. The FDA recently approved the use of deep learning techniques in cardiac diagnoses. Enlitic promises to automate the process of radiologic diagnosis for medical imaging. And with the advent of wearables, there is an ever-increasing volume of health data that requires “smart” algorithms to parse out the signal from the noise. Continue reading
A Quora answer/article about data science.
Incidentally, the same 5 skills are also highly relevant to be a physician-informatician, particularly in radiology. Give it a read.
Source: William Chen’s answer to What are the top 5 skills needed to become a data scientist? – Quora
There is something strangely satisfying about being able to take things apart and putting it back together. Inspired by the popularity of Lego sets in our childhoods, Minecraft brought this sense of wonder to video games.
For those of us who are life-long tinkerers who happen to be radiologists, I published in Radiology Data Quest a DIY on how one take DICOM apart and manipulate it. All in Python, no less.
DICOM is a pain in the neck. It also happens to be very helpful. As clinical radiologists, we expect post-processing, even taking them for granted. However, the magic that occurs behind the scene…
Source: DICOM Processing and Segmentation in Python – Radiology Data Quest
Because of its unique combination of data, science, and machines and the interactions with patients’ lives, innovation in the industry requires much more than cool new gadgets or one-off apps that don’t get to the heart of challenges around cost, quality, and access.”
Source: Doctors, data, and diseases: How AI is transforming health care | VentureBeat | Bots | by Charles Koontz, GE Healthcare
As we welcome 2017, it’s time to sum up the key developments in data science and machine learning from the past year so that we can open our eyes to the new year. Here is what you missed …
Source: Data Science and Machine Learning Developments in 2016 – Radiology Data Quest