Radiology Residents and Machine Learning – 6 quick tips and links

There are many moving parts and there is a lot of information passing before our eyes. This is my version of a summary snapshot as we move into Summer 2018. What did I forget? Please add your thoughts below.

6 quick tips, links and areas to consider as we move forward.

1. Pay attention to Drs. Dreyer and Michalski and their team at MGH & BWH CENTER FOR CLINICAL DATA SCIENCE

“Radiologists will not be replaced by ML, however radiologist who don’t use ML may be replaced by those who do.” Mark Michalski and Keith Dreyer

2. Get a sense of what’s out there.

There were 30 ML/AI companies at RSNA last year and Dr. Harvey has done a nice job curating the list:

The A-Z Guide to Radiology AI Companies

Per MIT, there are 130 companies working on AI and healthcare in China

3. See where the ML vendors are working to add to our current workflow. Some examples: Carestream, Fuji and IBM.

DEFINING ARTIFICIAL INTELLIGENCE, DEEP LEARNING, AND MACHINE LEARNING IN DIAGNOSTIC IMAGING – Carestream

Fujifilm Showcases Enterprise Imaging Portfolio and AI Initiative

TriHealth hospitals pay $10 million to adopt IBM Watson Health enterprise imaging

4. Take an intro ML class online – for free.

Coursera

Udacity

Get a sense of how computer scientists think. We are not so different but there are clinical things that they just don’t know. And that brings us to:

5. Own this paradigm shift.

Once we have an idea of how developers think, we can offer helpful feedback.

“I love my EMR” said no one ever. Let’s be involved in this tech shift. When your institution adopts an algorithm, insist on a system to provide user feedback.

There are so many areas to improve within radiology and between radiology and our clinical counterparts. Be creative. Think big.

6. Subscribe to a few newsletters with AI and ML stories, healthcare and others:

CognitionX

CBInsights

We are at the very beginning of this new time in radiology and quite frankly you could probably navigate the rest of your career and avoid any significant change.

But where is the fun in that?

 

Morning Routine – 3 Years Later

March 18, 2015 was the first day I started my daily morning routine and published an article on that in October of that year.

Answering the phone at work has been a noticeable change.

At work today, listen to how others and you answer the phone. I used to think answering the phone was not my real job. I was trained to be a radiologist. And during that training, any time the phone rang it was a distraction.

But during residency I noticed that no matter how curt I was on the phone, it kept ringing. As a staff I decided to at least be nicer, but it still frustrated me.

The phone rang. I got frustrated.

I can’t predict when the phone will ring, but I can choose my reaction.

Over the past 3 years of making time for myself each morning, I have been able to put a little space between things I cannot predict and my reaction.

Now the phone rings. I take a slight pause and answer like a calm professional (most of the time).

Of course my ringing phone is almost always a fellow provider looking for help. I make time to help and I am happy to do it (most of the time).

You’ve made it this far in your career so you know you can google lots of resources, this one is a decent start. 10percenthappier.com No affiliation to me.

RSNA and ML: 3 Big Questions

There is no doubt image recognition will be an increasing aspect of our radiology practice. Now what? In addition to the obvious next question on everyone’s mind – how can we use it? here are 3 more.

1. How can we better predict which patients need scanning? Will it be clinical decision support? Will new legislation help or hurt? Can we use the rest of the clinical, social, genomic and family history to better care for patients overall, to included proper imaging?

2. With that, is there a system to support ordering providers, on not just imaging utilization, but working with lab, blood, pharmacy, physical therapy and the many other teammates standing by to help with patient care. Can we decrease variability, improve accuracy and offer more value to the patient and the system?

3. Who is going to carefully follow incidental findings? Does the average radiologist know who is supposed to come in next month for their follow-up scan? Should this duty be taken on by radiology? Can we borrow systems from our colleagues in sales and make a Patient Relationship Management platform like their CRM (Customer)? If ML algorithms are expected to find more than I can, how can we prep for this now?

There will be many RSNA updates over the next week, I’ll be following to see who is thinking about radiology fitting in the bigger picture.

Have fun in Chicago and hope to see you there next year.

Three questions as we craft our ML/radiology future

As we watch the medical imaging future unfold, there are reasonable questions to consider.

As a radiologist, I am very excited. I welcome the the upcoming changes and new tools being developed as we speak. While the computer science and data analytics experts create these tools, I challenge my colleagues to create a framework that best utilize these tools and elevate the standard of care.

A machine learning algorithm can find things that I cannot. This is happening right now and it is impressive. My first question is, what if the study has no findings? How could we avoid an unnecessary study to begin with? The financial, time and possible radiation cost could be avoided. Can we apply deep learning algorithms to clinical scenarios and avoid the CT radiation to a 11 year boy with belly pain for a possible appendicitis and ultimately has enteritis?

In a similar vein, why does a common emergent CT scan start at the diaphragm and end at the symphysis pubis? Over the years we have found if the patient complains of pain from ribs down, we’ll probably find something in there. Can we tailor the exam? If there truly is pain at McBurney’s point in the right lower quadrant, can we scan a smaller area? For younger patients the radiation dose savings can add up.

Finally, when these systems become so efficient at finding masses, nodules, cysts, focal thickening and small fluid collections, how do we follow them up? We have a decent amount of them now and the system is inefficient at best and non-existent at worst. Who will lean forward to this space? Radiologists, primary care or our computer scientists and data analyst colleagues?

Let’s create the framework to expect nodules and carefully track them. I for one want to avoid a situation where a family member is diagnosed with advanced lung cancer only to see a small nodule on the edge of an abdomen CT scan which included the lung bases a year ago.

These are reasonable short term goals. The horizon is broad and to tell you the truth, I am looking forward to the next, next thing.

Clinical Cofounder

We can provide insightful questions for the machine learning engineer to answer; provide supervised learning guidance throughout the process as well.

Like the technical cofounder, the clinical cofounder wants to create a product or service and provide value.

Medical training however can be as much a hindrance as it helps.

In medical school, and subsequent residency, we are drilled on careful attention to detail for each patient. Unless you are in a leadership role, public health or preventive medicine, you rarely think of treating a group of people.

This is the opposite of scalable and this is the opposite of value added to a growing company, mostly.

By using skills learned while caring for one patient at a time, the forward thinking clinicians can lift his or her head above the noise to see trends. We can identify pain points in our workflow and recognize them in our colleagues. We can make changes in workflow and assess efficiency. We can measure diagnosis per patients per day.

These front line observations are critical for a founding team to create the first version of a product that clinicians will find valuable.

The medical tools created and given to us can be flexed, and broken if needed. Electronic health records can be compared to banking, travel or social user experiences. Why can Expedia predict where I am going but the radiology system cannot? This juxtaposition is how a clinician can help the user experience developer tailor an intuitive interface.

Physicians are great at continuing education but the majority are not great at, or care to, broaden their knowledge base. We live in a time where on-demand classes exist to learn about machine learning and cognitive neural networks. Not that we would perform these function but in the context of the founding team we can converse efficiently with the technical team to best create a system.

If my family member needs care today, the physician who couldn’t care less about all of this is the right person for us.

When we need care in 5 years, I am betting on significant improvement in healthcare delivery as a result of the clinical cofounder.