Treating to the Bell Curve


2 min read
25 Feb
25Feb

Most, if not all, of the evidence-based treatment guidelines in medicine are applicable to the majority of people – the people who live in the middle of a bell curve.  The 60-70% in the middle.  Most treatments do not address the outliers – the patients who responded when no one else did or the patients who never got better when others did.  We all know about examples like this.  Current medicine treats within the bell curve.  

As a patient, I’m looking for something more.  I want medicine to be personal to me.

Artificial intelligence (AI) is present in my daily life.  AI is an algorithm that has the capability to think like a human.  Whether it be the movies that Netflix recommends I watch, Amazon’s product recommendations for me, Alexa’s ability to order my groceries or send me reminders, or the way in which my Google searches are automatically filled in based on my prior searches.  In each of these situations there is an algorithm(s) running in the background gathering information about me and my preferences in order to make my life more personally convenient.

AI is in the early stages of its applications to breast cancer and I can’t wait until it is the new evidence-basis for personalized prediction, detection, and treatment of breast cancer.  

When I received my diagnosis of breast cancer in December 2019, my work and personal life collided.  As I waited for a single radiologist to read my mammogram, I found myself wishing that Kheiron Medical Technology’s Mia model was in use as a second reader.  My dependency on the radiologist to get it right was nerve wracking.  I would have preferred that Mia, who had read millions of mammograms and was highly accurate in detecting breast cancer, was looking over his shoulder when he read my mammogram.  But since Mia was not a part of my screening process, I was dependent upon a radiologist to have gotten it right.  I wondered how many tiny masses were undetected in women until the cancer was slightly larger and therefore more severe. 

Following my primary treatment, I began meeting with my medical oncologist for the estrogen blocking medication.  While there are evidence-based guidelines for the secondary treatment of my estrogen-receptor-positive (ER+) cancer, the application of the guideline is hit or miss.  The first medication left me brain-fogged, lethargic, and down.  Dreading what the second medication would do to my body, I procrastinated for weeks before starting to take it.  My medical oncologist suggested an every-other-day dose, stating that some women have to try all four medications in varying intervals or doses before they find one that does not impact their quality of life.  Every conversation of this type left me frustrated that medicine had not yet applied AI to uncover what precisely works for each woman.  

Last week I met with the CEO of a new start up, Clairity, who is leveraging the research of Dr. Connie Lehman, Director of Breast Imaging & Co-Director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital.  Clairity is working on personalizing the assessment of women’s risk for developing breast cancer.  Once again, I wished that their solution was in use in my screening program.  According to the current risk models, I should not have developed breast cancer.  Their AI solution will help to identify which women will benefit from earlier or more frequent screening, which will result in finding cancer earlier.  I just happen to be lucky that mine was found early – but women shouldn’t have to rely on luck when it comes to cancer detection. 

In my work life, I see the promise of AI in breast health and I am excited about what lies ahead. However, my current experience is one of life within the bell curve.

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