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Interesting AI & Immune System Initiative by Microsoft and Adaptive Biotechnologies that leaves behind some questions

It is no secret that the larger technology companies including Google, Microsoft, nVidia, IBM, Apple and others want to dominate AI, as well as healthcare, in an ever expanding competitive landscape. While it is anyone’s guess if they will succeed, or get upstaged by smaller, nimbler firms in either arena, the moves they make are definitely interesting to watch. A lot of the moves appear benign, but could lead to cannibalization, such as the “AI Contests” some of the organizations put up (more posts to come on this).

Partnerships can go both ways I suppose, and are probably a strategic way to externalize any risk of failure. In that sense, in the current example, both Microsoft and Adaptive Biotechnologies appear to want to play up their strengths. The premise of what they want to do with the partnership is also quite intriguing. You can read it from the horses’ mouths in the links provided below. I will summarize them and lay out a couple of thoughts that come to my mind.

Essentially, the project would turn the body’s immune system itself into the data source for diagnosis. Because every time the immune system responds to a disease, T-Cell receptor (TCR) proteins are expressed to combat antigens. Mapping the TCRs, through a simple blood test, as Microsoft and Adaptive postulate, can go a long way in early diagnoses of an array of diseases. To say the least, the project is ambitious, and here are a few thoughts:

  1. Accurate diagnosis and personalized therapy require knowledge of the state of the human body and its disease. Simply mapping the genetics of a person, and considering their epigenetics and their lifestyles, etc. is complex enough, but it still might not be enough! Thus, TCRs could be mapped and allow for quicker diagnoses, if the theory pans out on a large-scale. It is yet unclear to me that mapped TCRs can actually yield the necessary diagnostic clues, machine learning or not, for a larger variety of diseases. However, it might supplement diagnostic efforts alongside genetics, epigenetics and other health data sources.
  2. From a business angle, I also find this to be intriguingly different from the general bedlam of text processing a la Watson, and all the algorithms rushing to read and reinterpret imaging as with nVidia and others. Microsoft has appeared to have looked for and found a partner with a unique approach to the application of machine learning in healthcare.
  3. Any large set of unknown targets, powered by data might appear to be a classic problem for machine learning to solve. However, Microsoft and Adaptive (Microsoft has invested in them now, apparently) might have joined a Kool-Aid club that bridles the horses behind carts. What I mean when I array out those cliches is this: medicine already has a problem of knowledge paucity when it comes to diagnoses, until more clarity becomes available, by way of a progressed disease. This is fundamentally because disease precursors are poorly understood, from want of clinical research, not lack of intelligence. What is to say the TCRs won’t just set off an array of false and confounding alarms? Yes, with liquid biopsy and other such hyped up methods out there, the industry is in a rush for quick fixes. It might well turn out that this is much harder to resolve, with clinical studies and protocols that will require to demonstrate that TCR expression, their proportional presence, etc., do truly indicate the preliminary stages of a disease being present in the body. I am not convinced yet.
  4. There is also a maddening rush out the gate to define universal tests with pinpricks of blood. While I am not suggesting we practice good old, barbaric bloodletting (although there are all kinds of people out there “thirsting” for a comeback to this practice), I think this is an unnecessarily over-constrained problem definition, perhaps making titillating fodder for press releases and blog posts. There might also be an urge to combine these pinprick tests with diabetes monitoring and such. While it is tempting to fantasize about such possibilities, and at some point, these might come to fruition, there is no need to go to such extremes before solving fundamental problems in medicineaccurate diagnosis and targeted therapy. For example, when should a person’s blood be drawn? How frequently? Would the frequency vary when a certain set of TCRs are observed? There are so many things to worry about here. I would think companies would stop using overly broad terms such as “universal”.
  5. In my posts here, in my talks, daily discussions and so on, I always come back to a few bugaboos. Who will own the TCR mappings? Who owns the product of the machine learning algorithms? Will they be patented and bridled off? How will such diagnostic methods be regulated? Validated? Will Explainable AI, something I expect to be a fundamental principle that should be applied to healthcare be required (see explanation from DARPA linked below) and used judiciously? And on and on we go.
  6. Data has been walled off quite well in the healthcare industry up to this point. Yes, we got the human genome, but much much more sits behind curtains and masks and other cliches you can think off, that every new technology that promises to expose and dig through data always concerns me, surrounding ownership.
  7. The “Theranos” Effect: If you are like me, and know about the story of Theranos, you are still sitting up at nights, jaws dropped, wondering how in the hell, the company is still in vogue (I have written about this on my medical devices blog, in fact, using the same Pixabay image! See link below). I have also linked, one of several dozen well-written write ups that offer us a tale of caution, and I plan to call this, as I have named it, the “Theranos” Effect. In summary, this company went into the “over promise, and extreme under-delivery” (or never delivery, to date..) business. They engaged in egregious and unethical business practices, fooling the industry, investors, partners and more, along the way. How do we make sure that with all the promise of AI, companies don’t make such ugly incidents repeat themselves? Mind you, this is not me pointing fingers at Microsoft. I think this is indeed a great effort. I am just offering this up as an important tale of caution, for people in healthcare, and in any industry for that matter. I understand, as much as anyone else that businesses need hype to push their products. However, it would behoove you to make sure you don’t push things off a cliff…


In mankind’s march towards the goal of a healthy future for all, we have many strides to make. Naturally, we want to be as accurate and as thorough, yet economic as we can. Therefore, we rely on technological breakthroughs on one end, such as anything ranging from improvements in basic science, to sensors and AI, and on the economics of lower thresholds for test materials consumed, time to diagnosis and other aspects on the other end. What Microsoft and Adaptive aim to do with their (investment based) symbiotic looking partnership is commendable. It may take us one step closer to our goal, but it may not be the one to take us there at all. Only time can tell, and in the meanwhile, I hope commonsense and ethics prevail over hype and fantastic marketing materials.

Should you have something to add, please leave a comment below.

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  1. The Microsoft Blog Post:
  2. Adaptive’s Press Release:
  3. DARPA, on Explainable AI:
  4. Vanity Fair on Theranos:
  5. Myself, writing with incredulity on Theranos’s longevity:
  6. Image, Courtesy, Pexels+Pixabay:

Machine Learning shows promise in Dementia prediction

Rubik's Cube

I was flipping through some archives, and found this Scope (a Stanford University, School of Medicine Publication) article that delineates a machine learning tool (link below, Scope calls it AI, the authors of the tool, in their paper, also linked below, rightfully categorize it as ML, a subset of AI as we generally describe it). I always love when you have access to the paper linked to a study. It always makes things easy.

So, the folks at McGill, trained an ML system using PET scans from people who demonstrate symptoms of mild cognitive impairment, to see who among them would develop Alzheimer’s, given that not all of them do. They taught the system to focus on the elevated protein expression in specific brain regions to train and make predictions.

Used on an independent set, the tool had an 84% prediction accuracy of dementia progression. Read more in the paper. I want to share a few thoughts below.

  1. I think tools like this will become the norm over time. However, right now, they lack the kind of standardization and maturity required for integration into clinical practice. I don’t mean to state that in a negative sense. Such efforts take time, effort and funding, of course.
  2. An 84% percent efficiency is not enough, not even for a supporting tool, not even when humans are completely in charge. This is also achieved through training with large data sets, the use of better algorithms and other improvement methodologies. This could also use some standardization, that can then be spread to all ML, DL and AI tools, which use imaging for diagnostics in healthcare.
  3. The future should consist of such tools passively (and when necessary, actively) siphoning your imaging and other data off your EHRs, and then parsing them to see if predictions can be made. This however, requires more groups such as the ADNI (Alzheimer’s Disease Neuroimaging Initiative), from whose participants the imaging and other data was used, collaborations from hospitals, insurance companies and governments.
  4. To improve diagnosis across ages, sexes, races and other discriminating factors, global co-operation would be required.
  5. Of course, we need to take various types of data, ranging from imaging to genetics, to epigenetics and other sources to make diagnosis quite efficient. Perhaps, this combination is one way to get around the 84% efficiency in this tool, till a time comes when imaging alone produces better results. At that point, say you make predictions based on imaging, genetics, lifestyle and other factors, and they all chime in. You can probably use whatever interventions are available (this is a key factor, missing in all the hype about machine learning. You learn something, yes, but what do you DO?) to delay, treat and cure patients.

When I find more such interesting studies, I will share similar and other thoughts on Machine Learning, Deep Learning and AI, and their impact on Healthcare.

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Did you enjoy this post? Please subscribe for more updates, using the sidebar. Have ideas or blog posts you’d like to see here? Contact me at yamanoor at gmail dot com.


  1. The Stanford SCOPE Article:
  2. The McGill Paper:
  3. Image courtesy, Pexels: