1001 Entrepreneurial Tales: Relational AND Transactional Business Of Nuclear Medicine…
This is a true story about my KEE (Knowledge, Experience, and Expertise) to unlocking the use of Artificial Intelligence in Healthcare, and particularly, in Nuclear Medicine.
But it also taught me the importance of building both: Relational AND Transactional foundations to a successful business. And since it is a part of my 1001 Entrepreneurial Tales — you can also find it on my YouTube channel at: https://youtu.be/BeFJpKo3umE
The Shortest AI KISS Principle That You Can Take To The Bank…
What I learned from working on nuclear medicine Computer Tomography (CT) application — is how suitable neural networks are for processing huge amounts of healthcare data.
Without any prior medical training, we could quickly extract a huge number of unstructured input parameters — and train the system to recognize heart disease: myocardial perfusion.
But in the end, we still had to ask the doctors for confirmations. And yes, the doctors had to properly label healthy vs. unhealthy hearts, too.
Putting It All Together In The Right Context…
Steve Blank made it abundantly clear years ago: “all startups are essentially a temporary organization formed to search for a repeatable and scalable business model” — and my AI startup International Neural Machines (INM) wasn’t any different.
By 1996, we developed and successfully tested our pattern recognition platform on images and speech, as well as tons of financial data. Encouraged by initial successes in detecting Visa fraud and money laundering — we yearned for an opportunity to work on advanced medical diagnosis. This is how I met, Nathan Hermony, the head of Elscint’s nuclear medicine division.
Welcome To The Brave New World Of Nuclear Medicine…
Elscint Ltd. was at that time a world leader in advanced medical imaging equipment. They successfully competed against such industry giants as General Electric, Siemens, and Philips. Little did I know, that I’m also about to receive an introduction to the cut-throat world of Nuclear Medicine!
Nuclear medicine is a medical specialty that uses radioactive tracers to assess bodily functions and to diagnose and treat disease. Specially designed cameras allow doctors to track the path of these radioactive tracers.
Not All PETs Are Created Equal
Single Photon Emission Computed Tomography or (SPECT) and Positron Emission Tomography or (PET) scans — are the two most common imaging modalities in nuclear medicine. And Nathan had a serious problem. Historically, Elscint excelled in SPECT imaging. At the time we met, PET appeared to be eclipsing SPECT for cardiac imaging and oncology.
Toronto Hospitals
The success of PET was attributed to its inherently better image resolution. In cardiac scanning, for example, it has generally been reported that PET offers a resolution of 5 to 7 mm, compared with a cardiac SPECT resolution of 12 to 15 mm.
Elscint’s revenues exceeded $300MM+ and the company was negotiating multimillion-dollar sales with several hospitals in Ontario, Canada. INM’s advanced pattern recognition capabilities could have become the last straw that breaks the camel’s back.
Merit Selling…
Well, I was so proud of landing a nuclear medicine client — that I offered Elscint my help in arranging the meetings with hospital officials, and… sell, sell, sell!
Nathan was very pleased with my offer, but instead of rubbing his hands — he began to sweat, profusely. Soon, I was about to discover his predicaments.
Selling to Mount Sinai Hospital…
It took approximately 4 weeks, before my neural network presentation took place at Mount Sinai Hospital, in Toronto. The top-notch myocardial perfusion (heart disease) specialists, hospital administrators, and IT personnel came to the auditorium.
They were genuinely intrigued and impressed — by the possibilities of deep learning technologies. A few days later, as INM was setting up its training database, I received an urgent phone call from Nathan.
Selling To The Western Hospital…
Apparently, a much larger RFP has surfaced from The Western Hospital in Toronto — and this was it! Elscint simply had to win this bid! And Mount Sinai Hospital — could wait.
I met with Dr. Burns at The Western Hospital, immediately. He was one of the most respected cardiac specialists around the world — and one of the most enthusiastic researchers I ever met.
Not only did he help me understand the extent of the available database — I also learned a great deal about some of the critical factors leading to a successful diagnosis.
Selling To The Ottawa Heart Institute…
A few weeks passed, the data was received, and … you guessed it: Nathan calls again! This time, we were asked to work with the Ottawa Heart Institute at Ottawa’s Civic Hospital. And right now, this was THE TOP PRIORITY for Elscint.
As they say: “fool me once, shame on you, fool me twice, shame on me” — so I wasn’t going to turn myself into a pretzel, and blindly comply. Instead of rushing to Ottawa, I asked Nathan: “what is the root cause for such an erratic behavior?”
The Root Cause Unveiled…
After a while, it became clear to me: Elscint was dashing to close THE BIGGEST DEAL before the next show of Radiological Society of North America (RSNA). Some of Elscint’s competitors were expected to introduce new equipment at the show — with better features than Elscint could offer that year.
Granted, it took some convincing to do, but after a few heated exchanges — Elscint finally agreed to proceed as planned and to stop such a ridiculous “client-hopping”. And we agreed to give neural networks a well-deserved try — without further delays…
And The Winner Is…
3 months later, INM took the podium at one of the nuclear medicine users group conference — organized by Elscint. Just before our presentation, a medical team from Kansas presented their findings linked to a myocardial perfusion analysis.
They used conventional statistical methods and reported a detection accuracy of 62%. We were next. Using the same SPECT machines, INM reached 92% of average accuracy — over 10 different tests. All the results were neatly tabulated, and our neural networks processed 10x as many input variables, as the Kansas team did…
Nothing Lasts Forever…
Needless to say — Nathan was all smiling! He never requested INM to pitch in front of another hospital, again. Six months later, on Nov 25, 1998 — Elscint was successfully acquired by GE Healthcare.
Lessons learned…
What I learned from that experience, was how suitable neural networks are for processing huge amounts of healthcare data. Without any prior medical training, we could quickly extract a huge number of unstructured input parameters — and train the system to recognize the disease.
All we asked the doctors for — was to properly label healthy vs. unhealthy hearts. At some point, during the training phase, our neural networks plateaued — and reached the best generalization level they could.
No amount of additional training improved the overall accuracy. In fact, one should be extremely careful to prevent overtraining of neural networks on training sets. This could cause performance degradation on unseen data.
But remember this: no matter how important is the financial transaction, never lose sight of the fact that even the most hard-earned rapports can be destroyed with a drop of the hat…
All professional relationships must be never lopsided and should be handled with both: a high degree of Competence AND Integrity
Deep Learning Is Not Perfect
Yes, deep learning is not perfect, but it’s extremely fast and efficient. And it can help medical staff process more patients — much faster. However, since recognition accuracy is not 100% — a final decision should be ALWAYS reserved for a qualified medical practitioner…
My own experience is reflective of what transpired in using AI to improve patient care over the last 20 years. Imaging applications aimed at improving the diagnosis of cancer, or heart problems — are now quite common.
Telemedicine Rules…
I mentioned the SPECT application, but similar developments take place in other modalities such as X-rays, CT scans, MRIs, and echocardiograms — not to mention the latest trends and investments in Telemedicine.
To be successful, AI requires huge amounts of high-quality, labeled data. It can’t replace a human being yet — but it can be trained to understand vast amounts of data and alert the experts to abnormal findings. It’s a great help to physicians making the right and the more accurate decisions.
My Recommendations…
Don’t be blinded by the latest singularity headlines. It’s important to understand, though — that even the best AI tools are not going to replace clinicians. Nowadays AI technologies are great at aiding and helping clinicians to zero in on many signals hidden in massive amounts of data. Deep Learning has only reached the “rat-level” cognition and it is not enough to replicate a physician.
I’m not so sure about your preferences, but I’m not so keen to walk into a clinic and be diagnosed by a “rat-brain”. Especially, how do you even sue an artificial rat if things go bad?
For More Information…
For more information, please see my posts on LinkedIn, Twitter, Medium, and CGE’s website.
AI Boogeyman
You can also find additional info in my book on amazon: “AI Boogeyman — Dispelling Fake News About Job Losses”, and on our YouTube Studio channel… Thank you.
I offer hands-on AI investment advice to Venture Capital & Private Equity portfolio companies. Hence, I am in the business of joining Advisory Boards (ABs). And in many cases, I deliver results in 90 days by structuring JVs to bring untapped revenue streams — just as I did w/ Verizon 20-yrs ago…
A good Advisory Board improves the decision-making process by helping the CEOs to consider different perspectives. More importantly, there is not a single top athlete who does not have a coach. And yet, only 20% of CEOs are coached by Advisors. The other 80% — may never become the top performers they could have been. Thus, my advice to CEOs: “Amateurs don’t use coaches, professionals do… and so should you”.
I used advanced BusinessAI™ strategies in Renewable Energy for 12 yrs. Now, I help VC/CVC/PE funds to maximize their returns in Healthcare, Fintech, Transportation, Construction & Manufacturing, too. And I apply the same structured finance expertise I acquired through financing over $1B of Renewable Energy projects.
As a 30-yr BusinessAI™ veteran, I Propose, Design, Structure, Finance, and Deploy state-of-the-art Joint Ventures to bring RAPID & SUSTAINABLE REVENUES. And as a coach & mentor, I bring business savvy to separate the wheat from the chaff — through a unique process to beat the odds. Such pattern recognition abilities allow me to see what is still missing & how to maximize business offerings & profitability…
What I learned over the years is that it is not just technology innovation, but also the exponential increase in the value offered to clients at a much lower cost — that makes all the difference. Business Model Innovation is as disruptive as Technology Innovation and yet I see too many companies focused on pushing their product out the door — while losing ~70% of untapped revenue streams.
My LeanBOD™ recommendation? Mandate CEO Advisory Boards and create a pool of viable Co-CEOs to be chosen by the BODs. Co-CEOs offer the fastest way to accelerate Scale-Up & Expansion, Revenue Growth, Margin Enhancement & Opening New Channels.
SELECT ACCOMPLISHMENTS: Using AI in CT medical diagnostic, financial fraud detection, solar PV, wind, WTE, energy efficiency, etc. Finance skills: equity, non-recourse debt, balance sheet financing, and tax equity. I also took a tiny startup public, building a $135MM enterprise & received funding from NRC & DND. Academic R&D collaborations included: UW, UofG, UofT, and MCC Consortium in Texas.
