Bench Press

When I heard that GE’s CEO and Chairman Jeffrey Immelt was going to be at this year’s Web 2.0 Summit, I expected an “old business” CEO weakly touting all the ways that his company was embracing social media. I did not expect him to come to demonstrate a portable ultrasound device ‘with the works’:

GE’s new Vscan pocket ultrasound device is reminiscent of the mobile phone-powered portable ultrasound and light microscope that we’ve covered before, but while those mobile phone attachments felt more like demonstrations of mobile phone/medical technology mashup curiosities, the Vscan feels like its in an entirely separate category:

• pushed by a major healthcare technology company
• fits in the palm of your hand
• thumb operated UI to adjust gain or look at a color-doppler scan
• high quality display
• real-time imaging capability
• preset modes to fit what doctors are most likely to use
• support for WiFi transmission of information
• ability to annotate with voice recorder
• Immelt: “could be the Stethoscope of the 21st century”

Medical technology blog Medgadget captured a fascinating preview on YouTube:

In his presentation at the Web 2.0 Summit, Immelt captures what I perceive to be the real significance behind the VScan:

“This has the same power and image quality of an ultrasound from 2-3 years ago that cost $250,000! This is Moore’s Law in action. To get this image scale in 1995, you had a product that weighed several hundred pounds!”

On a medical level, this opens up new doors for physicians to study illnesses and treat patients in a wider range of regions, but even beyond that, it underscores the ability of technological innovation to

increase the ability of doctors, scientists, consumers, and patients all over the world to access the latest in scientific and medical technology.

(Image credit – Immelt and Vscan from GE website)

Although digital modeling may eventually lead to the creation of the Matrix, I think it's safe to take our chances.

The digital age is upon us! SLR cameras are now DSLRs, VHS tapes are now Blu-ray discs, CRT monitors are now OLED screens, and every two years, our chips double in computational power (as predicted by Moore’s Law). With all these rapid advancements in technology, I believe the next step will be a dramatic increase in the use of computer modeling.

We see the signs everywhere. More and more, people are relying on computers to help them gather and process information. But why stop there? Why not have computers be the primary engine of design and testing? Obviously it’d be remiss to ignore the human factor in completing any project, at least until the apocalypse foretold by the Matrix or the Terminator series comes true. However, I’m convinced that the more we incorporate computer programs into our decisions and our design flows, the better, for three basic reasons:

1. Objectivity: To quote Dr. Gregory House, “people always lie.” I don’t mean this in a cynical way, but humans “lie” even when they don’t intend to. We’ve all heard of the placebo affect and how manipulable and inconsistent human memory is. Computers, on the other hand, aren’t designed to lie or to be irrational. Give a computer program an input, and you can rest assured that it will faithfully return the output it calculates is best. No matter what the circumstance, you can rely on computer programs to provide unbiased, objective results.
2. Consistency: Try shooting 1000 free throws in a row. Impossible? Maybe, for a human (unless you’re Steve Nash). However, program a robot to shoot a basketball the same way, with the same stroke, and the same force, and you’re looking at a free-throw shooting machine (literally). Computers only know how to do what they’re programmed to do. Given an algorithm, a computer will duplicate it consistently, no matter how many times the user asks.
3. Speed: How fast can you calculate the roots of the polynomial 23x³ + 17x² – 31x + 71? With advancements in transistor speed and computing algorithm, the complexity of problems a computer can solve (and the speed at which they can be solved) is astounding — and growing. Some calculations that are considered intractable for humans are done in seconds on a computer.

These three points underscore my main argument: we shouldn’t use computers only when it’s necessary, but whenever it’s possible. Computers are fast, efficient, and reliable. With careful ingenuity and good programming, there’s no limit to what computers can accomplish. That’s why, to me, the ability to model things digitally is such an appealing prospect. The benefits include:

1. Thorough testing: As a computer scientist myself, I can attest to the fact that there is no such thing as too much testing, and especially when it comes to safety concerns. Using computers, builders of things ranging from automobiles to buildings will be able to run many tests on a product or a feature before release and, potentially, before a single tool is touched. This enhances our ability to design effective and safe products.
2. Lower costs: Instead of spending millions on supplies and materials for multiple physical prototypes, virtual trials will enable cheap testing of new designs and features. Obviously, physical trials cannot (and should not) be omitted, but digital models which are intrinsically easily re-tested and modified, are clearly the cost-effective approach to designing/testing/tweaking. Ask yourself what’s cheaper: designing four prototypes to test four possible features, or running four computer models of the different features and building out only two prototypes with the two best features?
3. Better understanding of design: Some scientists joke that engineers can only understand things that they can take apart and put it back together. While that subject is worth a post on its own, it’s hard to argue that one level of understanding comes from being able to model. Comparing these computer models to reality and seeing the differences, then, allows the engineer/designer to test their understanding. A product which succeeds on the computer monitor but fails in reality suggests that there is some aspect of the design that isn’t being modeled correctly — and that insight not only furthers scientific understanding, but can also help designers quickly design the next product or feature.

This is the power of virtual modeling. Using our computers to do the dirty work while we reap the benefits of inexpensive and efficient computing. We’ve already seen glimpses of this happening, and I believe that these trends will not only continue, but are good things for us to embrace. Imagine the FDA requiring drug trials to be first run in a computer model: testing its efficacy on a simulation of a human body. Imagine regulations requiring architects and contractors to digitally test the earthquake safety of a building before they even lay the first beam. The possibilities, and possible benefits, are endless.

To reach that end stage, I see several things needing to happen:

1. Greater emphasis on developing good mathematical models: In order to model things rigorously on a computer, we need a solid understanding of how things work, and we need to ingrain the “art” of computer modeling into a new generation of designers and engineers. Computer models should not be a luxury that only specialty shops engage in, they should be the norm, and something every architect, engineer, and product designer is aware of.
2. Developing better computing technologies: Although processors today are operating at unprecedented speeds, in order to realize large-scale digital modeling, we still need our computers to be faster and more efficient. This may mean designing faster computer chips (or using GPUs and/or gaming processors) or looking into innovative new technologies such as distributed computing.
3. Developing/sharing numerical computing algorithms: While faster computers means being able to physically process more data, this is only half of the equation. On the software side, computer scientists still need to find and deploy innovative solutions to solving these challenging modeling problems or else no amount of computing power will ever be enough. Hopefully, the scientific computing community will move to develop and share new (ideally open source) innovations oriented around solving these computing challenges, and an open environment of collaboration will drive the greatest innovation and adoption of these techniques.

If you have any thoughts on this subject, would love to hear them in the comments!