Healthcare is changing. It isn't a singular change, or a particular advancement of one form of technology. Instead, it is the emergence of a system promulgated by thousands of incremental technological advances. These changes include but are certainly not limited to:
- decentralized electronic medical records
- smartphone integration
- publicly available scientific data on pathology
- artificial intelligence for diagnostics
- lab-on-a-chip for cheap, personalized diagnostics
- personal genomic sequencing
- personalized drug programs
- regenerative medecine
(This goes further, but for the purposes of keeping our feet on the ground, these advances are enough to astound and inspire).
Please consider the following contrasts, with only a decade of technological advancement separating them:
You are sitting at home watching TV; you begin to feel heavy discomfort - a tightness in your chest, numbness in your arm. You feel dizzy, lightheaded. You happen to be aware of the signs, and manage to get up, trying to make your way to the phone in time..
You are driving to work, stuck in traffic. Your smartphone beeps the alarm for your healthcare app. You have a heart condition, which you've had to travel long distances to accommodate in the past, as specialists aren't located near you. The heart monitor, a small band on your finger, sends a constant stream of data to your smartphone, which in turn relays it to your doctor. Within an hour, you learn that your risk of heart attack has increased by several dozen percent for the proceeding week. You call your family doctor, and you're able to prevent catastrophe.
You are prescribed a drug for blood pressure. You die.
You are not prescribed a drug for blood pressure that would kill 25% of people with a particular genetic variant, for which you have been screened. Another is prescribed instead. (Conversely, effectiveness can be increased by genomic vetting).
A group of terrorists kidnap a synthetic biologist and force him to alter the genetic code of smallpox to create a virulent superbug; they release the virus into a public place; millions die.
A group of terrorists kidnap a synthetic biologist and force him to alter the genetic code of smallpox to create a virulent superbug; the CDC detects it in the smartphone of the third person infected, and a quarantine is established, while scientists construct a vaccine from the genetic information collected by the smartphone.
These are only a few examples which demonstrate the staggering implications of technological change in medicine. They embody the advancement of two trends: convergence, and diffusion of information. Though these two seem at odds, they are closely related in the case of medical advancement. Medical resolution technology increases by a factor of one thousand per decade. This means an MRI will be considered barbaric, compared to, for example, fluorescent biomarkers tagged to cancer cells, and identified with laser light.
It used to be that medicine was defined by physiology as delineated by rigorous study of biology. Now, biophysics and computational systems biology are coming to the forefront, and emergent properties of such systems can be predicted with greater degrees of accuracy. These techniques are currently used for research purposes, but as the technology's price performance drops below the "threshold" or "critical margin" as I call it, such technologies become mass-distributed among the populace. Consider X-rays, which are now common, but at their outset were rare. Computers, and by extension, smartphones, follow the same trend. Combine the plummeting price-performance of smartphones, computation, resolution, and nanotechnological diagnostics; and you get the makings of a doctor-in-your pocket.
This brings me to the point of Artifical Intelligence as a vehicle for diagnostics. It has already been demonstrated, for example, that an AI can diagnose breast cancer earlier than a team of oncologists.
(The method employed to accomplish this, a neural net, is also employed to mimic audivisual and sensory information; to recognize patterns [cats and dogs were among the first objects]; as well as being the primary architecture of Google's search algorithm).
This is in part due to the advancement of software which mimics brain processes; namely, artificial neural networks. These are not new phenomena, however, the computational capabilities we can now achieve make such informational brain-mimickers effective. Using Hierarchical Hidden Markov Models; users are able to determine "fitness" or "rightness" of a network, and base the inputs of Markov Models to fit the success or failure of the initial goal. "Eigenvalues" are probabilities that a neuron will fire or inhibit (there is recursion as well - namely the likelihood that a previous neuron will fire again). This is the same way our brains work when interpreting images, and making complex decisions. Our experience dictates the likelihood a neural network will fire based on past experience.
[I'm learning the math behind this for artistic purposes]
This transitions to my next point, which is that overall computational power of populace + Smartphone biophysical readings + mass-distributed Markov-chain-style AI + open source pathology = Smartphone doctors. Doctor Pepper.
Let's not get too excited now. It is impossible to model a full human brain using this software structure, under the current computational conditions. However, new advances in materials science, physics, and nanotechnology will allow such things to be possible. For example, nanofluidics are a way to reduce the energy consumption of transistors, while mimicking the flow of ions in the brain. Giant scaled architectures of neuromorphic chips, or nanofludic superconductors, would produce brain-like activity. The scale would need to be huge, however, as there are trillions of synaptic connections in the brain. Currently, less than fifty million synaptic connections can be modeled.
Nanoparticle advancements are currently allowing powerful new diagnostic tools. It is possible to have particles which can be tracked using various forms of energy (radio waves, laser light, ultrasound) - which attach themselves to disease-oriented processes. This is a bit of a sloppy generalization, as nanoparticle design has exploded in the past few years - spanning a range of diseases.
Possibly the most exciting paradigm of healthcare which we are approaching is 3D manufacturing, and regenerative medicine. It is currently possible to print organs using biocompatible scaffolding, a special 3d printer, and the patient's own stem cells. What this means is organ transplants could be done without a donor, and with zero chance of rejection. Currently, printing such an organ can take several hours, and costs a lot. But just as smartphones and with resolution technology, price performance constantly drops.
Medical technology is extremely important, because it is the primary driver of technological progress. It is the largest market for consumers on earth, and the platform for advanced civilization. Health is the hallmark of advanced nations, and is reflected in productivity and life-span (quality of life also coincides). As a result, the medical field naturally integrates seemingly divergent fields of scientific study to accomplish these problems; as was proposed by MIT in a white paper on Convergence in 2010. It is no coincidence that Obama has decided to consolidate (and perhaps subsidize) 3d printing, and neuroscience, in a multidisciplinary effort to uncover what may be the most important discoveries in the history of medicine. Penicillin will seem a trifle, when a bullet wound can be healed by lasers, and a spare heart can be printed in your garage.
There is an important point that should be raised on the subject of this changing medical landscape: ethics are a question that will not be asked by those seeking profit, or to hold positions of hierarchical, or economic power. It is a question that should be fielded at academic institutions, but in the public sphere as well. This is easily accomplished using the internet. Another threat produced by this technology is the risk of mass-surveillance, and not just mass surveillance, but the kind that knows what you eat, drink, and even think. That's right, Markov models with nanofluidic smartphones could likely predict your behaviour, and that information might be valuable to governments and corporations.
Additionally, decentralized medicine is a threat to educational institutions, pharmaceutical companies, venture capitalists, and hospitals. Why pay to see a doctor who is ten times more likely to misdiagnose you than your $100 smartphone? It is a legitimate question, which should be answered in a way that is non-destructive to the medical framework that we currently benefit from, but also integrates the advantages of very powerful, very cheap medical care. (The answer is simple ; drop the cost by a factor of 10, increase the volume by a factor of 10). My final admonition is to advocate that the fate of a human's health should be in their hands as a fundamental right, and not a proprietary one belonging to a corporation or government.