It appears that the completely archaic notion of mass-produced drugs for the average patient is about to change (1). The manufacturers paid lip service to personalized medicine for nearly a century and it was clear that their heart or business models were never in it. The normal function may have done as much damage to humanity as nuclear weapons, for those who adhere to it blindly believe in averages and standard deviations based on a manufactured construct. The only redeeming quality of humans is that they are different and diverse. As the men in power separate the weak from the wealthy, the struggling from those who never struggled, the golfers from those who cannot afford a club, the academics from practitioners, the atheists from the religious, the North from the South, the West from the East, they miss an important point - every human on Earth is different, regardless of the visible features they exhibit or where they originate from.
The design of clinical trials seems to fail this basic notion. Pushing humans through protocols like cattle through a food manufacturing company is not the best way to discover drugs. It is certainly the best way to reduce costs and to prove to the regulators that something important has been done. In the process, they left large underserved populations in the lurch and pumped those who take the medicine with a dose that is suboptimal. Emerging technologies are immensely capable to figure out who will benefit from a drug and who will not and at what quantity. It is time statisticians left the industry as their contributions do more harm than good, not unlike the insurance industry, clinging to actuarial tables.
Now, available technology can titrate every individual to the optimal dose and we do not need, "population statistics," to approve or to disapprove drugs. If the regulators do not return to school to learn what has been happening, they will continue to make bad decisions.
(1) Digitization of multistep organic synthesis in reactionware for on-demand pharmaceuticals
Philip J. Kitson, Guillaume Marie, Jean-Patrick Francoia, Sergey S. Zalesskiy, Ralph C. Sigerson, Jennifer S. Mathieson, Leroy Cronin*
Recent news that a single blood test could provide the diagnosis of eight common cancers with 99% specificity (1) is a constant reminder that medicine is still stuck in archaic and invasive procedures to detect, diagnose and treat ailments. With a high concentration of human resources in provider settings, medicine has been slow in embracing emerging technologies and ideas, outside the domain. And this attitude is shared across the healthcare value chain including manufacturers, payers, and regulators.
It is unfortunate. Granted, Biology still remains to be the arena where humans could not progress exponentially. Their brains, with millions of years of deterministic training, have been well specialized to dominate engineering and chemistry. However, they could not understand the marvelous machines assembled by nature from a single cell organism to somewhat more complex humans, with any level of precision. Nature has had time to perfect designs of such beauty and humans, ever curious, have been trying to walk up to the cup of knowledge. But it has not been. Fossils indicate attempts at brain surgery many hundreds of thousands of years ago and despite higher structural knowledge, we have not advanced sufficiently to a differentiable plateau. In most simpler fields, we have demonstrably shown that humans are the weak links in decision processes - from transportation, energy, manufacturing and even, finance.
It is a conundrum. We are stuck - great strides in deterministic sciences do not translate into domains of high uncertainty and diversity. And, those who practice in these complex domains seem to have their blindfolds on as if they have nothing more to learn. Diagnostics could provide the impetus to move higher - serum and stool harbor such information content, it is a shame we have not figured it out.
(1) Detection and localization of surgically resectable cancers with a multi-analyte blood test
1. Joshua D. Cohen1,2,3,4,5, Lu Li6, Yuxuan Wang1,2,3,4, Christopher Thoburn3, Bahman Afsari7, Ludmila Danilova7, Christopher Douville1,2,3,4, Ammar A. Javed8, Fay Wong1,2,3,4, Austin Mattox1,2,3,4, Ralph. H. Hruban3,4,9, Christopher L. Wolfgang8, Michael G. Goggins3,4,9,10,11, Marco Dal Molin4, Tian-Li Wang3,9, Richard Roden3,9, Alison P. Klein3,4,12, Janine Ptak1,2,3,4, Lisa Dobbyn1,2,3,4, Joy Schaefer1,2,3,4, Natalie Silliman1,2,3,4, Maria Popoli1,2,3,4, Joshua T. Vogelstein13, James D. Browne14, Robert E. Schoen15,16, Randall E. Brand15, Jeanne Tie17,18,19,20, Peter Gibbs17,18,19,20, Hui-Li Wong17, Aaron S. Mansfield21, Jin Jen22, Samir M. Hanash23, Massimo Falconi24, Peter J. Allen25, Shibin Zhou1,3,4, Chetan Bettegowda1,2,3,4, Luis Diaz1,3,4, Cristian Tomasetti3,6,7,*, Kenneth W. Kinzler1,3,4,*, Bert Vogelstein1,2,3,4,*, Anne Marie Lennon3,4,8,10,11,*, Nickolas Papadopoulos1,3,4,*
Deep learning has been in vogue. Combining ideas from the 60s and an insane amount of computing power, the search giant and others have been learning deep - mind and all. This is good news, gentle tricks on established mathematics seem to have reduced overfitting and accelerated "learning." But, technologies based on unlimited resources and computing power, tend to be lazy and deep learning seem to have all the characteristics. Some even call it "Artificial Intelligence," even though there is nothing artificial or intelligent about it.
Humans have been fascinated by their brains forever. They have searched for the mind and soul in a few pounds of messy grey matter they carry on their shoulders but found nothing. When the computer scientists arrived who could create "General Artificial Intelligence," by assembling dumb silicon and using dumber games, their age showed why wisdom is not that easy to attain, Ph.D. or not. The search giant has been on a prowl, picking up anything that ends in .ai for a premium and as the greatest technologist of all times who invented the electric car and electrified space travel proclaimed that only he knew what AI was all about, we seem to have arrived at ego driven emptiness.
Get used to it. Nobody is intelligent enough to create "general artificial intelligence." Those who harbor higher than average brain cells have headed in the opposite direction by proclaiming that knowledge results from understanding and not modeling ideas. Therein lies the conundrum, as the technologists rise without human contact and attempt to travel to Mars, there appears to be a great vacuum between knowledge and know-how. There is a distinct difference between the two, the former conquered by philosophers and the latter by engineers and it is important to distinguish between the two.
It is time to look forward and abolish ego-driven behavior. Those who are prone to it should be told that they are no better than the worst of humans.
For nearly hundred years, every field, life-sciences, manufacturing, high-energy physics, economics, healthcare, and others relied on basic statistics and a rather crude assumption that everything follows the Normal function. There is nothing wrong with the assumption but in a regime that works on the tails, the observation that something works for the population has little practical value. In life sciences, they have been inventing mediocre therapies for over a century, as the clinicians, their regulators, and aiding statisticians have been enamored by the mighty "p-value." They have been striving to prove that the incremental average benefits delivered to a large population are a lot better than life-saving therapies for a few. In manufacturing, they have been optimizing with constraints in an attempt to save nickels and dimes. Lean, mean and mighty, their determinism has led to incorrect decisions in the presence of uncertainty. In healthcare, they have been waiting for the protocols to change based on simplistic observations of small samples. Meanwhile, half the healthcare costs in the World could be attributed to a handful of related disease states. In physics, stuffed with engineers, they have been deploying heavy steel for finding particles and hearing waves, based on basic statistical notions. Even with that, they will be the first to admit that they do not yet know 94% of it. In economics, they have been inventing theories based on regression and even winning Nobel prizes but it is unclear if they are creating insights. Some of them ventured into even making money and some have failed spectacularly as would have been predicted by their own theories. Overall, if one can write down an equation for a process, it is symptomatic of the fact that she has not understood it. The practitioners, who seem to cling to the past are being rendered less effective in the presence of those who look forward.
A generation seems to have wasted their time adhering to basic principles laid out a century ago. Lately, statistics have been made sexier by better naming - now called, "Machine Learning." One has to admit it does sound a lot better, but has anything changed? In a world full of practicing scientists, who have been trained to make equations for everything, we are approaching a significant discontinuity. Machines are certainly marching forward but not because they know statistics but because they do not. Such is the state of affairs that a systematic education delivered by the greatest institutions in the world prepares the next generation to fail with high certainty. Meanwhile, machines can see, hear and make decisions in the presence of uncertainty. As we hunt for fossils to establish our own identity in a process that seems to have taken a long time, machines with no emotions and even less historical baggage, rise. Are humans being rendered irrelevant? As the greatest living physicist warns of ETs, as the world's richest and powerful worry about AI, and as the most powerful man on Earth worry about if his hair is falling straight, we have arrived at the precipice of a great discontinuity.
As they moved out of their homeland in Africa, humans must have made important calculations based on uncertainty. As they descended from the trees into the African Savannah, a few million years prior, they knew the regime was shifting. With dangers all around them, mighty beasts who could maul them in a single swipe, they made decisions based on uncertainty. Their initial journeys into the Middle East and South Asia, closely followed by those who went a bit North, seem to have provided a level of safety. They advanced culture and boredom, the latter most important for the development of human psyche. As the caves in Southern France prove, they could certainly rise above determinism and engineering, very early in their progression.
The regime is shifting again - the opponents are not as gentle as the Neanderthals. Machines are brutal and they are immensely capable. Humans, the victors of past conflicts, are starting from a position of a great disadvantage because of their education of the past. The end of statistics, a figment of the imagination of the most recent generation, is very near.
A new study (1) demonstrates that there are significant common factors that influenced the evolution of past societies. One clear and obvious trend is toward more complex arrangements. The researchers analyzed a large database spanning over 400 societies over 10,000 years. The results show that human societies follow a singular blueprint as they evolve. This appears to have many implications for future designs.
Size, decision controls, information systems, literature and economic development are features that all contribute to a singular measure of social complexity (1). Given the large data set, the researchers may be able to assess the level of development in contemporary societies as well as speculate on eventual outcomes. The fact that most societies show growth and predictable decline means that humans are stuck in a blueprint that was put in place a few million years ago. With complexity grow arrogance and inequality and those climbing to the top of the pyramid seem to lose context and wisdom. Given the data, it appears possible to predict the half-life of the present societies with high accuracy. But it is unclear if such information could have any practical effect on policy that could reverse the predetermined course.
On the positive side, the level of knowledge and sophistication seem to have equalized across countries and societies. Those who were ahead have been arrested by ignorant leaders and those behind are driven by a desire to succeed. In either case, modern humans, already long in the tooth are due for a reset. It is a shame that they could not learn from the abundance of historical data using their nascent tools in "machine learning."
A recent article in the Proceedings of the Royal Society B (1) proposes that "class is inversely related to a propensity for using wise reasoning in interpersonal situations, contrary to established class advantage in abstract cognition. " This is an important finding that could explain why the world appears to be slipping in knowledge while increasing in know-how. The idea has been recognized by advanced societies of the past and the prophets and leaders of yesteryear advocated egalitarianism as an optimum design tool to advance wisdom.
If wisdom, indeed, is inversely correlated with intelligence, that may pose a great challenge to those pursuing advanced societal designs. The referenced study appears to demonstrate that activities that enhance education and presumably abstract cognitive capabilities are incongruent to the individual's ability to reason wisely. That may portend a decline of developed countries in the West who optimize know-how and mechanistic education at the cost of wisdom. Recent trends in the US and UK could be symptomatic of this idea as large swaths of populations, in spite of their education, seem to act without a tinge of wisdom and make decisions that future generations will find hard to fathom.
The mistaking of know-how for knowledge, intelligence for wisdom, wealth for competence and speech for comprehension, have brought many civilizations down in the past. Is history repeating itself?
As the thousand people in Washington, whose healthcare is covered for life, figure out how many millions they would like to deny the same benefits, the industry is going through a massive transformation. The system, suffering from misaligned incentives and sophisticated gameplay, is likely the most complex. It is a lot easier to figure out autonomous cars and even “artificial intelligence.” The fundamental question in healthcare is how to maintain the health of every individual in a cost-effective fashion. There is only one class of humans who come close to this objective – providers who take care of patients and clinicians in manufacturing companies who want to solve big problems.
However, providers are suffering from technophobia. In less than five years, steering wheels will disappear from automobiles and humans will be a rare sight in manufacturing and power plants. Machines, without biases, are proving to be superior to humans in many decision processes. Every aspect of medicine, even the most cherished clinical components, will be influenced by machines in a few years. Machines, like it or not, will get better at diagnosis and treatment. The role of the provider will change to explain rather than to determine, for humans constrained by slow evolutionary processes will remain prisoners of the present.
The tsunami in healthcare is on the way. In its foggy supply chain including manufacturers, providers, payers, and patients, sunlight will descend and there will be no hiding anymore. Prevention shall matter more than treatment, non-invasive intervention more than invasive procedures, primary care more than specialty care, inventions more than incremental therapies, the patient more than a singular disease state and care plans more than procedures.
Providers who embrace technology will accelerate this trend and others could get ossified.
It appears that complexity is increasing in every field. Past experience tells us that knowledge only arrived by simplification, the exact opposite of what seems to be happening currently. In Physics (1), theories have been emerging from every corner but most of them are pure fantasy and remain to be unprovable. Having a mathematical foundation to a theory does not mean that it is useful - one could always dream up such constructs but they have no implication for knowledge for lack of testability. In Medicine, doctors seem to believe that humans are extremely complex to figure out and they seem to adhere to empirical tests of small samples that emerge routinely. In economics, simple theories are now considered commonplace and academics are constantly on the hunt for more complex formulations.
Are we reaching the limits of knowledge? The slope of aggregate knowledge has been declining since the 1930s, and it is problematic for a society that believes it is progressing forward. Yes - technology and engineering have made strides but those are applications of knowledge not the creation of it. There, the current crop of technologists appear to be highly efficient - Artificial Intelligence and all - but none of these ideas are going to make a step-function change in knowledge. To make matters worse, money has been a luring influence on emerging thinkers, who have shunned academics and headed to the nightmare on Wall Street or the valley, replete with coding testosterone. The few who have stayed behind seem to be more attracted to complexity rather than creating insights. The committees who award prestigious prices, including the Nobel Prize, also gravitate toward complexity and that provides misguided incentives to young academics.
We are slowly slipping toward the next dark ages of knowledge creation. With no progress in aggregate utility metrics for society, one could argue that we are living through one of the worst time periods in human history. The arrival of the next genius, who can simplify and create knowledge is the only hope.