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By Steve Berkowitz
In a previous blog, I discussed the first step in the critical analysis of artificial intelligence in healthcare—an operational or working definition of artificial intelligence. I would now like to take that definition and take the next step—develop a descriptive model of AI that would illustrate the evolution of artificial intelligence and help us better understand the future of AI in healthcare. The model I propose has three components. Each one can evolve separately as well as influence the progress of the others. The ultimate AI application or outcome is a function of the effectiveness of all three:
HARDWARE – THE RAPID DEVELOPMENT OF PROCESSING POWER When I talk about the evolution of the individual processing unit, I am primarily referring to the unprecedented progress that has been made in the past sixty years in terms of fulfilling the mantra: faster, smaller, cheaper. Moore’s Law, coined in 1965 by Gordon Moore, an engineer at IBM, stated that the number of transistors in an integrated circuit will double about every two years. This doubling process has continued unabated to this day. Processing speed and efficiency are essential in the ultimate evolution of AI, and until very recently, it has been the rate-limiting step in the development of advanced capabilities such as language processing and complex machine learning. It has only been in the last five or so years that we have had the processing capability to have such entities as GPT and deep learning applications. Now that this limit has been attained and exceeded, true advanced intelligence is operationally and commercially feasible. One can only imagine what will be possible as this capability continues to double. The recent development of quantum computers, for example, which rely on using quantum mechanics and subatomic particles to power the processing, is currently in the research stage, but it promises to further jumpstart this cycle of “faster, smaller, cheaper.” AI will operate more effectively, a million times faster and more efficient. CONNECTIVITY – INTEGRATION OF INDIVIDUAL UNIT INTO NEURAL NETWORKS AND THE INTERNET Put simply, not only is the individual processor becoming faster, smaller, cheaper at exponential rates as mentioned above, but each individual computer unit now has the capacity to become interfaced and connected to multiple units and form large computer networks. These neural networks have the potential to give each computer access to enormous amounts of data, including becoming part of the internet of things, or the “cloud.” The ability to work as large systems makes the AI process much more robust. All systems would have the potential to communicate with each other and learn from each other… and even influence each other. Neural networks using artificial intelligence resemble the human brain, in which thousands or millions of individual units become interconnected and organized into layers. The output of one of the individual entities is now the input of another entity. This synergistic interaction further empowers AI to do extremely complicated processing such as deep learning and to do so faster, smaller, and cheaper. SOFTWARE – THE EVOLUTION OF MACHINE LEARNING AND COMPLEX REASONING Now that the hurdles of requisite speed and power as well as the ability to interconnect on complex levels have been considered, the model now moves on to the actual application of learning algorithms and logic models. As algorithms and systems become more sophisticated, more complex logic that simulates or even exceeds human logic, are incorporated into the model. Supervised learning, where models, trained with labeled data sets and pre-set algorithms, can now progress to unsupervised learning, and even reinforced learning. This allows the AI to evolve and move beyond the initial data sets and algorithms. As I mentioned in my last blog, not only can this learning bear enormous benefits, but it also has the downside of potential negative effects of emergent properties, and even hallucinations. Not only can the model readily incorporate the most basic form of reasoning, deductive reasoning (going from the general to the specific), but it also can begin to develop more complicated patterns of inductive reasoning (going from the specific to the general). The potential further exists to incorporate or “learn” more advanced forms of the human reasoning process such as creativity, emotions, and even empathy. AI has not acquired these traits yet, but if the human brain can learn these traits, why couldn’t AI too? And do so more quickly and efficiently. I believe we will see increasing complexities of AI resulting in applications that combine deductive, inductive, and more creative forms of reasoning. Once again, we can only imagine what the ultimate applications will be able to accomplish as the three stages of hardware, interconnectivity, and software come together and mutually advance. MODELING THE FUTURE OF AI IN HEALTHCARE As healthcare executives better understand the application of AI, it is valuable to conceptualize the model into these stages in looking to the future. I hope this model gives us a better framework in which to analyze the evolution and future of artificial intelligence, particularly AI in healthcare. Putting the model back together, we see there are three major components: the hardware, connectivity, and the software. All three will continue to quickly evolve, and work collectively and synergistically to improve the ultimate sophistication of the application outputs. Products that now flood the media daily have resulted from the evolution and development of these three stages. Planning your next event? Get in touch with us at the Capitol City Speakers Bureau today to schedule your ideal speaker and make your event a success!
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By Steve Berkowitz
Hardly a day goes by when there isn’t some story in the news about artificial intelligence (AI). We are bombarded by the media with topics both inspiring and terrifying. This is particularly true in healthcare. We read how artificial intelligence will drastically change the practice of medicine, nursing, our entire healthcare infrastructure, etc. The word “AI” has now become a buzzword—an overused and perhaps inappropriately cited cliché in our business. But there is no doubt that this technology will radically change how we do things in the future. But what is AI, particularly in the context of healthcare? As we dive deeper into the field of AI, however, we first must come to grips and hopefully achieve a workable consensus with some fundamental concepts: What do we mean by artificial intelligence in the first place, and how do we conceptualize and define it? Is AI an entity, and if so, does somebody own it and have accountability for it? Can AI develop a life of its own? Is this technology a blessing or a curse? Can it save mankind or destroy mankind? And the list goes on. WHAT IS ARTIFICIAL INTELLIGENCE? Prior to addressing any of those questions, we must first develop a practical working definition of artificial intelligence. As you will see, this is no simple matter. Webster’s dictionary defines artificial intelligence as follows:
Once again, it appears quite simple and predictable: There is an input in the form of a database, there are rules of engagement in the form of algorithms, and there is an output that imitates human intelligence to offer a solution to the problem, and all aspects of this process are supervised by humans. If that truly is the definition of the process, we should have little to worry about. What can go wrong if the AI is limited to a particular set of “rules”, and the computer simply applies these “rules” to a given, ostensibly finite, set of data points, all of which are controlled by humans? If anything, this should be a boon to humans because the computer has incredible speed, will apply the algorithms with complete accuracy, and can perform those duties 24 hours a day, seven days a week without stopping- all to serve mankind. COMPLICATIONS OF AI IN THE REAL WORLD As we learn more about the actual application of AI in the real world, however, two phenomena have been observed in the actual application of the AI process that are particularly concerning. First, even though specific “rules” may be initially programmed into the computer, through a process called emergent properties, the computer may randomly start to deviate from these initial “rules” and develop events or conclusions that are entirely unexpected and unpredictable. By deviating from the initial programmed constraints, the computer could develop a “mind of its own” which the original human programmers cannot predict or control. The ability to develop emergent properties allows the “machine” to modify the human controls and supervision. This introduces a scary randomness or uncertainty into what previously was considered a defined and confined process of human-machine engagement. Additionally, through a process known as hallucinations or confabulation, AI can randomly give a confident response that is nonsensical or not justified by its training data or source content, in other words, it can simply lie and make a false statement on a random and unpredictable basis. Put another way, the computer can occasionally make things up. Therefore, as a practical matter, I believe we must expand our initial definition of AI to include these two well-described phenomena. This makes the potential impact of AI much more precarious and frankly scarier. WHAT IS AI IN HEALTHCARE? Considering all of the above, let me submit the following working definition of artificial intelligence. I will use this understanding of AI in subsequent discussions regarding the impact of AI in healthcare: Artificial intelligence is the process whereby humans program a computer with multiple algorithms which the computer will apply to a predefined set of data points to provide a solution that simulates human intelligence…. BUT, on a random basis, uncontrollable by humans, it may change the rules of engagement, and on occasion, even confabulate and lie to you about the results. And it will do so extremely rapidly, extremely confidently, and extremely efficiently! We now have a practical working definition of the process of artificial intelligence. We must understand and concede that even though the rules of engagement may be initially set by human programmers, the end result may on occasion be compromised by randomly unexpected and even false conclusions. Artificial intelligence continues to advance using Moore’s Law of doubling every 12 to 18 months in capacity and ability. What we have today will pale in comparison to what we will see in the coming years. It is clear that: AI has the capacity to not only exceed human intelligence and human processing, but also exceed human control and accountability. Thus, we can see how AI can be simultaneously a blessing and a curse. It could deliver incredible value or create unforeseen and unpredictable disasters. There are two polar opposite answers to each of the questions listed above. Planning your next event? Get in touch with us at the Capitol City Speakers Bureau today to schedule your ideal speaker and make your event a success! By Steve Berkowitz
I’m sure by now, you have heard many times the latest buzz phrase “Follow the science” from our politicians. Dr. Tony Fauci recently said, “Science is truth, and as a scientist, I hold the truth”. Interesting sound bite, but it begs a deeper dive. What is “science”, what is the difference between science and opinion, and what is the role of the subject matter expert during this crisis? The scientific method is a time-honored and reliable process. It involves observation, proposing a hypothesis, testing that hypothesis through experimentation and research, and then drawing a conclusion based upon that data. The whole process is then vetted and peer-reviewed. Only then does the conclusion merit the status of truth. Of course, real life and the COVID pandemic are much more complex than any lab experiment, but the fundamentals of that time-tested methodology still apply. So, if we want to “follow the science”, we must clearly differentiate, what is a known truth versus what are interpretations or opinions regarding that truth. Scientific facts or principles, such as E=mc2 have passed the tests of time. But opinions by their very nature are subjective and prone to interpretation, conflation, ulterior motives, bias, and can sway with the existing political climate. Therefore, an opinion, regardless of the credentials of the “expert” behind that opinion, may not rise to the same level of truth as the scientific observations behind it. The science does not necessarily move in a straight line. There are a lot of gray zones. Opinions will change as the data changes, as we have definitely seen from the vacillating recommendations of our experts. Relying on the latest opinions may be akin to watching the stock market go up and down every five minutes. The problem with consensus: To complicate matters, opinions are often justified through consensus. As social beings, we strive for consensus in our decisions. But there is a conundrum with consensus. Scientific truth is objective and results from the application of the above scientific method. Consensus is subjective, and is the result of a political or social process– “the majority wins”. Truth is not determined by a popularity contest. It was the unanimous consensus of queen Isabella’s court in 1492 that the earth was flat. Consensus, yes. Truth, no. Going from truth to opinion can lead to problems as different people can and will reach different conclusions given the same data based upon their own experiences or biases. Lawyers deal with this every day. During a trial, for instance, a given set of facts are presented to the jury for their consideration. Each side typically produces their “expert”, who will take those facts and proceed to give their opinion. The trial becomes a spectacle of dueling experts. The experts on each side will take those same facts and advocate opposite conclusions. Which expert is right? The jury then debates which of the expert opinions are the most applicable or credible, and a conclusion is reached based upon their consensus. The facts remain the same, but the conclusions can be very different. Truth should be inviolate. Consensus can be arbitrary and easy to influence. This begs the question of what is “truth”. Neil DeGrasse Tyson described three kinds of truth: 1. Objective truth- It is true whether you believe it or not. It’s based upon the scientific method and should be a universal truth that is constant. Examples are F=ma in physics or the laws of thermodynamics which apply throughout the universe. 2. Personal truth- These are beliefs held dearly and are very deeply ingrained within the individual. An example is the belief in God. People who hold that belief will insist on its truth, end of discussion. 3. Political truth- Something becomes true because it’s been incessantly repeated enough to become be perceived as truth. Tell a lie often enough, and it becomes truth. In the age of COVID, the public is so desperate for facts, opinions can quickly be regarded as fact. The three types of truth, objective, personal and political, can become completely entangled with each other. The conflating of science with politics: Perhaps some of you saw Dr. Fauci getting grilled by Ohio Representative Jim Jordan who asked Dr. Fauci whether participating in public demonstrations could put people at risk for COVID. If a statute can require that a church congregation size should be limited, for example, shouldn’t it apply to any group, such as a public demonstration. After all, one thing is for sure. The virus is an equal opportunity infector. Fauci did not give a straight response to Mr. Jordan’s persistent questioning, resulting in our medical expert now being perceived as a political expedient. I believe he missed an opportunity to truly advise us. The virus doesn’t give you a break if you go to a demonstration, nor does it give you a break if you go to a funeral. As our medical expert, he should have emphatically stated that ANY public gathering can increase the risk of transmission. However, he was absolutely correct in not recommending a particular statute. The legislators should be the ones making the laws, not the subject matter expert. But the absence of an opinion is an opinion. Back to the four COVID dimensions– medical, economic, political and social: As we discussed earlier regarding the current COVID crisis, these four dimensions make the management of this pandemic especially challenging. The “truth” can be influenced by all four. Are scientific facts only used when convenient or expedient? Do we selectively only believe the facts that promote a particular non-scientific agenda? Scientifically driven conclusions are factual whether one chooses to believe them or not. For example, is wearing a mask a scientific truth or a political imperative or an individual rights infringement? Science, politics and social implications become completely immersed and subject to the ultimate motives of the politician. Confusion generated by non-experts- ultracrepidarianism: That’s a word you can use to impress your friends. It means giving advice or opinions outside of one’s base of knowledge. Do we really need to hear one more celebrity or athlete opine on social media? They may be superstars in their fields, but what do they know about COVID? Who even cares what they think? “Expert” opinion is on shaky enough ground, we do not need another baseless, extraneous opinion that is published just because someone is famous. Being well known does not make one an expert. Down with ultracrepidarianism! The ultimate decisions made by our President and elected officials are indeed challenging: A successful leader relies on the subject matter expert in any given area, but the subject matter expert is rarely the ultimate decision maker. Given the four dimensions, is even more complex. There is no pure medical solution. There is no pure economic solution. There is no pure social solution, and there is no pure political solution. Any effort in one dimension will affect all four. And it will get horrendously spun in an election year. Bottom line, the leader must take all qualified opinions into consideration, and ultimately make the best decision to best improve the overall outcome. The subject matter expert weighs in. The leader decides. It starts by clearly discerning what is fact and what is opinion. Once the expert ventures beyond the scientific facts, that expert now enters the twilight zone of conjecture, regardless of the credentials of the so-called expert. That person has gone from science to speculation. Remember the old TV show Dragnet? Inspector Joe Friday said many times, “Just the facts”. I hope our President and our elected officials can develop the appropriate discernment between truth and opinion. If we pledge to follow the science, let’s follow the science. Opinions and recommendations, even from the subject matter experts, are still opinions, and not necessarily science. We need this discernment in order to truly combat the COVID virus. We need it badly! Stay healthy! Planning your virtual event? Get in touch with us at the Capitol City Speakers Bureau today to book your healthcare speaker! “Flatten the Curve”. We hear it every day. What does it really mean for the average person?
Let’s start by looking at the fundamental two-phased approach for any epidemic or outbreak: 1. Containment- There are two components of containment: secure the perimeter of the infection to prevent any future spread outside of that region, and then eradicate the cases within that region, resulting in eliminating the threat. If there has already been spread that cannot be contained, one must move on to the next step- 2. Mitigation- Limit the spread and reduce the burden of disease. Basic mitigation measures include: a. Sheltering in place/ quarantine b. Social distancing c. Wearing masks, personal protective equipment (PPEs) d. Frequent hand washing e. Not touching the face or eyes f. Isolating and protecting the vulnerable, high risk population g. Isolating new or possible infections by staying home if one has symptoms or has been exposed If these mitigation efforts are successful, the new infection rate should be reduced and the overall curve of the infection should be blunted or “flattened”. Hence the term. In March, the President made the unprecedented move to shut down the economy and impose travel restrictions to and from the affected areas. During this time, it became rapidly clear that the actual spread of COVID was worse than previously thought. Pure containment was not possible, and efforts had to be directed toward mitigation. As we began to quarantine and shelter in place, we heard the expression “flattening the curve” from our elected officials and medical advisors to the point of now becoming a mantra. It is important to note that flattening the curve does NOT change the total number of cases that might develop over time (the total area under that curve). It just spreads those cases out over a longer period of time. Successful flattening of the curve will therefore result in reducing the number of active infections at any given point in time. To the negative, by reducing the number of cases at any point in time, it will also prolong and delay the time required to achieve “herd immunity”. There are three critical reasons to “flatten the curve”. We must decrease the number of active cases at any point in time in order to: 1. Not overwhelm the health care delivery system in terms of hospital beds, medications, ventilators, other supplies and very importantly, health care personnel. 2. Maintain an adequate core healthy work force so that the economic infrastructure can still function. 3. Defer the onset of infection in as many individuals as possible, especially those at high risk, into the future where hopefully “good things” will happen, which will then result in decreased total morbidity and mortality. The “good things” list that would actually reduce total case morbidity and mortality includes: 1. Natural attenuation of the virus- spontaneous, seasonal or otherwise, it goes away 2. Effective medications for prophylaxis or treatment 3. Immunity is developed either through an effective vaccine or population “herd immunity” Until one or more of these occur, we must dutifully continue all mitigation measures. Although to date, none have reached fruition, we have seen some optimistic signs. There is evidence that the virus is intrinsically less deadly than it was earlier in the year. We are protecting the vulnerable so that the average age of infection has decreased, which has reduced morbidity and mortality. Certain medications such as remdesvir and dexamethasone have shown a promise to reduce hospitalizations and mortality with other drugs in the pipeline. Several companies seem to be progressing well on the accelerated development of a vaccine, with availability potentially by the end of the year. Ultimately, the goal is to achieve a mass immunity in the population, often referred to as “herd immunity”. “Herd immunity” exists when enough of the population has been infected and has become immune so that the virus no can no longer replicate within the population, resulting in the virus either becoming dormant, endemic, or even disappearing. Typically, “herd immunity” requires 60-70% of the population to be exposed and develop antibodies to that virus. Optimistically, one study suggests that COVID immunity could be achieved with as little as 20% infected. We simply do not know the number, except that it is much larger than the present number of people infected. We are just beginning the road to immunity. The problem with just letting a population spontaneously go toward this herd immunity, like the approach in Sweden, is that in order to get to that point, 60-70% of the population has to suffer the disease. Until some of the “good things” happen, “herd immunity” is a long way down the road, hopefully months, but perhaps years away. So, here’s what we know for now: Mitigation efforts will have to continue indefinitely until those “good things” happen. The economy must continue to rebound and move forward We now come back to the four dimensions of decision-making: medical, economic, political, social. We need to find the sweet spot that balances the need for the virus mitigation with the imperative to sustain and grow the economy. We will need to manage the political and social consequences of this balance as well, especially as the election draws near, and issues will become further politicized. There has been a great deal of talk about the economy having opened too soon, especially in response to the rapid rise in new cases over the past month. Given the proximity of the elections, opening too soon has become a political hot potato. Medical/economics/politics/social dimensions are at odds with each other as politicians battle back and forth while Americans continue to succumb to this virus. A more productive way to resolve this discussion of opening too soon is to say that the issue is not necessarily opening too soon per se, but rather, once the society was opened, many thought we returned to pre-pandemic times with “business as usual” prevailing. Part of the responsibility in this resurgence lies in the fact we all dropped our guard a bit after the frustrations of the initial lock down. And the virus fought back mercilessly. Similarly, masking has devolved from a medical issue into a social/political issue. Should masks be mandated? Where is the balance between public health and individual rights? Who is protecting the public? As are now in the complicated process of managing this new resurgence of COVID. Here are some thoughts to keep in mind: 1. Mitigation efforts need to proceed with even more urgency as the overall prevalence of COVID in the general population has increased due to this recent surge in cases. Social distancing and wearing a mask are more important now than ever, as the chance of randomly encountering a patient with COVID is higher given the greater prevalence of the disease. 2. There is some reason for optimism on the “good things” list, but until then, we must continue the course of strict mitigation methods. There is a light at the end of the tunnel, but we are still in the beginnings of this ordeal. 3. We must take all efforts to minimize crowds. The virus is an equal-opportunity infector and loves to be in high concentrations of people. The virus does not care if the crowd is demonstrating, worshiping, watching a sports event, being educated or simply riding a crowded bus. If these encounters must occur, major mitigation efforts as described above are a must. Whether mandated or voluntary, it is a public responsibility for which every American must step up to the plate. 4. Even though we are daily tantalized by potential vaccines in record time, we need to take a long-term approach to our present efforts. Everyone wants this to be an old memory, but until then, we all must work together to achieve the goal of eradicating this virus. Medical, economic, political, social differences and implications—We must stand together. Planning your virtual event? Get in touch with us at the Capitol City Speakers Bureau today to book your healthcare speaker! |
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