Over the past 2 days I been given a view of the possible future of medicine, attending two lectures: ‘2084: Future Medicine’ and ‘Nanotainment‘. I will discus the actual presentations later, but the impact is that doctors as we know them and other current medical practitioners being will become obsolete other than for the treatment of injuries, and artificial intelligence linked diagnostics on our ‘companion computers’ (smartphones or their descendants), will be the future of medicine.
The three key themes are:
- genome specific medicine
- individualised constant analysis
- the impact of nanomedicine
This post gives background, more detail and discusses the material presented.
Background | Detailed view of the future
Background.
Professor David James, from the Charles Perkins Centre and School of Molecular Bioscience, was quite emphatic on three background points:
- Obesity is not a will power problem, but an interaction of genes and environment
- Current medical science uses a ‘one size fits all approach’ which is problematic
- Earlier detection is both possible and necessary for optimum outcomes
Obesity.
Several examples supporting the key premise were presented, and I list some here but not necessarily in the order presented. The general point is that humans have been naturally selected over a long time to be able to survive periods of famine, such the current gene pool does not necessarily handle well an environment of continual availability of unlimited food.
One illustration presented was follow-up analysis of outcomes from a group from ‘the biggest loser’. The contestants all did, during the contest, lose significant weight, but with only two exceptions, all gained even more weight subsequently, producing a negative outcome. For one of the two who did not gain more weight, the result was through medical intervention achieved this through stomach bypass surgery, leaving only one contestant with a positive outcome from the program itself.
Another illustration cited with the island state of Naru. Before and after pictures of a typical resident before the combination of becoming a wealthy modern society and a World War II experience that further selected only those able to survive starvation as the only population. The contrast was profound with the process leaving the nation with the highest obesity rates in the world. This article on Wikipedia discuss the record obesity, but does not include information on the genetic selection contribution to the problem. A quick further search revealed much information such as this article (see the section genocide of the Naruans).
The one size fits all approach.
Another illustration was data on how experiments showed that from earthworms through to mice, a variety of organisms all lived longer with a reduced caloric intake. But only when you group the entire population together! Analysing the population by genome type, almost as many actually died younger as lived longer. If you were one of these earthworms about to volunteer for the experiment, best learn which genome you have first!
Data on cutting back on specific foods yield similar results. The is basically no food that everyone needs to have radical cutbacks, different people thrive with very different levels.
Similarly, data was presented on the efficacy demonstrated through trials by a common diabetes medication. The result at full dosage: 50% efficacy, which is impressive. But the downside is for the other 50%, the medication provides no benefit at all despite any risk of side effects. With genetic analysis, we could determine which people would gain no benefit rather than just prescribing the same medication regardless.
Early detection: Importance and feasibility.
Consider the graph to the left. The Orange line plots the progress of health problem, such as cancer or organ failure. Initially all is healthy, then the trigger(A) occurs and health drops into the problem area, but until something actually fails the problem goes undetected. Once health actually fails in some way (B) a visit to the doctor and the problem is diagnosed and with treatment (C), health is improved, but significant recovery is required.
Now consider if the initial trigger (A) could be detected without waiting for the problem to progress to a failure that produced obvious symptoms. None of the internal problems, such as spread of the problem, never occur. The re-mediation is simpler, and full health is maintained.
Detecting the initial trigger very early? Professor James has obtained sufficient data from mass spectrograph of just a drop of blood to detect a wide variety of potential initial triggers at just such an early stage. All that is required is a system or process to take such scans routinely.
Detailed view of the future.
- Individual Medical Records, with volumes of data
- Doctors: less doctors primarily for trauma and accidents
- Medication: tailored for the individual, with live and individual trials
- Surgery and Micro-surgery.
Individual Medical Records.
In this future, each individual would have medical records of their fully decoded genome, as well as continuous monitoring of their body to enable early detection. Currently, individuals all keep masses of data in terms of photographs and other files, but almost no health data, and individuals have little control or access to what health data is kept on them. Moving to store health data in format usable by several applications, and accessible by the individual would be life changing.
Doctors.
The amount of data collection discuss simply cannot be cost effectively be collected by ‘visiting the doctor’. Ideally data would be collected not just daily but even hourly. To be at all cost effective, this requires automated or self collection. Do we go as far as allowing medical sensors to be implanted? Obviously the actual solution depends on how far into the future we are discussing. But expert systems, processing the data collected and processed by our own companion computing are the most logical future for medical diagnosis. A ‘doctor’ would then only be required to confirm diagnosis which are not secure: for example symptom which could be tampered with in order to gain access to medication. But generally, there will be less need for individual doctors as the process is automated, and the information processed becomes more personalised and less about treating all individuals in a common manner.
Medication.
Development and trials: a significant rethink
Drug companies currently spend fortunes on trials. These trials are still generally in the ‘one size’ fits all category. If different people may react differently, then this is a statistic, not a proposal to personalise outcomes. So a drug that is 50% effective may be prescribed to everyone, rather than attempting to identify who is part of that 50%, and it follows that no effort is necessarily made to find a specific treatment for those people for whom the drug is not effective. Currently, the question becomes, “can we instead find a single treatment that works for 70%, even if that means starting from the beginning again”, rather than, “we have 50% covered, can we cover half of the remaining people and then have a solution then for 75% of subjects”.
What is the point of the current trials system if everyone is different? We need a trial for every genome that could possibly react differently. Now consider that if every individual is constantly monitored, then they can become the subject for their own individual live trial.
In fact, every course of medication becomes a live trial, assisting building data for the future and continuously monitored for efficacy and potential side effects.
Surgery and Micro-surgery.
Physical damage from accidents could become the only reason for surgery as we know it today. All other conditions requiring intervention may be detected so early that far less invasive treatment is required. Micro biological machines may perform the equivalent of today’s surgery without the trauma and operate inside the body, even as we continue daily tasks.
Conclusion.
Medicine as career is just as subject to change as factory work. The role doctors play today will largely move in two directions: expert systems and distributed technology allow continuous diagnosis without ‘a visit to the doctor’, and automation and new technology superseding most intervention medicine. Medical research will be the growth area.
For individuals, expect the ‘fitness tracker on the future’ to become a part of our lives.
