Healthcare is evolving from one-size-fits-all to personalized, from reactive to preventive, from intuitive to data-driven, from paternalistic to participatory. Can crowdsourcing facilitate this transformation?
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2018 should be very exciting for science and technology. As seen in the diagram below, recent years were filled with groundbreaking projects and the stars may be aligning for something really big, driven by advances in software and hardware.  Last year, artificial Intelligence software hit the mainstream. In 2018 it will be more prolific, more creative and invade every corner of our life. The current, second, wave of AI is not quite ready to break and deep learning will continue to dominate this year . Deep learning is driving the future of autonomous vehicles. Fully automated self-driving cars (level-5, as defined by the Society of Automotive Engineers and depicted in the figure below) won't take over the roads this year or next, but we'll see plenty of highly automated vehicles in 2018. Models of ownership will be also changing - perhaps to a monthly subscription service vs lease or traditional ownership.   Machines will be taking on more responsibilities in education, getting more heavily involved in the evaluation and counselling of students. Massive open online courses, or MOOCS, did not meet the great expectations everybody had. But e-learning learned to overcome the challenges, smaller private online courses started to gain more popularity and many traditional universities are already at risk to become obsolete. Seven years ago, in his book The Innovative University, world-renowned innovation expert Clayton Christensen predicted that as many as half of American universities would close or go bankrupt within 10 to 15 years. This year AICTE (All India Council for Technical Education) will be shutting down around 800 engineering colleges as students can get comparable or better educations over the internet. Only the most technology-advanced Universities will evolve, find ways to reduce the cost of education, perhaps even build programs for specific companies, and survive.  MOOCs is an example of mass collaboration - as is Wikipedia or Citizen Science. The model has many other flavors and names - the sharing economy, the gig economy, the peer, platform or on-demand economy. It enabled brands like Uber and Airbnb to become world giants. Virtually any industry could be disrupted by Uberisation, through sharing of assets and human resources. And labor market is quietly transforming traditional job structures into on-call microjobs without benefits. The gig economy is lonely and it often turns over-educated individuals into low skilled workers. Could we develop a better platform to utilize complex technical skills, social abilities and cultural competences? Could we create a new social safety net where the new age workers can rely on each other?  In 2018, Amazon and robots will be moving into health and wellbeing, and telemedicine will become mainstream. Fitbits did not take over the world, but an "immersive fitness" trend is paving the way to the new gym-less future of fitness. Small accessories such as AliveCor's Kardiaband EKG reader detecting atrial fibrillation are already recognized as medical devices. Omron's HeartGuide smartwatch can measure blood pressure on the go and will seek FDA approval later this year. AI has shown its ability to screen for eye diseases and skin cancer. The cost of genome sequencing - touted as the future of healthcare - was rapidly plummeting until we realized that the quality remains a problem and much remains to be learned. In 2018, the ambitious 100K Genomes project will be completed and the 100K Foodborne Pathogen Genome project will release more data. Technological advances made it possible to sequence pathogen genomes rapidly with portable devices such as MinION. As two landmark technologies of genome editing and immune engineering - CRISPR/Cas9 and CAR-T - had critical milestones, there will be new and exciting advances in gene therapy. The health industry is poised to enter the next phase of digitization: a phase of hyper-personalization. Hyper-personalization already infiltrated social networks contributing to political polarization. It might lead to mixed results in retail - that hopes to use ur biometric data for better sales. But the more personalized and precise healthcare is, the better it is for all of us.  Virtual and digital worlds will continue to blur leading to marketplace consolidation. And there will be also an increase in consolidation in healthcare, life insurance, gig economy and science.
It will be even more challenging to succeed as an entrepreneur. Big business will continue to getting bigger, swallowing up the resources, market share, and consumer support that used to be more evenly distributed among all types of companies. But the stars may be aligning up for new breakthroughs, entrepreneurs will rise up, come up with new ingenious ideas and be the driving force behind economic growth.
Every intelligent entity - whether human or machine - depends not only on the configurations of its neurons, but also connections between itself and others entities, optimized for efficient exchange of information. Hence, better human networks providing training and feedback from others will lead to both smarter humans and better AI. Just one example of how this could be leveraged in Healthcare.  The biggest obstacle for applying AI in Healthcare is the lack of good data available for computation. AI can predict heart attacks and strokes more accurately than a doctor - if there are good quality medical records. AI is better in analyzing visual information - if there are tenths of thousands of good quality images annotated for thousands of patients. But in most cases data we need for predicting outcomes is either too expensive to prepare or impossible to get - as it remains in the brains of individuals. And cages accurately monitoring food intake, activities and symptoms so far work only for mice.  In machine learning, efficiency can be improved if we pre-train algorithms on cheap and large datasets. This helps to pre-optimize the parameters for working with more expensive data. In the world of humans, efficiency of medical research could be improved if cheap large datasets focusing on particular medical questions could be easily collected. If clinical trials were more engaging and convenient, generating outcomes clinically meaningful to participants; if the participants could properly design studies by themselves, guided by Software as a Medical Device (SaMD) platform and their own devices (BYOD model), we could collect enough data to get to the next level. The success of an N-of-1 trial methodology, hindered by the operational complexity, largely depends on the collaboration of patients and knowledgeable parties. Large scale crowdsourced studies would need innovative software solutions inter-connecting participants and their treatment sequences. And the impact of this platform would be no less important than the race to build an artificial intelligence for everything. References Geirhos R, Janssen DH, Schütt HH, Rauber J, Bethge M, Wichmann FA. Comparing deep neural networks against humans: object recognition when the signal gets weaker. arXiv preprint arXiv:1706.06969. 2017 Jun 21.
Scuffham PA, Nikles J, Mitchell GK, Yelland MJ, Vine N, Poulos CJ, Pillans PI, Bashford G, Del Mar C, Schluter PJ, Glasziou P. Using N-of-1 trials to improve patient management and save costs. Journal of general internal medicine. 2010 Sep 1;25(9):906-13. Lillie EO, Patay B, Diamant J, Issell B, Topol EJ, Schork NJ. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?. Personalized medicine. 2011 Mar;8(2):161-73. Sackett DL. Clinician-trialist rounds: 4. Why not do an N-of-1 RCT?. Li J, Tian J, Ma B, Yang K. N-of-1 trials in China. Complementary therapies in medicine. 2013 Jun 30;21(3):190-4. Nyman SR, Goodwin K, Kwasnicka D, Callaway A. Increasing walking among older people: A test of behaviour change techniques using factorial randomised N-of-1 trials. Psychology & health. 2016 Mar 3;31(3):313-30. Federman DG, Shelling ML, Kirsner RS. N-of-1 trials: not just for academics. Journal of general internal medicine. 2011 Feb 1;26(2):115-. Duan N, Kravitz RL, Schmid CH. Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research. Journal of clinical epidemiology. 2013 Aug 31;66(8):S21-8. Joy TR, Monjed A, Zou GY, Hegele RA, McDonald CG, Mahon JL. N-of-1 (single-patient) trials for statin-related myalgia. Annals of internal medicine. 2014 Mar 4;160(5):301-10. Shaffer JA, Falzon L, Cheung K, Davidson KW. N-of-1 randomized trials for psychological and health behavior outcomes: a systematic review protocol. Systematic reviews. 2015 Jun 17;4(1):87.  “Everything is a test,” says Terry Pratchett in "I Shall Wear Midnight". Test - a way to establish if something is acceptable or not - is one of the world's most commonly performed procedures. Billions of years ago, Mother Nature established agile processes to test molecules, cells and organisms while creating them. Stone age toolmakers were checking if their tool was doing what it was supposed to do before using it. Ancient humans assessed others for civil service and education. How did all those tests evolved with time?  First tests were manual. Teachers read students' essays, engineers manually debugged their code, and a bridge was tested by walking an elephant across its length. The need to scale up led to automation. Educators got standardized tests and scanners. Software Quality Assurance professionals mastered machine-driven test cases and new test automation frameworks, teaming with software developers for better productivity. Medical testing evolved from tasting urine to sophisticated techniques and molecular diagnostics. At the forefront of test automation, electronic design engineers moved from asking "Does it work?" to "Are all elements present and working?" to "What could go wrong with this design?" focusing on defect-, circuit-, environment- and equipment-dependent variations.  What's next?
Will artificial Intelligence take over, testing software, hardware and interviewing people for the AI-proof jobs? Will the Internet of Things and Wearables improve the assessment of people's health, educational progress and behavior? Will medical diagnostics merge with therapeutics enhancing nature's proof-reading and error correction mechanisms? Will crowdsourcing evolve into testsourcing with everyone everywhere being a tester of something for public good? Perhaps. We have already started to explore AI-powered bots testing apps and see Artificial Intelligence challenging medical doctors on their home turf, so anything is possible.  When you hear about Artificial Intelligence (or AI, in cyber parlance), you may picture Ex Machina, Azimov's robots, Hal 2001, R2D2, Terminator, Siri, Alexa, Cortana, Watson or Google Brain. You may also recall flashy news headlines about AI predicting specific health events and outcomes more accurately than a doctor, navigating better than humans, outperforming government workers, bankers, trial jurors and psychologists. But there are many other new ideas out there.  This April, Y combinator's online startup school opened its doors to about 3,000 companies from 141 countries. 169 of them indicated "Artificial intelligence" as their industry and 62 of them "graduated" with a video-pitch, although many more mentioned AI technologies in one way or another. What are young startups working on?  Digital assistants and web robots (aka bots) are eating the world. Millennials are willing to rely on chatbots. Two out of five trust bots more than humans (from 30% in health-related information to 74% in regards to food). Most would prefer chatbots for communication compared to texting and email, phone calls and face-to-face interaction. The total number of messaging and voice chatbot startups in the class of 2017 was close to 50. The new generation is thinking ahead creating chatbot factories, nurseries, marketplaces - where bots can be auctioned off - and schools - where you can feel assured your bot is cared for. Schools for humans are also proposed - to learn how to better understand your bots.  By 2020 30% of mobile/web browsing sessions will be done without a screen. Many startup school founders are looking at AI as an interface - to buy and sell (fashion items, stocks trading and sports betting, real estate, whatever), get help with mundane work, learn new languages, improve our personal style, save the planet, read news, stay energized, eat well and have a better sleep.
One stealth startup even wanted to convert anything we touch to artificial intelligence, Asgard proposed to use AI technology to discover technologies, and Nudge to motivate learning artificial intelligence - getting closer to the 3rd generation AI. Perhaps the next step is to create artificial intelligence tools to develop the next generation artificial intelligence tools. The future is AI, but we are still competitive. |
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