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?
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.
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“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.
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.
Cute animals are the perfect distraction from our hectic lives.
Videos and images of cats, dogs and other animals make up the most viewed content on the web. Science proves it: looking at these images can actually boost productivity, motivation, focus, and lift mood.
Real-life interactions are even more powerful. Pets provide social and emotional support, boost self-esteem, conscientiousness and make us happy.