Methods:
Terminology: Helps define your
focus and ask better questions.
Machine Learning market segments can include
Preclinical
machine learning, Clinical, Consumer healthcare including wearables .
Segments are not always mutually exclusive.
Reimbursement may be a factor for clinical machine learning
startups.
Market analysis/Commercialization strategy:
Funding opportunities:
Where are venture capitalists investing?
Which companies want to acquire technologies? | Look into
Merger & Acquisition histories and joint venture partners,
availability of grants and competitions.
IT companies
such as Apple, Google and IBM are actively investing in machine
learning.
Big pharmas such as GlaxoSmithKline, Merck, Johnson & Johnson,
Sanofi seem particularly active.
Venture Capital
Healthcare is the hottest category for Artificial Intelligence
deals says CB Insights.
SBIR Small Business Innovation Research awards from NIH
https://projectreporter.nih.gov/reporter.cfm
Machine learning has 1,189 current awards and deep learning 72.
Information sources
for AI/Machine Intelligence startups:
CB Insights: identified
over 100 companies that are applying machine learning algorithms
and predictive analytics to reduce drug discovery times, provide
virtual assistance to patients, and diagnose ailments by
processing medical images”
https://www.cbinsights.com/research/artificial-intelligence-startups-healthcare/
AngelList
https://angel.co/machine-learning
has 3,061 machine
learning startups.
CrunchBase
https://www.crunchbase.com/
has 6,304 machine
learning organizations.
Marketing and Distribution
Current and emerging channels: Talk to distributors and
suppliers, sales people.
Human Capital
Salaries, Job openings and growth. Look at Indeed.com,
LinkedIn, Monster.com.
Challenges, Bottlenecks and Risks Availability of data
fragmented among diverse EHRs, lab and imaging systems, individual
physician practices and clinics, health insurance claims. HIPAA
and privacy concerns, interoperability, patient trust "algorithms
themselves are a far easier problem to solve than the cultures of
data protection, commercial blockage of data sharing, and limited
capacity for cleansing assets." Machine Learning in Healthcare
May 2017, Merck Boston MA https://www.machine-learning-healthcare.com/
FDA and machine learning
Machine Learning market segments can include
Preclinical machine
learning, Clinical, Consumer healthcare including wearables .
Segments are not always mutually exclusive.
Reimbursement
may be a factor for clinical machine le
HIPAA Consider HIPAA
when using machine learning, Medstack, 2017 Nov
https://medstack.co/blog/consider-hipaa-using-machine-learning/
Intellectual Property and
Regulatory
“Artificial
Intelligence (AI) investment has turned into a race for patents
and intellectual property (IP) among the world’s leading tech
companies” Louis Columbus, McKinsey's State of Machine
Learning and AI, Forbes 2017 https://www.forbes.com/sites/louiscolumbus/2017/07/09/mckinseys-state-of-machine-learning-and-ai-2017/#7657c10975b6
Free patent databases are not a substitute for legal advice or
professional patent searches. Learn about
intellectual property protection. Have an advisory team to keep
you up to date.
Ethics can’t be neglected.
Machine Ethics Wikipedia
https://en.wikipedia.org/wiki/Machine_ethics
Marketing Research Association Standards
https://www.insightsassociation.org/issues-policies/mra-code-marketing-research-standards
Best practices, legal and ethics Strategic and Competitive
Intelligence ethics
http://www.scip.org/?page=CodeofEthics
Competitive
Intelligence and Market Research limitations
Requires resources and
time. No single source and rarely complete numbers.
No confidential data or proprietary information. Not
everything is on the web, for free. Some questions may have no
answer. Move on to new topics.
Moravec’s Paradox: contrary
to traditional assumptions, high-level reasoning requires very
little computation, but low-level sensorimotor skills require
enormous computational resources …As
Moravec writes, "it is comparatively easy to make computers
exhibit adult level performance on intelligence tests or playing
checkers, and difficult or impossible to give them the skills of a
one-year-old when it comes to perception and mobility."[1]
Similarly, Marvin Minsky emphasized that the most
difficult human skills to reverse engineer are those that are unconscious.
"In general, we're least aware of what our minds do best," he
wrote, and added "we're more aware of simple processes that don't
work well than of complex ones that work flawlessly."[2] Wikipedia accessed
2018 May
https://en.wikipedia.org/wiki/Moravec%27s_paradox
My
search for the best computer vision API
https://medium.freecodecamp.org/chihuahua-or-muffin-my-search-for-the-best-computer-vision-api-cbda4d6b425d
Next steps:
Focus on challenging -- but not TOO
challenging problems. Identify promising white space and
topics in demand by potential funders.
Continue to collect relevant terms, refine questions and
look for people to
interview. Monitor
whether, and when to pivot.
Network!
References:
see poster at Market Research
Machine Learning Startups
Machine Learning Terminology Cambridge Healthtech,
Data Science and Machine Learning Glossary & Taxonomy
http://profit.cambridgehealth.com/genomicglossaries/content/datascience.asp
Glossary of common machine learning, statistics and data
science terms 2013-2017
https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/
Google Machine learning glossary 2017
https://developers.google.com/machine-learning/glossary/
General terms and ones specific to TensorFlow
HealthIT analytics
https://healthitanalytics.com/news/machine-learning-in-healthcare-defining-the-most-common-terms
Kohavi, Ron, Glossary of
terms, Machine Learning, 30, 271- 274, 1998, 45 definitions.
http://ai.stanford.edu/~ronnyk/glossary.html
Gartner, Data Science and Machine Learning Hype Cycle The hype
around data science and machine learning has increased from
already high levels in the past year. Data and analytics leaders
should use this Hype Cycle to understand technologies generating
excitement and inflated expectations, as well as significant
movements in adoption and maturity.
https://www.gartner.com/doc/3772081/hype-cycle-data-science-machine
Healthcare
Applications
Tom Davenport, Revolutionizing Radiology with Deep Learning at
Partners Healthcare – and Many Others, Forbes 2017
https://www.forbes.com/sites/tomdavenport/2017/11/05/revolutionizing-radiology-with-deep-learning-at-partners-healthcare-and-many-others/#6a5131bb5e13
Daniel Fagella, Machine Learning Healthcare Applications – 2018
and beyond, TechEmergence, 2018 Jan
https://www.techemergence.com/machine-learning-healthcare-applications/
Daniel Fagella, Where Healthcare’s Big Data Actually comes
from, TechEmergence 2018 Jan.
https://www.techemergence.com/where-healthcares-big-data-actually-comes-from/
Google, Partnering on Machine Learning in Healthcare May 2017
https://blog.google/topics/machine-learning/partnering-machine-learning-healthcare/
Google Brain, Research at Google: Healthcare
https://research.google.com/teams/brain/healthcare/
Future of Healthcare
Artificial intelligence & Machine Learning
HealthIT Analytics, Healthcare data access is biggest
artificial intelligence bottleneck
https://healthitanalytics.com/news/healthcare-data-access-is-biggest-artificial-intelligence-bottleneck
McKinsey, Artificial Intelligence The Next Digital Frontier
https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx
McKinsey, A Machine-learning approach to venture capital, 2017
June
https://www.mckinsey.com/industries/high-tech/our-insights/a-machine-learning-approach-to-venture-capital
IBM and MIT Bet That Materials and Quantum Advances
Will Supercharge AI
Will
Knight and Elizabeth
Woyke MIT Technology Review,
September 7, 2017
https://www.technologyreview.com/s/608810/ibm-and-mit-bet-that-materials-and-quantum-advances-will-supercharge-ai/
Funding AI & Machine Learning Machine learning startups, AngelList
https://angel.co/machine-learning
MIT Technology Review Google is backing an eclectic group of
startups that use AI in healthcare, 2017 Nov
https://www.technologyreview.com/the-download/609301/google-is-backing-an-eclectic-group-of-startups-that-use-ai-in-health-care/
Bensci, 16 pharma
companies using AI in drug discovery, Feb 2018
https://blog.benchsci.com/pharma-companies-using-artificial-intelligence-in-drug-discovery
CB Insights, Healthcare remains the
hottest AI Category for deals 2017 Apr
https://www.cbinsights.com/research/artificial-intelligence-healthcare-startups-investors/
We identified over 100 companies that are applying machine
learning algorithms and predictive analytics to reduce drug
discovery times, provide virtual assistance to patients, and
diagnose ailments by processing medical images, among other
things. CB Insights
https://www.cbinsights.com/research/artificial-intelligence-startups-healthcare/
10 Venture Capital databases for startup data
https://www.nanalyze.com/2017/02/venture-capital-databases-startup-data/
Don’t let the
fear of your idea being stolen hold you back, Stephen Key, 2015
https://www.entrepreneur.com/article/242069
Acknowledgments:
Many thanks to
Tonya Urquizo and Parmalee Eastman
for collaboration and wise advice.
|