Mapping A.I. in Healthcare: Early Stage Startups VCs Should Keep An Eye On
Healthcare has the highest number of VC-backed artificial intelligence startups
In my previous article, I wrote about the broken healthcare system in the U.S. and I discussed how exactly A.I. will help lower the cost of healthcare for governments, insurance companies, and healthcare providers, which subsequently will reduce your medical insurance premiums.
In summary, these are the different ways A.I. will shape the future of healthcare:
- Data Analytics: Applying machine learning on the massive amount of medical data from healthcare providers and insurers to find new patterns between symptoms and diseases
- Drug Discovery: Applying machine learning to the research in academia as well as known chemical characteristics to find new candidates for new drugs, find patients for trials, etc.
- Diagnostics: Use machine learning to categorize symptoms, lab results, medical images, etc. to diagnose the diseases
- Administrative: Over 30% of the healthcare costs in the U.S. is associated with administrative tasks such as insurance claims, insurance pre-authorization, going after unpaid bills, negotiations between insurers and providers, appointments, record keeping, etc.
- Virtual Doctor/Nurse: Use A.I. to gather your symptoms and vital signs, pre-diagnose you and maybe refer you to a specialist and reduce the hospital visits.
- Lifestyle: Better nutrition and an active lifestyle will lead to a huge cut on healthcare costs, let chatbots inspire you to live healthier.
- Mental Health: Hundreds of millions of people suffer from some form of mental illness, moderate or severe, and many of them simply do not know of their illness or do not want to/cannot afford to see a psychiatrist. Chatbots may help to find patterns in the way they talk, react and pre-diagnose them or help them with treatment.
According to CB Insights, more than $2.14Bn of venture capital has poured into healthcare A.I. companies in the past 5 years across more than 300 deals. Most active investors of this space are AME Cloud Ventures, Data Collective, Vinod Khosla’s Khosla Ventures, and Google Ventures (GV).
While there are already unicorns in this space, namely Flatiron Health, Benevolent, and iCarbonX, and many companies who have raised 10s of millions of dollars in funding, I was interested to map out the early stage startups in this space.
I screened more than 200 companies in the A.I. Healthcare space (thanks to CB Insights, Crunchbase Pro, YCombinator, and other sources) and filtered out the ones that:
- Raised Less than $10M in Total Funding
- Are Headquartered in the U.S.
- Are Active (and Not Acquired) Based on Social Media, Hiring, etc.
This left me with exactly 70 startups to work on. Then I scored all these 70 startups based on the below criteria (0–10 scale):
- Customers have a real problem? (Product/Market Fit Risk)
- Lots of people have this problem? (Market Size)
- Healthy competition? (low score if no competition or competition from established companies or many other startups, high score if there are some players but not very strong or exactly focused on that customer segment)
- High barrier to entry? (Is the technology based on hooking existing solutions together or requires building something from the ground up)
- The solution can make a profitable business? (Business model is scalable? Is the correct part of the funnel? Can take a large pie?)
- Traction? (Sales, Pilots, Partnerships, Media Coverage)
- Is the timing right? (Why now? what technological advancement or market shift makes it the right time?)
- Is the team capable? (I looked into the LinkedIn profiles of all the employees of all these 70 startups. Main things I look for: experience running a previous startup, experience working in the same field or in the companies they want to disrupt, etc.)
- Current Investors? (Good investors currently on-board means they can risk further funding)
Using the above scoring system, I calculated a total score for each startup (which was a function of their scores for each category), and ranked all these companies. Here I will present the top 5 investment opportunities for each category (raised angel, raised seed, raised series A).
Top Angel-Funded Startups:
1 — Billion to One (Score=436): Prenatal Genetics Testing Using Blood Test
Category: Diagnostics — Funding: $120K — Where: SF Bay Area — YC Alumni.
2 — ShiftDoc (Score=313): On-demand Staffing Marketplace for Private Practitioners
Category: Administrative — Funding: $120K — Where: SF Bay Area — YC Alumni.
3 — zPREDICTA (Score=203): Simulation Platform for Drug Discovery
Category: Drug Discovery — Funding: $120K — Where: SF Bay Area — YC Alumni — Great Oaks Ventures
4 — Glidian (Score=189): Insurance Preauthorization of Treatments from Provider Side
Category :Administrative — Funding: ~$100K — Where: SF Bay Area — Alchemist Alumni.
5 — Macro Eyes (Score=180): Multiple products for reducing no shows, device selection, etc.
Category: Administrative — Funding: $100K — Where: Seattle, WA — Investors: Bill & Melinda Gates Foundation
Other startups in the angel stage that were analyzed and were close contenders: BeholdHealth, Tensor, Kangaroo Health, docCheer, HealthWiz, iSono Health, Able Health, Sunu, WoundMe, Invio, Nimblr, FitBliss, Simplifimed, Sunrise Health, Totemic Labs, EnsoData, Autonomous Healthcare, Clipboard Health , VisExcell, TinyKicks.
Top Seed-Funded Startups
1 — Athelas (Score=660): Rapid at-home Blood Test Diagnostics
Category: Diagnostics — Funding: $3.6M — Where: SF Bay Area — YC Alumni — Investors: Sequoia, Dorm Room Fund
2 — Enzyme (Score=598): Help Life Sciences Companies with FDA/Compliance
Category: Administrative — Funding: $2M — Where: SF Bay Area — YC Alumni — Investor: Data Collective
3 — Viz (Score=445): Identify Anomalies in Brain Scans
Category: Diagnostics — Funding: $9.6M — Where: SF Bay Area — Pejman Nozad @Pear Launchpad—Investors: AME Cloud Ventures, Danhua Capital
4— Darmiyan(Score=445): Alzheimer’s Diseases Diagnostics from Brain Scans 15 Years Prior to Symptoms Appearing
Category: Diagnostics — Funding: $3.1M — Where: SF Bay Area — YC Alumni — Investors: Fenox Venture Capital, DHVC
5 — TwoXAR(Score=442): Find New Drug Candidates
Category: Drug Discovery — Funding: $4.3M — Where: SF Bay Area — StartX Alumni — Investors: Vijay Pande of Andreessen Horowitz
Other startups in the seed stage that were analyzed and were close contenders: Medal, Spring Health, Envisagenics, VitaGene, Lively, Oncora Medical, BodyPort, Cardiogram, iQuity, BloomAPI, Ava, CareSkore, Kaia, Proscia, TechCyte, Atomwise, NarrativeDx, PulseData, Picnic Health, DocSync, CloudMedX, PotBotics, Eko Devices, LexiGram, TypeZero, Care Angel, VEDA Data, Saykara, Datalog
Top Series-A Funded Startups
1 — Qrativ (Score=543): Drug Discovery For Rare Diseases
Category: Drug Discovery — Funding: $8.3M — Where: Cambridge, MA — Investors: Mayo Clinic, Matrix Partners
2 — NuMedii (Score=387): A.I. on Research Data for Discovery of Precision Medicine
Category: Drug Discovery — Funding: $5.5M — Where: SF Bay Area — Investors: Lightspeed, Claremont Creek
3 — Bay Labs (Score=294): Cardiovascular Imaging Diagnostics
Category: Diagnostics — Funding: $5.5M — Where: SF Bay Area — Investors: Data Collective, Khosla, Greenbox
4 — NextHealth (Score=244): Various Products to Shift Patient Behavior To Reduce Cost For Insurance Companies
Category: Misc. — Funding: $9.5M — Where: Colorado — Investors: Norwest Venture Partners
5 — Jvion(Score=142): Predicting Risk of Illnesses In the Future
Category: Data Analytics — Funding: $8.9M — Where: Georgia, LA — Investors: Unknown
Other startups in the series-A stage that were analyzed and were close contenders: Cogitavito, Care Predict, Buoy Health
I hope this article was useful for VCs and other professionals looking into the healthcare A.I. companies.
If you found this article useful, may I ask you to share it with others who may benefit on social media?
Also, Please feel free to get in touch through LinkedIn and I can share the mapping file.
