Ultra-modern medicine: Examples of machine learning in healthcare

July 4, 2019
Updated: November 1, 2019
Written by Mike Thomas

The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning (a subset of artificial intelligence) plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. As computer scientist Sebastian Thrum told the New Yorker in a recent article titled “A.I. Versus M.D., “Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful.”

Machine Learning Applications in Healthcare

Machine learning has virtually endless applications in the healthcare industry. Today, machine learning is helping to streamline administrative processes in hospitals, map and treat infectious diseases and personalize medical treatments.

Despite warnings from some doctors that things are moving too fast, the rate of progress keeps increasing. And for many, that’s as it should be. "AI is the future of healthcare,” Fatima Paruk, CMO of Chicago-based Allscripts Analytics, said in 2017. She went on to explain how critical it would be in the ensuing few years and beyond — in the care management of prevalent chronic diseases; in the leveraging of “patient-centered health data with external influences such as pollution exposure, weather factors and economic factors to generate precision medicine solutions customized to individual characteristics”; in the use of genetic information “within care management and precision medicine to uncover the best possible medical treatment plans.”

“AI will affect physicians and hospitals, as it will play a key role in clinical decision support, enabling earlier identification of disease, and tailored treatment plans to ensure optimal outcomes,” Paruk explained. “It can also be used to demonstrate and educate patients on potential disease pathways and outcomes given different treatment options. It can impact hospitals and health systems in improving efficiency, while reducing the cost of care."

Here are five applications of machine learning in healthcare, along with some companies that harness its power to benefit patients and providers.

 

Smart Records

quotient health machine
Quotient Health

Quotient Health

Location: Denver, Colorado

How it’s using machine learning in healthcare: With the help of machine learning, Quotient Health developed software that aims to “reduce the cost of supporting EMR [electronic medical records] systems” by optimizing and standardizing the way those systems are designed. The ultimate goal is improved care at a lower cost.

Industry impact: The company’s founding CEO Jason Michael O'Rourke recently spoke about "Healthcare’s Disruptive Next Generation" at the YJP CEO Healthcare Symposium in New York.

 

KenSci machine learning health tech
KenSci

KenSci

Location: Seattle, Washington

How it’s using machine learning in healthcare: KenSci uses machine learning to predict illness and treatment to help physicians and payers intervene earlier, predict population health risk by identifying patterns and surfacing high risk markers and model disease progression and more.

Industry impact: KenSci recently partnered with healthcare consulting firm T3K Health to focus on helping caregivers harness AI and machine learning for health records and workflow.

 

Ciox Health machine learning healthcare
Ciox Health

Ciox Health

Location: Alpharetta, Georgia

How it's using machine learning in healthcare: Ciox Health uses machine learning to enhance "health information management and exchange of health information," with the goal of modernizing workflows, facilitating access to clinical data and improving the accuracy and flow of health information. 

Industry impact: The company recently partnered with Chicago-based Northwestern Memorial Healthcare "to bring efficiency and transparency to Northwestern Memorial’s release of information (ROI) process."

 

Medical Imaging and Diagnostics

path ai machine learning
PathAI

PathAI

Location: Cambridge, Massachusetts

How it’s using machine learning in healthcare: PathAI’s technology employs machine learning to help pathologists make quicker and more accurate diagnoses as well as  identify patients that might benefit from new types of treatments or therapies.

Industry impact: In 2017 the company raised an additional $11 million in a Series A funding round, which brought its total bank to $15 million.

 

Quantitative Insights medical machine learning
Quantitative Insights

Quantitative Insights

Location: Chicago, Illinois

How it’s using machine learning in healthcare: Quantitative Insights want to improve the speed and accuracy of breast cancer diagnosis with its computer assisted breast MRI workstation Quantx. The goal: better results for patients via improved diagnoses by radiologists.

 

microsoft machine learning healthtech
Microsoft

Microsoft

Location: Redmond, Washington

How it’s using machine learning in healthcare: Microsoft's Project InnerEye employs machine learning to differentiate between tumors and healthy anatomy using 3D radiological images that assist medical experts in radiotherapy and surgical planning, among other things. 

Industry impact: InnerEye is used in the United Kingdom to produce 3D imaging that pinpoints the precise location of tumors and enables more accurately targeted radiotherapy.

 

Drug Discovery and Development

pfizer machine learning
Pfizer

Pfizer

Location: New York, New York

How it’s using machine learning in healthcare: With the help of IBM’s Watson AI technology, Pfizer uses machine learning for immuno-oncology research about how the body’s immune system can fight cancer. 

Industry impact: According to fiercebiotech.com, Pfizer expanded its collaboration with Chinese tech startup XtalPi “to develop an artificial intelligence-powered platform to model small-molecule drugs as part of its discovery and development efforts.
The project will combine quantum mechanics and machine learning to help predict the pharmaceutical properties of a broad range of molecular compounds.”

 

insitro machine learning health tech
insitro

insitro

Location: San Francisco, California

How it’s using machine learning in healthcare: Machine learning and data science combined with advanced laboratory technology are helping recent startup insitro develop drugs with the goal of more quickly curing patients at a lower cost. 

Industry impact: Insitro’s list of top-tier investors includes ARCH Venture Partners, Foresite Capital, a16z, GV and Third Rock Ventures.

 

Biosymetrics machine learning
Biosymetrics

Biosymetrics

Location: Boston, Massachusetts

How it’s using machine learning in healthcare: Via its machine learning platform Augusta, Biosymetrics “enables customers to perform automated ML and data pre-processing,” which improves accuracy and eliminates a time-consuming task that’s typically done by humans in different sectors of the healthcare realm, including biopharmaceuticals, precision medicine, technology, hospitals and health systems.

Industry impact: BioSymetrics’s recently announced Strategic Advisory Board will work with company leadership team to advance healthcare and R&D innovation via machine learning and integrated analytics.

 

Medical Data

Precision Health AI Machine Learning
Precision Health

Concerto Health AI

Location: New York, New York

How it’s using machine learning in healthcare: Concerto Health AI uses machine learning to analyze oncology data, providing insights that allow oncologists, pharmaceutical companies, payers and providers to practice precision medicine and health. 

Industry impact: Its recently launched platform, Eureka Health Oncology, uses deep data from electronic medical records to offer AI solutions for the management, delivery and use of clinical data.

 

Orderly Health Machine Learning
Orderly Health

Orderly Health

Location: Denver, Colorado

How it’s using machine learning in healthcare:  Orderly Health thinks of itself as “an automated, 24/7 concierge for healthcare” via text, email, Slack, video-conferencing. The company’s goal is to help employers and insurers save time and money on healthcare by making it easier for people to understand their benefits, locate the least expensive providers. enabling employees or members to understand their benefits and find lowest cost providers.

Industry impact: Orderly joined TreeHouse Health’s stable of startups in 2017 and landed a grant months later to expand its operations.

 

MD Insider machine learning
MD Insider

MD Insider

Location: Santa Monica, California

How it’s using machine learning in healthcare: The MD Insider Platform uses machine learning to better match patients with doctors.

Industry impact: Not long ago, the company partnered with ConsumerMedical to enhance the latter’s physician referral capabilities.  

 

Treatment and Prediction of Disease

Beta Bionics
Beta Bionics

Beta Bionics

Location: Boston, Massachusetts

How it’s using machine learning in healthcare: Beta Bionics is developing a wearable “bionic” pancreas it calls the iLet, which manages blood sugar levels around the clock in those with Type 1 diabetes.”

Industry impact: The company was recently awarded an SBOR grant valued at up to $2 million by the NIH-affiliated National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

 

Prognos machine learning
Prognos

Prognos

Location: New York, New York

How it’s using machine learning in healthcare: The company claims its Prognos Registry contains 19 billion records for 185 million patients. With an assist from machine learning, Prognos’s AI platform facilitates early disease detection, pinpoints therapy requirements, highlights opportunities for clinical trials, notes gaps in care and other factors for a number of conditions.               

Industry impact: Last year Prognos reportedly raised $20.5 million in a Series C funding round. The backing came from insurance companies, drug manufacturers and venture capitalists.

 

Berg Machine Learning Health
Berg

Berg

Location: Framingham, Massachusetts

How it’s using machine learning in healthcare: Powered by AI, Berg’s Interrogative Biology platform employs machine learning for disease mapping and treatments in oncology, neurology and other rare conditions. Using patient-driven biology and data, the company allows healthcare providers to take a more predictive approach rather than relying on trial-and-error.

Industry impact: Berg’s director of digital health, Vijetha Vemulapalli, recently took part in the Artificial Intelligence in Healthcare Conference in Boston.

 

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