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Ultra-modern medicine: Examples of machine learning in healthcare

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.

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.'

View Jobs + Learn More 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.

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, 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.”  

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.

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.

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.

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Banks love to brag about how many data scientists they’re hiring and their shiny machine-learning “centers of excellence.” In the 2018 JP Morgan Chase annual report, CEO Jamie Dimon said the company had gone “all in” on artificial intelligence, adding that artificial intelligence and machine learning were “being deployed across virtually everything we do.” Not to be outdone, HSBC has opened multiple “data and innovation labs” around the world, in order to build artificial intelligence tools that can take in the bank’s more than 10 petabytes of data.

As the BOE explains, “machine learning” isn’t always substantially different from the statistical models banks have used for decades, like the credit scores they develop to predict the likelihood a customer will default on a loan, or the models bank use to predict whether a particular debit or credit card transaction was fraudulent.

As legal scholar Spiros Simitis wrote in 1987, “Information processing is developing [into] long-term strategies of manipulation intended to mold and adjust individual conduct.” And as Shoshana Zuboff points out in The Age of Surveillance Capitalism: The Fight for Human Future at the New Frontier of Power, the ultimate purpose of machine learning and artificial intelligence by corporations is often to induce “behavior modification at scale,” ideally in ways that are subtle enough that they happen “outside of our awareness, let alone our consent.”

Banks are particularly incentivized to go whole-hog on machine learning because of all the ways financial products vary from most of the other things we “buy.” Consumers rarely pay for bank products like checking accounts or credit cards with one lump sum upfront, the way we buy ice cream or shoes.

For example, the Consumer Financial Protection Bureau has found that the typical credit card in the United States has more than 20 distinct “price points”—separate fees or interest rates contingent on specific ways you might use the card—a level of complexity that can raises lots of opportunities for “gotcha” moments induced via behavior modification.

Between advances in machine-learning technology and the limits of existing law, banks can toggle each customer’s particular payment options, based on all that individualized data, to increase the likelihood that the customer will miss a payment and get hit with a late fee, while calibrating that it happens just infrequently enough that customer won’t get fed up and close their account.

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