The global market for artificial intelligence (AI) in healthcare is currently estimated at $1.9 billion, generated largely by sales in the workflow solutions market segment, followed by population health management. The market is expected to keep pace with the total AI market growth, reaching $11.4 billion by 2024. This is according to market researcher Kalorama Information’s new report, “The Market for Artificial Intelligence (AI) in Healthcare.”
Population health, workflow improvements, diagnostics, and risk assessment are among the areas where artificial intelligence is already being employed. Some examples of AI technologies detailed in the report include the following:
- – GE Healthcare Hospital Command Centers: GE Healthcare is collaborating with hospital service providers in Europe and the U.S. to implement an AI-powered command center to effectively and efficiently synchronize all elements of a patient’s hospital experience. Advanced algorithms will help staff to anticipate and resolve bottlenecks in care delivery before they occur, recommending actions to enable faster, more responsive patient care and better allocation of resources.
- – Sensely “Avatar Nurse:” This startup company uses AI to power “Molly,” a virtual health assistant. Molly can help connect patients with their healthcare team, guide them to resources that are helpful for their current needs, use speech recognition to answer patient questions with natural language, and connect with their health records for a truly personalized experience.
- – Google Deep Mind: What physicians are making decisions based on is often a black box. Deep Mind’s model provides the clinical information that was most important in making its predictions of deteriorating kidney function and provides predicted future results for several relevant blood tests. This information may help clinicians understand the reasoning behind the AI-enabled alert and anticipate future patient deterioration. Google’s DeepMind applied AI technology to a comprehensive deidentified electronic health record dataset collected from a network of over a hundred US VA sites. The research shows that the AI could accurately predict acute kidney injury in patients up to 48 hours earlier than it is currently diagnosed. Importantly, the model correctly predicted 9 out of 10 patients whose condition deteriorated so severely that they then required dialysis.
- – Copan Diagnostics AI Culture Plate Analysis: Copan’s Phenotypic Colony Recognition algorithm examines colonies present on the plate and compares them against a library of thousands of colony images to match the phenotypic characteristics to assign a predictive value. The physician confirms the presumptive ID assigned by the software and then performs further workup.
Kalorama’s report, “The Market for Artificial Intelligence (AI) in Healthcare,” has more examples of the uses of AI in healthcare.