The healthcare artificial intelligence (AI) market is going to grow enormously and has the potential to increase the efficiency of all healthcare processes and procedures; it may even reduce physician burnout (see: Cerner Offers AI Tool to Combat Physician EHR Burnout). A recent article discussed how the healthcare AI market will increase in value (see: Healthcare AI market expected to surge from $2.1 to $36.1 billion by 2025). It's a long and complex article so read it in its entirely if you're interested. Below is a small excerpt from it:
The healthcare artificial intelligence market is expected to grow from $2.1 billion in 2018 to $36.1 billion by 2025, at a compound annual growth rate of 50.2 percent during the forecast period....Increasingly large and complex data sets available in the form of big data, and a growing need to reduce increasing healthcare costs, are driving the growth of the market. Improving computing power and the declining cost of hardware are other key factors in the projected market growth. Despite the emerging data/technology picture, challenges remain. There's a reluctance among medical practitioners to adopt AI-based technologies, and a lack of skilled workforce and ambiguous regulatory guidelines for medical software also conspire to restrain the growth of the healthcare AI market....AI has already penetrated healthcare in areas such as radiology and cancer detection....Perhaps due to this, some are worried that AI is coming to replace their jobs -- fears that experts say are largely unfounded. Artificial intelligence and machine learning algorithms tend to rely on large quantities of data to be effective, and that data needs human hands to collect it and human eyes to analyze it. And since AI in healthcare is currently utilized mainly to aggregate and organize data -- looking for trends and patterns and making recommendations -- a human component is very much needed.
I have recently blogged about how a relatively high percentage of jobs in healthcare will be immune from automation (i.e., obsolescence on the basis of AI). This is because many of them are related to personal services that only humans can perform (see: Which Healthcare Jobs Are Safe from Replacement by Automation or Robots?). One of the reasons that AI has been deployed relatively early in radiology is that image analysis software was already highly developed and radiologists were also attune to the idea of using automation to relieve some of the more tedious tasks in the field. One would think that pathology would be a close second in automated image analysis but progress has been hampered by the slow deployment of digital pathology due, in part, to regulatory constraints.
The earliest AI deployments in healthcare will undoubtedly take place in areas where we already know roughly how to provide better care and reduce expenses. One example would be the goal of reducing the number of premature patient discharges, resulting in readmissions for which hospitals are financially penalized (see: Talking to Patients Helps Reduce Hospital Readmissions; Keeping Tabs on Patients Post-Discharge Via Telemonitoring; Home Monitoring of Discharged Patients by Hospitals for Cost Savings). For the future, AI systems will be constantly monitoring the status of hospitalized patients, calculating in the background when they can be discharged and what type of services they will require at home.
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