- This is a preview of the AI in Medical Diagnosis research report from Business Insider Intelligence.
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AI is rocking medical diagnosis with its potential to incite drastic improvements to hospital processes. AI can process images and patient health records with more accuracy and expediency than humans are capable of, lessening physician workload, reducing misdiagnosis, and empowering clinical staff to provide more value. 
While early moving hospitals are already extracting value from AI in medical diagnosis, most US hospitals are at the very early stage of the AI transformation curve — and they risk falling behind if they don't move now.
In this report, Business Insider Intelligence examines the value of AI applications in three high-value areas of medical diagnosis — imaging, clinical decision support, and personalized medicine — to illustrate how the tech can drastically improve patient outcomes, lower costs, and increase productivity.
We look at US health systems that have effectively applied AI in these use cases to illustrate where and how providers should implement AI. Finally, we examine how a leading US health system validates AI partners and internally organizes its AI strategy to offer provider organizations a template for AI innovation.
The companies mentioned in this report are: Aidoc, Allscripts, Amazon, Arterys, Boston Gene, Cabell Huntington Hospital, Cerner, Cleveland Clinic, Epic, Geisinger Health System, Google, HCA Healthcare, IBM, iCAD, IDx, Intermountain Healthcare, Johns Hopkins, Meditech, Microsoft, Mount Sinai, NorthShore University HealthSystem, Oak Street Health, Stanford University, Tempus, UCI Health System, Unanimous AI, Verily, Viz.ai, and Yale New Haven Hospital.
Here are some of the key takeaways from the report:
- The use of AI in diagnostic imaging, clinical decision support, and precision medicine offers the greatest cost savings and efficiency opportunities across hospitals.
- Most US hospitals haven't implemented AI, and risk missing the tech's gain if they don't develop an effective AI strategy.
- Early moving health systems are already reaping the reward of AI in medical diagnosis by improving outcomes, driving efficiency, and reducing costs.
- The winning strategies employed by hospitals that are undergoing an AI transformation reveal how to best capture the opportunity. These strategies highlight the need for an AI strategy underpinned by talent, data aggregation techniques, and partnerships with external vendors
In full, the report:
- Outlines how AI is disrupting medical diagnosis in US hospitals.
- Details the three key AI use cases within medical diagnosis that will create the most value.
- Identifies transformation strategies US hospitals can leverage to deploy AI effectively.
- Predicts how the use of AI will evolve within clinical decision support, diagnostic imaging, and precision medicine.
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