Trending data in AI
Software that incorporates artificial intelligence (AI) and a subset of AI known as machine learning (ML), is increasingly becoming an important part of a number of medical devices. Potentially, one of the greatest benefits of ML resides in the ability to create new and important insights from the vast amount of data generated during the delivery of healthcare every day.
Size of the global AI market
$11B in 2021
~$188B forecast by 2030
37% compound annual growth rate (2022–2030)
~$150B savings by using AI applications on annual U.S. healthcare costs by 2026
<10% of healthcare organizations fully integrate AI technologies into business processes today
Authorized medical devices
Over the past decade, the Food and Drug Administration (FDA) has reviewed and authorized a growing number of devices legally marketed [via 510(k) clearance, granted De Novo request, or approved PMA] with ML across many different fields of medicine. And, the trend is expected to continue. Of the 521 devices on the FDA’s updated list, 448 are Radiology & Cardiology devices:
- 75% are in Radiology: 391 devices
- 11% are in Cardiology: 57 devices
- 3% are in Hematology: 15 devices
- 3% are in Neurology: 14 devices
- 1% come from other disciplines
Note: The FDA list is based on publicly available information and is not a comprehensive resource of approved AI/ML-enabled medical devices. See below for a full list of sources.
Learn more about machine learning and AI in medical diagnostics by checking out a report from the U.S. Government Accountability Office.
Potential impact & lack of widespread use
Diagnostic errors are some of the most common, catastrophic and costly errors.
>12M Americans impacted by diagnostic errors each year
$100B+ in costs associated with those errors
An accurate medical diagnosis is a critical first step in care delivery, and it significantly improves a patient’s overall chance for positive outcomes. While still in the early stages of implementation, ML has the potential to provide more accuracy in diagnostic results—saving time, money and lives.
While researchers continue to expand AI and ML capabilities in diagnostics, the technology has generally not been widely adopted. If faces a number of challenges limiting broader use because:
- Performance has not been widely proven in diverse clinical settings
- There is a lack of familiarity about how ML would fit within & enhance workflows
- There are gaps in regulatory guidance & requirements
- Implementation & maintenance is costly
Impact to patient outcomes
The Medical Device Management (MDM) team at HealthTrust continuously monitors the market for new technology and trends, and artificial intelligence is one of those emerging disciplines, shares Chris Stewart, VP of Medical Device Management at HealthTrust. The MDM team can assist members with analysis of acquisition cost, service and expected value in the application of AI.
“Many of these initial AI product platforms can assist physicians with presurgical planning that could lead to an aligned and customized delivery of supplies and implants. This should reduce the number of implant options and surgical instruments to be processed, which should translate to cost avoidance, waste mitigation and patient safety,” adds Stewart.
Email thesource@healthtrustpg.com to let the Medical Device Management (MDM) team at HealthTrust know how they can assist your organization or to share the ways in which you use AI and/or ML in your clinical practice.
1. Source: Artificial intelligence (AI) in healthcare market size worldwide from 2021 to 2030
2. Source: Celebrate the Latest ‘Impact of AI on Healthcare Savings’ Report with Cautious Optimism
3. Source: The rise of artificial intelligence in healthcare applications
4. Source: Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices | FDA
5. Source: Society to Improve Diagnosis in Medicine
6. Source: U.S. Government Accountability Office
