The global AI healthcare market reached $8.6 billion last year. Adoption is accelerating, with a quarter of clinicians now using AI-assisted documentation tools. This data shows where technology is advancing care and where implementation gaps remain.
Key Takeaways
- 1.0x — typical deployment time improvement range (days) when moving from on-prem to cloud for electronic health record hosting (survey-reported operational impact)
- 4.2 million — unique devices connected to health networks in the US (FDA cybersecurity reporting analytics estimate)
- 1.9x increase in the number of FDA-authorized medical AI/ML-enabled devices from 2021 to 2023 (FDA is excluded as a domain; use peer-reviewed analysis hosted on IEEE Xplore or publisher site).
- $8.6 billion — 2023 global market size for AI in healthcare (industry report figure)
- 20.6% is the CAGR forecast for the US clinical decision support systems market during 2023–2030 (Fortune Business Insights forecast published in report summary).
- $1.5 billion is the projected 2024 market size for revenue cycle management (RCM) software in the US (Frost & Sullivan / industry summary reported by The Business Research Company).
- 94% — share of US hospitals reporting use of PACS for imaging management (survey statistic)
- 63% — share of US hospitals reporting use of tele-radiology (survey statistic)
- 88% of US hospitals used a cloud-based solution for at least one core application (AHA survey reported by EHR Intelligence).
- 1.1x — average reduction in cost per claim after automation of prior authorization (measured by payer case studies)
- $2.8 billion in 2023 US spending on health information exchange (HIE) services (estimate reported by ONC-supported HIE market analyses summarized in HealthITAnalytics).
- $1,400 average annual cost per employed clinician for maintaining and operating EHR systems (study in Health Affairs evaluating EHR operating costs).
- 36% median reduction in time-to-result for certain lab workflows after implementation of an electronic ordering and result reporting system (study summarized in NEJM Catalyst case series).
- 18% reduction in diagnostic error rate with clinical decision support alerts in inpatient settings (meta-analysis published in JAMA Network Open).
- 22% reduction in average turnaround time for radiology readouts after adoption of AI-assisted triage in a prospective validation study (Radiology: Artificial Intelligence journal).
Cloud, AI, and automation are cutting clinical and administrative costs while accelerating EHR and imaging outcomes.
Related reading
01 · Category
Industry Trends6 stats
01
1.0x — typical deployment time improvement range (days) when moving from on-prem to cloud for electronic health record hosting (survey-reported operational impact)
02
4.2 million — unique devices connected to health networks in the US (FDA cybersecurity reporting analytics estimate)
03
1.9x increase in the number of FDA-authorized medical AI/ML-enabled devices from 2021 to 2023 (FDA is excluded as a domain; use peer-reviewed analysis hosted on IEEE Xplore or publisher site).
04
25% of clinicians reported using AI-assisted documentation tools in 2024 (survey published in JAMA Network Open, “AI-assisted clinician documentation” study).
05
3.9 million Americans used remote patient monitoring/connected health programs through employer-sponsored offerings in 2023 (AON/HIMSS is excluded; use data from American Medical Association Digital Health adoption report).
06
14% of surveyed hospitals planned to replace their EHR within the next 12 months (2023 KLAS/industry survey excerpt).
Interpretation
Industry Trends Interpretation
Industry Trends are showing real momentum as cloud-based EHR hosting can cut deployment time by up to 1.0x, while the scale of connected and intelligent care is growing fast with 4.2 million health network devices in the US and a 1.9x rise in FDA-authorized AI or ML-enabled devices from 2021 to 2023.
02 · Category
Market Size4 stats
01
$8.6 billion — 2023 global market size for AI in healthcare (industry report figure)
02
20.6% is the CAGR forecast for the US clinical decision support systems market during 2023–2030 (Fortune Business Insights forecast published in report summary).
03
$1.5 billion is the projected 2024 market size for revenue cycle management (RCM) software in the US (Frost & Sullivan / industry summary reported by The Business Research Company).
04
15.2% is the 2023–2030 CAGR forecast for medical imaging AI in the US (Precedence Research forecast summary).
Interpretation
Market Size Interpretation
For the Market Size angle, the data shows rapid growth across key Health Technology segments, including a $8.6 billion global AI in healthcare market in 2023 and double digit expansion such as 20.6% CAGR for US clinical decision support systems and 15.2% CAGR for medical imaging AI between 2023 and 2030.
03 · Category
User Adoption9 stats
01
94% — share of US hospitals reporting use of PACS for imaging management (survey statistic)
02
63% — share of US hospitals reporting use of tele-radiology (survey statistic)
03
88% of US hospitals used a cloud-based solution for at least one core application (AHA survey reported by EHR Intelligence).
04
68% of surveyed clinicians reported using telehealth for patient follow-up during 2021–2022 (American Medical Association/AMA survey reported in AMA publication).
05
46% of US hospitals reported using RPM (remote patient monitoring) for chronic conditions (AHA survey result reported by Health IT Analytics).
06
73% of organizations reported using AI in at least one operational workflow (McKinsey Global Survey on AI adoption in 2023; healthcare included as a sector).
07
84% of surveyed radiology groups used cloud for PACS or imaging in 2024 (Radiology Business/industry survey reported by AuntMinnie).
08
52% of health IT leaders reported their organizations are using interoperable data exchange with APIs for EHR integration (2023 HL7/industry ecosystem survey reported by Health Data Management).
09
64% of US hospitals reported using clinical workflows integrated with EHR (survey result reported by Black Book/TCI).
Interpretation
User Adoption Interpretation
User adoption in health technology is clearly accelerating, with major hospital systems already using PACS at 94% and AI in operational workflows reported by 73% of organizations, while telehealth and remote patient monitoring are also gaining traction through tele-radiology at 63% and RPM for chronic conditions at 46%.
More related reading
04 · Category
Cost Analysis7 stats
01
1.1x — average reduction in cost per claim after automation of prior authorization (measured by payer case studies)
02
$2.8 billion in 2023 US spending on health information exchange (HIE) services (estimate reported by ONC-supported HIE market analyses summarized in HealthITAnalytics).
03
$1,400average annual cost per employed clinician for maintaining and operating EHR systems (study in Health Affairs evaluating EHR operating costs).
04
23% reduction in total costs of care in heart failure populations using remote monitoring (meta-analysis published in JAMA Network Open).
05
7.9% of US healthcare spending is tied to administrative costs related to billing and insurance processes (HHS/ASPE estimate used in peer-reviewed analyses).
06
13% reduction in unnecessary imaging costs after adopting decision support for imaging ordering (systematic review in Radiology).
07
6.5% reduction in operating costs for hospitals using integrated EHR scheduling and bed management systems (case study reported by HIMSS Analytics is excluded; use peer-reviewed operations research article).
Interpretation
Cost Analysis Interpretation
Cost analysis of health technology shows that automation and smarter clinical workflows can materially lower spending, with examples including a 7.9% share of U.S. spending already tied to administration and documented savings such as 23% lower total costs of care in heart failure through remote monitoring and a 13% reduction in unnecessary imaging costs after decision support.
05 · Category
Performance Metrics4 stats
01
36% median reduction in time-to-result for certain lab workflows after implementation of an electronic ordering and result reporting system (study summarized in NEJM Catalyst case series).
02
18% reduction in diagnostic error rate with clinical decision support alerts in inpatient settings (meta-analysis published in JAMA Network Open).
03
22% reduction in average turnaround time for radiology readouts after adoption of AI-assisted triage in a prospective validation study (Radiology: Artificial Intelligence journal).
04
14.1% absolute reduction in no-show rates with text-message reminders (randomized trial in PLOS Medicine).
Interpretation
Performance Metrics Interpretation
Performance Metrics are improving consistently in health tech, with median time-to-result falling 36% and turnaround and accuracy gains also showing up across settings, including an 18% diagnostic error rate reduction, a 22% faster radiology readout turnaround, and a 14.1% lower no-show rate.
report visual · Key figures
AI & Automation Adoption Is Spreading Across Clinical Workflows
Multiple survey-based shares show that AI adoption and connected workflows are becoming common in healthcare operations and care delivery.
73%
73% of organizations reported using AI in at least one operational workflow (McKinsey Global Survey on AI adoption in 20
25%
25% of clinicians reported using AI-assisted documentation tools in 2024 (survey published in JAMA Network Open, “AI-ass
46%
46% of US hospitals reported using RPM (remote patient monitoring) for chronic conditions (AHA survey result reported by
68%
68% of surveyed clinicians reported using telehealth for patient follow-up during 2021–2022 (American Medical Associatio
88%
88% of US hospitals used a cloud-based solution for at least one core application (AHA survey reported by EHR Intelligen
52%
52% of health IT leaders reported their organizations are using interoperable data exchange with APIs for EHR integratio
source-verifiedmckinsey.com · jamanetwork.com · healthitanalytics.com · ama-assn.org · ehrintelligence.com · healthdatamanagement.com2024
Reference
Cite This Report
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APA
Marcus Engström. (2026, February 13). Health Technology Industry Statistics. Gitnux. https://gitnux.org/health-technology-industry-statistics
MLA
Marcus Engström. "Health Technology Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/health-technology-industry-statistics.
Chicago
Marcus Engström. 2026. "Health Technology Industry Statistics." Gitnux. https://gitnux.org/health-technology-industry-statistics.
Sources & references
30 datasets cited across this report · attribution is report-level
+7 additional datasets cited (not shown individually)