Redefining standards of care.

Aidoc’s clinical AI is changing healthcare at the world’s largest health systems.

Tremendous pressure exists on health systems to do more with less, and facilities are searching for solutions to optimize resources without sacrificing the quality of care delivered. Today, the role AI can play in reducing costs, improving outcomes and increasing efficiency is not an untested theory.

Since 2016, Aidoc has delivered clinically validated results for a broad spectrum of institutions varying in size and population diversity. We’re committed to growing the body of evidence that continues to quantify the many positive clinical and financial outcomes of AI.

View selected evidence

a doctor wearing glasses looking at brain scans

Real-World Impact Across Key Facility Metrics

Multiple factors are contributing to operational challenges for health systems, but Aidoc’s effectiveness as a prioritization tool has led to positive downstream financial and clinical results at partner facilities.

Revenue Capture

40%

increase in clinically appropriate interventions (PE)¹

Prioritized Discharge

10% - 26%

decrease in patient length of stay (ICH & PE) ^2,3,4

Disease Awareness

45% – 70%

enhanced triage (iPE)^5,6

Clinical Efficiency

22% - 55%

improvement in turnaround time reduction^7-10

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Accurate, Aware, Always On

Regardless of pathology or care setting, Aidoc solutions have demonstrated a high-level of reliability across more than 1,200 global hospitals and health systems, helping to expedite the time from scan to diagnosis and treatment.

96% solution accuracy

36.6% improvement in turnaround time

5 minute average scan to notification prioritization of urgent cases

3 million scans reviewed in clinical settings each month

Aidoc Research Overview

Each year, Aidoc’s Clinical Research team collaborates with partner sites to author and support scientific publications and presentations that demonstrate the opportunities of clinical AI across numerous pathologies and topics.

150+ studies or abstracts published

36 global facilities represented

30+ peer reviewed publications

50+ peer reviewed abstracts

10+ pathologies investigated

Accurate, Aware, Always On

Doctors performing doing Clinical Impact Research

96% solution accuracy

36.6% improvement in turnaround time

5 minute average scan to notification prioritization of urgent cases

3 million scans reviewed in clinical settings each month

Aidoc Research Overview

A doctor performing doing Clinical Impact Research

150+ studies or abstracts published

36 global facilities represented

30+ peer reviewed publications

50+ peer reviewed abstracts

10+ pathologies investigated

Interested in your own AI research?

Robust and diverse evidence about artificial intelligence clinical outcomes are essential to proving its value and impact. Are you interested in developing a clinical study with Aidoc solutions? Please complete the form below and a member of our Clinical Research team will follow-up.

* This page presents a body of research conducted using Aidoc’s solution, with some assessing products and use cases that are currently under R&D or investigational use only. For information on Aidoc’s cleared products indications for use; safe and correct usage and risk information, please check our quality and compliance page.

1.

Akhil Khosla, “Transforming Endovascular Care with AI-Triggered PE Response Teams.” webinar. https://www.aidoc.com/resources/?filter=webinars#fF_caPjLGZ4

2.

Petry M, Lansky C, Chodakiewitz Y, Maya M, Pressman B. “Decreased Hospital Length of Stay for ICH and PE after Adoption of an Artificial Intelligence-Augmented Radiological Worklist Triage System.” Radiol Res Pract. 2022 Aug 18;2022:2141839. doi: 10.1155/2022/2141839. PMID: 36034496; PMCID: PMC9411003.

3.

Davis, Melissa A et al. “Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Non-contrast Computed Tomography.” Current problems in diagnostic radiology vol. 51,4 (2022): 556-561. doi:10.1067/j.cpradiol.2020.10.007

4.

K. Gupta, MD, H. G. Lipshutz, MD, O. Friedman, MD, Y. Matusov, MD, M. Yaqoob, MD, J. Steinberger, MD. “Mechanical Thrombectomy, AI and the Activation of a Pulmonary Embolism Response Team.” PERT Consortium Conference 2022

5.

Yale New Haven Hospital – Bader et al. (2020) Use of a Machine Learning Algorithm to Detect Incidental Pulmonary Embolism. SIIM 2020.

6.

Region Halland – Wiklund et al. (2022) Incidental pulmonary embolism in patients with cancer: prevalence, underdiagnosis and evaluation of an AI algorithm for automatic detection of pulmonary embolism. European Journal of Radiology. doi: 10.1007/s00330-022-09071-0

7.

UT Southwestern Parkland Health – O’Neill et al. (2020) Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage. Radiol Artif Intell. 2020 Nov 18;3(2):e200024. doi: 10.1148/ryai.2020200024

8.

University of Rochester – Wismueller et al. (2020) A Prospective Randomized Clinical Trial for Measuring Radiology Study Reporting Time on Artificial Intelligence-Based Detection of Intracranial Hemorrhage in Emergent Care Head CT. Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113170M doi: 10.1117/12.2552400

9.

Lahey Hospital & Medical Center – Harrison et al. (2021) Worklist Reprioritization Using Artificial Intelligence Improves Turnaround Times Of Chest CT Examinations Positive For Acute Pulmonary Embolism. Session ID: SDP-MS-2, RSNA 2021

10.

University of Chicago – Ginat et al. (2021) Implementation of Machine Learning Software on the Radiology Worklist Decreases Scan View Delay for the Detection of Intracranial Hemorrhage on CT. Brain Sci. 2021 Jun 23;11(7):832. doi: 10.3390/brainsci11070832