AI Solutions Deliver Connected Teams

The pressure on medical professionals to provide quality and effective care to patients is enormous. Physicians in every subspeciality are searching for efficiencies at every patient and collaboration touchpoint while navigating their own resource constraints. That’s where AI can help.

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More than 1,000 medical systems worldwide are using Aidoc’s AI-powered technology to:

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Improve patient care and clinical outcomes through rapid triage and notification

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Empower teams to operationalize the unexpected and collaborate seamlessly

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Automatically assist with patient capture and follow-up management

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Decrease disruptions and breaks in continuum of care by helping teams coordinate

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Reduce care costs, treatment turnaround time, and length of stay 

Our always on AI platform and AI-based solutions cover 75% of patient populations, empowering physicians to make informed decisions based on real-time data. Connected care teams can drive improved outcomes across specialties and departments, unifying a patient’s journey.

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A Comprehensive Clinical AI Solution

Cardiovascular

  • Aortic Care
  • Venous Thromboembolism (VTE)
  • Coronary Artery Disease
  • Echocardiography (AI application partner)
  • Structural Heart Disease
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Neuro

  • Neurovascular
  • Neurosurgery
  • Spine Care
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Radiology

  • Abdominal Aortic Measurements*
  • Aortic Dissection
  • Brain Aneurysm
  • C-Spine Fracture
  • Incidental Pulmonary Embolism
  • Intra-Abdominal Free Gas
  • Intracranial Hemorrhage
  • M1 Large Vessel Occlusion
  • Malpositioned Endotracheal Tubes (ETT)
  • Pneumothorax
  • Pulmonary Embolism
  • Rib Fractures
  • Vessel Occlusion
  • Vertebral Compression Fracture

*Pending 510(k), not available for sale within the United States.

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AI-Powered, Human-Enabled Workflows

Aidoc’s connected system of intelligence enables clinicians to work more efficiently by consolidating data and streamlining communication to support quicker and more comprehensive decision making. Streamlined workflows help prevent patients from being lost to follow-up and can increase downstream procedures.

Radiology

Radiology solutions include seamless, image-based triage and quantification connecting to 13 organic FDA-cleared algorithms and eight FDA-cleared partner algorithms.

Aidoc solution interface
Radiology imaging on a smart phone

Care Coordination

Mobile app providing real-time notification of time-sensitive cases, built-in risk stratification and a mobile imaging viewer. Automatically fed EHR data facilitates cross-department communication.

Patient Management

Identify patients needing follow-up – such as those with untreated or undertreated coronary artery disease – using integrations to track follow-up adherence, provide clinical pathway steerage and automate outreach to patients, primary care physicians or clinics.

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Aidoc Clinical Trial Boards dashboard

Clinical Trial Enrollment

AI-driven automatic patient identification for clinical trial enrollment.

Trusted and Proven Solutions

Our understanding of the complex problems faced by clinicians is reflected in key success metrics from our customers, such as:

Reduction in los

11% YALE NEW HAVEN HEALTH (ED)1

12% Cedars-Sinai Medical Center (Inpatient)2

26% Cedars-Sinai Medical Center (Inpatient)2

23% Cedars-Sinai Medical Center (ICU)3

Quality improvement

4% Cedars-Sinai Medical Center4

5% Beth Israel Lahey Health5

7% Christiana Care6

14% Envision Healthcare7

45% Yale New Haven Health8

Turnaround time reduction

22% UT Southwestern9

35% University of Chicago10

40% Beth Israel Lahey Health11

55% University of Rochester12

See Clinical Outcomes
Carl Amato headshot
Carl Armato
Novant Health, CEO
Dr. Howard Forman headshot
Howard Forman, MD, MBA, FACR
Emergency Radiologist and Faculty Director of Finance, Yale New Haven Hospital
Carl Armato
Novant Health, CEO
Carl Amato headshot

“With the help of [Aidoc], Novant Health is taking proactive steps to improve patient experience, improve health outcomes and reduce emergency department length of stay.”

Howard Forman, MD, MBA, FACR
Emergency Radiologist and Faculty Director of Finance, Yale New Haven Hospital
Dr. Howard Forman headshot

“This has far exceeded my expectations…”

Discover more

Explore why effective healthcare starts with a connected system of intelligence in this free whitepaper. Learn:

  • Why now is the time to build the business case for artificial intelligence
  • What effective clinical AI looks like
  • How health system-wide interconnectivity can help improve clinical outcomes
  • Steps to building a long-term AI strategy
Get the Paper
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1.

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

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.

Kavish Gupta, MD, H. Gabriel Lipshutz, MD, Oren Friedman, MD, Yuri Matusov, MD1, Maida Yaqoob, MD, Jonathan Steinberger, MD. “Mechanical Thrombectomy, Artificial Intelligence and the Activation of a Pulmonary Embolism Response Team.” PERT Consortium Conference 2022

4.

Yosef Chodakiewitz, Marcel Maya, Barry Pressman. Abstract WP63: AI-Augmented Review of CT Brain Exams to Determine Rate of Missed Diagnoses of Intracranial Hemorrhage by Practicing Neuroradiologists

5.

Small, J E et al. “CT Cervical Spine Fracture Detection Using a Convolutional Neural Network.” AJNR. American journal of neuroradiology vol. 42,7 (2021): 1341-1347. doi:10.3174/ajnr.A7094

6.

Hasan et al. Implementation of Quality Assurance for CTA PE Interpretation utilizing A.I. monitoring, to avoid missed diagnosis of acute pulmonary embolism. RSNA 2022

7.

A. Jacob, B Doan, C Granville., (2022). Man and machine: implications of an artificial intelligence based algorithm in the detection of pulmonary embolism on computed tomography angiography SIR 2022 Annual Meeting

8.

Anna S Bader, MD MS, Iram A Khan, MBBS, Melissa Davis, MD, Irena Tocino, MD, Isabel O Cortopassi, MD. “Use of a machine learning algorithm to detect incidental pulmonary embolus” SIIM2020

9.

Thomas J O’Neil, Yin Xi, Edward Stehel, Travis Browning, Yee Seng Ng, Chris Baker, and Ronald M. Peshock. “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” Radiology: Artificial Intelligence 2021 3:2

10.

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

11.

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

12.

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

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