Cedars-Sinai Medical Center: AIM

Los Angeles, California, USA All United States companies → — Founded 2022

AI Research Lab Clinical AI Platform Imaging AI Specialist

Internal funding, grants, and philanthropic contributions — Grants and internal institutional funding (e.g., over $5M for KronosRx project from ARPA-H)

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Products

Autoplaque

Cedars-Sinai Medical Center: AIM

Cardiology AI

Autoplaque, developed by Cedars-Sinai AIM, is an FDA-cleared AI-powered software for quantitative analysis of coronary plaques and luminal stenoses from CT angiography images, aiding in heart attack risk prediction and treatment guidance.

Not disclosed

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About Cedars-Sinai Medical Center: AIM

The Artificial Intelligence in Medicine (AIM) Research Center and Division at Cedars-Sinai Medical Center are dedicated to leveraging artificial intelligence to address critical gaps in medical understanding and patient care. Their mission encompasses using AI to solve existing challenges in disease mechanisms, diagnostics, risk assessment, and therapeutics across a wide spectrum of human disease conditions. For physicians, this translates into the development and deployment of AI tools designed to enhance screening, prevention, and management strategies, ultimately aiming to improve patient outcomes and streamline clinical workflows.

The center and division utilize Cedars-Sinai’s extensive clinical data warehouse, which contains secure health information for over 6 million patients, and collaborate with other institutions to design, ethically vet, evaluate, validate, and implement AI solutions. Their work employs various AI and machine learning methodologies, including deep learning, computer vision, generative AI, and robotics, applied across diverse medical and surgical disciplines. Key initiatives include developing algorithms for early disease detection, predicting patient risks, optimizing treatment pathways, and enhancing operational efficiencies within the healthcare system.

Focus Areas

Prediction and prevention of sudden cardiac arrest cardiac imaging analysis pancreatic cancer detection surgical AI (assessing surgeon performance anticipating patient outcomes) virtual care drug safety and toxicity prediction mental health therapy improving operational efficiency personalized medicine computational biology of gliomas identifying medication risks clinical trial recruitment

Business Intelligence

Partnerships	K Health (for Cedars-Sinai Connect), Redesign Health (for Digital Innovation Platform), L.A. Pierce College (for National AI Campus), University Health Network (advancing AI in healthcare)
Technology	Cloud-native, deep learning, computer vision, generative AI, robotics, machine learning, automated machine-learning (AutoML), clinical data warehouse, electronic health records, synthetic data, organoids and organ-on-chip systems, natural language processing
FDA Clearances	1 cleared products (estimated)

What Physicians Need to Know

Prediction & Prevention of Sudden Cardiac Arrest

Cedars-Sinai AIM utilizes advanced AI algorithms to analyze electrocardiograms (ECGs) and identify patterns indicative of future sudden cardiac arrest (SCA). They've developed VFRisk, a clinical tool that integrates 13 biomarkers from ECGs, echocardiographic imaging, and clinical history to score SCA risk. Ongoing research incorporates multimodal AI (imaging, genomics, proteomics, biochemical markers) for enhanced predictive accuracy.

Cardiac Imaging Analysis

AI tools are employed to assess cardiac function with greater accuracy than traditional sonographer assessments. They've pioneered AI-based solutions to measure coronary artery plaque buildup from standard CT scans, predicting heart attack risk within five years. Their Cardiac Imaging Program integrates state-of-the-art imaging with physics-guided AI to deepen understanding of heart disease and personalize treatment strategies.

Pancreatic Cancer Detection & Treatment

An AI-driven tool can predict pancreatic cancer years before diagnosis using CT scan images. This tool is being adapted for specific populations, including Black patients who face higher incidence rates. Furthermore, an AI platform predicts the most effective chemotherapy regimens for advanced pancreatic cancer by analyzing digital images of tumor biopsy slides, moving beyond traditional biomarker testing.

Surgical AI & Outcome Prediction

AI analyzes surgical videos to identify and classify individual surgical gestures, correlating them with long-term patient outcomes (e.g., erectile function recovery after prostatectomy). This allows for systematic connection between intraoperative decisions and postoperative recovery. AI also predicts post-operative mortality using pre-operative electrocardiograms, informing surgical decision-making.

Virtual Care & Remote Monitoring

Cedars-Sinai Connect, an AI-powered virtual platform, provides 24/7 care for acute illnesses, chronic conditions, and preventive needs. It uses chat-based symptom intake and patient records to generate treatment recommendations, which physicians review and approve. The platform has expanded to include pediatric and Spanish-speaking patients and is piloting remote monitoring for chronic diseases.

Drug Safety & Toxicity Prediction

Cedars-Sinai is developing KronosRx, an AI-based platform to predict drug toxicity before clinical trials using 'patient avatars' (organoids/organ-on-chip systems) and extensive EHR data. They also created OnSIDES (ON-label SIDE effectS resource), a publicly available database that uses AI to extract and analyze adverse drug events from medication labels, enhancing drug safety and risk identification.

Improving Operational Efficiency

AI is leveraged to streamline administrative tasks and improve workflow. Examples include the Aiva Nurse Assistant for voice-assisted documentation, transcribing nurse notes directly into EHRs, and Cedars-Sinai Connect, which reduces administrative burdens for clinicians. They also provide AI training for employees to foster efficiency and innovation.

Personalized Medicine & Computational Biology

A core focus across various domains, including predicting optimal chemotherapy for pancreatic cancer and computational biology of gliomas. Researchers use AI, imaging, and biological data to create patient-specific models of brain tumor growth, spread, and treatment response, aiming for individualized treatment strategies and improved clinical trial design.

Clinical Trial Recruitment

AI-driven predictive models, particularly in mathematical neuro-oncology for brain tumors, assist in patient selection for clinical trials. These models help identify participants whose tumors are predicted to be biologically vulnerable to specific therapies, potentially improving trial outcomes and reducing development timelines.

Physician Tip

For physicians, Cedars-Sinai AIM offers a powerful suite of AI tools designed to enhance diagnostic accuracy and speed, provide personalized treatment recommendations, and improve patient risk stratification. These technologies aim to reduce administrative burdens through automated documentation and virtual care platforms, freeing up more time for direct patient interaction. Physicians gain access to cutting-edge research and proprietary AI solutions, supported by a multidisciplinary team and a commitment to ethical AI implementation. Opportunities for continuous AI education and training are also available to help integrate these advancements into clinical practice effectively.

Cedars-Sinai AIM's solutions are deeply integrated with their extensive clinical data warehouse, leveraging millions of anonymous patient data points from electronic health records (EHRs). Their platforms, such as Cedars-Sinai Connect, are built to streamline workflows and integrate virtual care with in-person services. The development of tools like OnSIDES and KronosRx demonstrates a focus on creating interoperable resources and platforms that can be applied across various stages of drug development and patient care, often utilizing machine learning, deep learning, computer vision, and natural language processing techniques.

At a glance

Founded 2022

HQ Los Angeles, California, USA · United States

Team size A large multidisciplinary team comprising clinicians, investigators, data experts, software engineers, and programmers.

CEO Not applicable (internal division/center of Cedars-Sinai Medical Center). Dr. Sumeet Chugh is the Director of the Division of Artificial Intelligence in Medicine. Mouneer Odeh is the Chief Data and Artificial Intelligence Officer for Cedars-Sinai.

Funding Internal funding, grants, and philanthropic contributions — Grants and internal institutional funding (e.g., over $5M for KronosRx project from ARPA-H)

FDA clearances 1 (estimated)

Products 1

Products by Cedars-Sinai Medical Center: AIM

1 product in the directory

Autoplaque

Cedars-Sinai Medical Center: AIM

Cardiology AI

Not disclosed

What the Web Says

Cedars-Sinai Medical Center: AIM (Applied Informatics in Medicine) receives generally positive feedback, particularly for its innovative approach to healthcare technology and research. As an employer, it is often praised for its professional environment and opportunities for growth, though some common challenges in large medical institutions are noted.

Overall: Positive

Strengths

Strong focus on innovation and research in healthcare IT.
Opportunities for professional development and learning.
Collaborative and supportive work environment.
Impactful work contributing to patient care and medical advancements.
Reputable institution with high standards.
Good benefits package for employees.

Limitations

Potential for high workload and demanding hours common in healthcare.
Bureaucracy and slow decision-making processes inherent in large organizations.
Compensation may not always be competitive with pure tech companies.
Specific product reviews for 'AIM' are scarce on general review sites like G2/Capterra.
Some reports of internal communication challenges.
Onboarding process can be lengthy.

Based on reviews from: Glassdoor, Reddit (various healthcare and tech subreddits), Healthcare IT News (mentions of Cedars-Sinai innovation), Cedars-Sinai official publications/research papers, LinkedIn (employee testimonials)

Last updated: 2026-07-17

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Press & Coverage

FDA

K212758 - 510(k) Premarket Notification - FDA

This document details the FDA 510(k) Premarket Notification for Cedars-Sinai Medical Center: AIM's 'Autoplaque' device, received on April 25, 2023, and determined substantially equivalent. The device is a workstation-based post-processing application for analyzing coronary CT angiographic images to assess plaque and stenosis.

2022-08

FDA

TPLC - Total Product Life Cycle - FDA

This FDA TPLC entry indicates that Cedars-Sinai Medical Center: AIM's 'Autoplaque' device (K212758) has been found substantially equivalent. It is listed among other medical image management and processing systems.

2026-07

Innolitics

Definitive Guide to AI/ML SaMD Ground Truthing - Innolitics

This article discusses ground truthing for AI/ML SaMD, referencing Cedars-Sinai Medical Center: AIM's 'Autoplaque' (K212758) as a precedent where ground truthing was performed by two cardiologists with a radiologist resolving discrepancies. It highlights strategies for defensible ground truth in regulatory submissions.

2025-09

PLOS One

Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups | PLOS One

This peer-reviewed article, published in PLOS One, lists Cedars-Sinai Medical Center, AIM Group, Los Angeles, CA, USA, as an affiliation for one of the authors. The research investigates the effect of grey-level discretization on texture features in MRI images across different disease groups.

2021-06

Cedars-Sinai Medical Center

Artificial Intelligence in Medicine

This page describes Cedars-Sinai's Artificial Intelligence in Medicine (AIM) program, which focuses on developing software to process and analyze 3D heart images, aiming for quick, quantitatively accurate, and consistent measurements.

unknown

Cedars-Sinai Medical Center

User's Manual CSI, QGS + QPS, QBS, MoCo and AutoRecon - Cedars Sinai Cardiac Suite

This user manual for the Cedars-Sinai Cardiac Suite, developed by the AIM Program, details the software's features for automated display, review, and quantification of Nuclear Medicine Cardiology medical images. It also states the software is CE Marked to the Medical Device Directive 93/42/EEC.

2019

Youngpoong Bookstore

2024 uae00ub85cubc8c AIuc758ub8cc ud5ecuc2a4ucf00uc5b4 ube44uc988ub2c8uc2a4 ud578ub4dcubd81 - uc601ud48dubb38uace0

This Korean business handbook on global AI medical healthcare for 2024 lists Cedars-Sinai Medical Center: AIM as one of the key players in the field.

2024-09

Frequently Asked Questions

How is Cedars-Sinai's AIM program leveraging AI for the prediction and prevention of sudden cardiac arrest (SCA)?

Cedars-Sinai's AIM program utilizes AI to predict SCA risk by analyzing ECG data, echocardiographic imaging, and clinical history, leading to tools like VFRisk. Researchers are also working to distinguish between treatable and untreatable forms of SCA to guide more effective prevention strategies.

What AI-driven solutions does Cedars-Sinai offer for advanced cardiac imaging analysis?

Cedars-Sinai employs AI for rapid cardiac imaging analysis, capable of extracting valuable heart health insights from CT scans in as little as 18 seconds without contrast. Their Autoplaque software, validated in numerous studies, uses AI to quantify dangerous plaque volumes from cardiac CT angiography, which is a stronger predictor of myocardial infarction than traditional stenosis measurements.

What advancements has Cedars-Sinai's AIM made in early pancreatic cancer detection and personalized treatment?

Cedars-Sinai has developed an AI tool that can detect subtle, early signs of pancreatic ductal adenocarcinoma in CT scans up to three years before an official diagnosis with 86% accuracy. They are also using a precision-medicine AI tool, the Molecular Twin Precision Oncology Platform, to analyze pathology data and predict the most effective chemotherapy regimens for individual patients.

How is Cedars-Sinai applying AI to surgical performance assessment and patient outcome prediction?

The Hung Laboratory at Cedars-Sinai is utilizing AI to objectively assess surgeon performance during robotic surgery by analyzing video data and correlating surgical gestures with patient-reported outcomes, particularly for prostatectomies. Additionally, an AI tool developed by the Smidt Heart Institute accurately predicts post-operative mortality using pre-operative electrocardiograms, helping clinicians determine which patients would benefit most from surgery.

What are Cedars-Sinai's initiatives in drug safety and toxicity prediction, including any specific products?

Cedars-Sinai is developing KronosRx, an AI-based platform funded by ARPA-H, to predict drug toxicity before clinical trials using 'patient avatars' to enhance trial safety and reduce failures. They also created OnSIDES, a publicly available AI-driven database that extracts and organizes adverse medication events from drug labels to improve drug safety and identify medication risks.

How does Cedars-Sinai address operational efficiency and virtual care through its AI programs?

Cedars-Sinai enhances operational efficiency with AI tools like the Aiva Nurse Assistant for voice-assisted documentation, reducing administrative burdens for nurses. For virtual care, the Cedars-Sinai Connect mobile app, developed with K Health, provides 24/7 access to care and has expanded to support diverse patient populations, streamlining workflows for clinicians.

What is Cedars-Sinai's approach to computational biology of gliomas and personalized medicine in this area?

Cedars-Sinai researchers are at the forefront of mathematical neuro-oncology, integrating physics, imaging, AI, and biology to create computational forecasts of individual brain tumors. These patient-specific models help understand tumor growth and spread, guide personalized treatment strategies, and improve patient selection for clinical trials.

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