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RadNet’s subsidiary DeepHealth has announced a strategic collaboration with CARPL.ai to develop an advanced AI control system for image interpretation in radiology. The partnership aims to create a monitoring system for AI performance, safety, and scalability, integrating CARPL.ai’s AI orchestration capabilities with DeepHealth’s cloud-native operating system. The system will automate the measurement of performance metrics such as specificity, sensitivity, and model drift, while enabling radiologists to access and monitor FDA-cleared AI models within their workflows.
La sussidiaria di RadNet DeepHealth ha annunciato una collaborazione strategica con CARPL.ai per sviluppare un sistema di controllo avanzato basato su AI per l’interpretazione delle immagini in radiologia. L’obiettivo della partnership è creare un sistema di monitoraggio delle prestazioni, della sicurezza e della scalabilità dell’AI, integrando le capacità di orchestrazione dell’AI di CARPL.ai con il sistema operativo nativo cloud di DeepHealth. Il sistema automatizzerà la misurazione dei parametri di prestazione come specificità, sensibilità e deriva del modello, permettendo ai radiologi di accedere e monitorare i modelli di AI approvati dalla FDA all’interno dei loro flussi di lavoro.
La subsidiaria de RadNet DeepHealth ha anunciado una colaboración estratégica con CARPL.ai para desarrollar un sistema de control avanzado basado en IA para la interpretación de imágenes en radiología. El objetivo de la asociación es crear un sistema de monitoreo del rendimiento, la seguridad, y la escalabilidad de la IA, integrando las capacidades de orquestación de IA de CARPL.ai con el sistema operativo nativo en la nube de DeepHealth. El sistema automatizará la medición de los métricas de rendimiento como especificidad, sensibilidad y deriva del modelo, facilitando a los radiólogos el acceso y monitoreo de modelos de IA aprobados por la FDA dentro de sus flujos de trabajo.
RadNet의 자회사 DeepHealth가 CARPL.ai와 함께 방사선 이미지 해석을 위한 고급 AI 제어 시스템 개발을 위한 전략적 협력 관계를 발표했습니다. 이 파트너십의 목표는 CARPL.ai의 AI 오케스트레이션 기능과 DeepHealth의 클라우드 네이티브 운영 체제를 통합하여 AI 성능, 안전성 및 확장성을 모니터링하는 시스템을 만드는 것입니다. 이 시스템은 성능 지표의 측정을 자동화할 것입니다 특정도, 민감도 및 모델 변화를 포함하고, 방사선 전문의들이 자신의 작업 흐름 내에서 FDA 승인 AI 모델에 접근하고 모니터링할 수 있게 합니다.
La filiale de RadNet DeepHealth a annoncé une collaboration stratégique avec CARPL.ai pour développer un système de contrôle avancé basé sur l’IA pour l’interprétation des images en radiologie. L’objectif de ce partenariat est de créer un système de surveillance des performances, de la sécurité et de l’évolutivité de l’IA, en intégrant les capacités d’orchestration de l’IA de CARPL.ai avec le système d’exploitation natif cloud de DeepHealth. Le système automatisera la mesure des indicateurs de performance tels que la spécificité, la sensibilité et la dérive du modèle, tout en permettant aux radiologues d’accéder et de surveiller les modèles d’IA approuvés par la FDA dans leurs flux de travail.
Die Tochtergesellschaft von RadNet DeepHealth hat eine strategische Zusammenarbeit mit CARPL.ai angekündigt, um ein fortschrittliches KI-Kontrollsystem für die Bildinterpretation in der Radiologie zu entwickeln. Ziel der Partnerschaft ist es, ein Überwachungssystem für die Leistung, Sicherheit und Skalierbarkeit der KI zu schaffen, das die Orchestrierungsfähigkeiten von CARPL.ai mit dem cloud-nativen Betriebssystem von DeepHealth integriert. Das System wird die Messung von Leistungsmetriken automatisieren wie Spezifität, Sensitivität und Modellveränderungen und es Radiologen ermöglichen, auf von der FDA zugelassene KI-Modelle innerhalb ihrer Arbeitsabläufe zuzugreifen und diese zu überwachen.
Positive
- Strategic partnership to develop commercializable AI control system
- Integration of CARPL.ai’s marketplace with DeepHealth OS platform
- Automation of AI performance monitoring and optimization
Insights
This strategic collaboration between DeepHealth and CARPL.ai represents a significant advancement in AI monitoring for radiology. The integration combines DeepHealth’s clinical expertise with CARPL.ai’s orchestration capabilities to create a comprehensive AI control system that addresses critical challenges in AI deployment.
The partnership has three key technical advantages:
- Automated performance monitoring of AI models in real-time
- Dynamic model selection and optimization capabilities
- Integration of multiple AI models for single use cases
While the immediate revenue impact may be , this positions RadNet strategically in the growing AI healthcare market. The system’s ability to monitor metrics like specificity and sensitivity, while detecting model drift, provides a competitive edge in ensuring AI reliability and clinical adoption.
- DeepHealth and CARPL.ai have established a strategic collaboration to create a unique Artificial Intelligence (AI) control system for image interpretation to ensure AI scalability, performance monitoring, and safety, with the aim to accelerate the adoption of AI.
- DeepHealth currently monitors the performance of DeepHealth’s SmartMammo™ AI-powered solution for breast cancer detection at RadNet. Through the collaboration, the two companies aim to expand, productize and scale this control system across more applications to other customers.
- Furthermore, DeepHealth will embed CARPL.ai’s cutting-edge AI orchestration capabilities that enable easy selection, implementation, and monitoring of appropriate AI models within DeepHealth’s cloud-native operating system, DeepHealth OS.
LOS ANGELES and SOMERVILLE, Mass., Dec. 01, 2024 (GLOBE NEWSWIRE) — DeepHealth, Inc., a global leader in AI-powered health informatics and a wholly-owned subsidiary of RadNet, Inc. (NASDAQ: RDNT), today announced a strategic collaboration with CARPL.ai, a leading AI orchestration company that enables radiologists to access, assess, and integrate radiology AI solutions in their workflows. DeepHealth will use CARPL.ai’s technology to develop an AI control system that can be commercialized and will be designed to monitor and optimize imaging AI performance for improved clinical outcomes, operational efficiency, and accelerated adoption of AI in radiology. AI monitoring is crucial to ensure reliable, accurate, and unbiased performance.
The two companies will collaborate on a new closed-loop AI feedback system that will continually monitor AI model accuracy and relevance in clinical settings. The system will automate the measurement and monitoring of performance and safety metrics such as specificity, sensitivity, data- and model drift.
“Establishing a robust AI infrastructure with monitoring tools is key for safe, effective, and scalable AI adoption in radiology. While the current landscape is marked by an overwhelming array of AI-enabled point solutions, the future involves running multiple AI models, even for a single use case. DeepHealth’s partnership with CARPL.ai addresses this very need by creating a unique environment to dynamically run a combination of models and monitor performance and then continuously optimize the best models for specific tasks,” said Sham Sokka, PhD, Chief Operating and Technology Officer, DeepHealth.
The partnership will also combine CARPL.ai’s AI marketplace and orchestration platform, which offers a simplified process for selecting, implementing, and monitoring third-party FDA-cleared AI models, with DeepHealth’s cloud-native operating system, DeepHealth OS, which unifies data across the clinical and operational workflows. These platforms will be integrated and extended to monitor real-world workflows on an ongoing basis. The aim is to enable radiologists to access performant and safe AI interpretation tools deeply integrated in their workflows.
“We are very excited to partner with DeepHealth to harness the transformative potential of AI within the radiology care continuum, particularly through workflow automation and clinical assistance. This new AI infrastructure is set to fundamentally redefine radiology by making AI an integral component of the system,” said Dr. Vidur Mahajan, CEO of CARPL.ai. “Monitoring AI performance is essential to ensure the reliability and accuracy of AI applications over time, and our technology enables real-time performance monitoring of both their accuracy and consistency for safe and effective use of AI in clinical practice.”
For more information, visit the DeepHealth (#1340) and CARPL.ai (#5733) booths at the Radiological Society of North America 2024 Annual Meeting.
About RadNet, Inc.
RadNet, Inc. is the leading national provider of freestanding, fixed-site diagnostic imaging services in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 399 owned and/or operated outpatient imaging centers. RadNet’s markets include Arizona, California, Delaware, Florida, Maryland, New Jersey, New York and Texas. In addition, RadNet provides radiology information technology and artificial intelligence solutions marketed under the DeepHealth brand, teleradiology professional services and other related products and services to customers in the diagnostic imaging industry. Together with affiliated radiologists, and inclusive of full-time and per diem employees and technologists, RadNet has a total of over 10,000 employees. For more information, visit http://www.radnet.com.
About DeepHealth
DeepHealth is a wholly-owned subsidiary of RadNet, Inc. (NASDAQ: RDNT) and serves as the umbrella brand for all companies within RadNet’s Digital Health segment. DeepHealth provides AI-powered health informatics with the aim of empowering breakthroughs in care through imaging. Building on the strengths of the companies it has integrated and is rebranding (i.e., eRAD Radiology Information and Image Management Systems and Picture Archiving and Communication System, Aidence lung AI, DeepHealth and Kheiron breast AI and Quantib prostate and brain AI), DeepHealth leverages advanced AI for operational efficiency and improved clinical outcomes in lung, breast, prostate, and brain health. At the heart of DeepHealth’s portfolio is a cloud-native operating system – DeepHealth OS – that unifies data across the clinical and operational workflow and personalizes AI-powered workspaces for everyone in the radiology continuum. Thousands of radiologists at hundreds of imaging centers and radiology departments around the world use DeepHealth solutions to enable earlier, more reliable, and more efficient disease detection, including in large-scale cancer screening programs. DeepHealth’s human-centered, intuitive technology aims to push the boundaries of what’s possible in healthcare. https://deephealth.com/
About CARPL.ai
CARPL.ai is a vendor-neutral Artificial Intelligence (AI) platform that allows radiologists to access, assess, and integrate radiology AI solutions in their clinical practice.
CARPL provides a single user interface, a single data channel, and a single procurement channel for the testing, deployment, and monitoring of AI solutions in clinical radiology workflows.
We are the world’s largest radiology AI marketplace offering 140+ applications from 60+ AI vendors.
For more information, visit https://carpl.ai/.
Forward Looking Statement
This press release contains “forward-looking statements” within the meaning of the safe harbor provisions of the U.S. Private Securities Litigation Reform Act of 1995. Forward-looking statements, including statements regarding the capabilities of RadNet, CARPL.ai, and DeepHealth’s informatics, hardware and software product portfolios and the collaboration’s impact on radiology practices and healthcare workflow, are expressions of our current beliefs, expectations, and assumptions regarding the future of our business, future plans and strategies, projections, and anticipated future conditions, events and trends. Forward-looking statements can generally be identified by words such as: “anticipate,” “intend,” “plan,” “goal,” “seek,” “believe,” “project,” “estimate,” “expect,” “strategy,” “future,” “likely,” “may,” “should,” “will” and similar references to future periods.
Forward-looking statements are neither historical facts nor assurances of future performance. Because forward-looking statements relate to the future, they are inherently subject to uncertainties, risks and changes in circumstances that are difficult to predict and many of which are outside of our control. Our actual results and financial condition may differ materially from those indicated in the forward-looking statements. Therefore, you should not place undue reliance on any of these forward-looking statements.
For media inquiries, reach out to:
DeepHealth
Andra Axente
Communications Director
Phone: +31 614 440971
Email: andra.axente@deephealth.com
RadNet, Inc.
Mark Stolper
Executive Vice President and Chief Financial Officer
310-445-2800
CARPL.ai
Shruti Singhal
Director – Marketing
+919811189074
FAQ
What is the purpose of RadNet (RDNT) subsidiary DeepHealth’s collaboration with CARPL.ai?
The collaboration aims to develop an AI control system for image interpretation to ensure AI scalability, performance monitoring, and safety in radiology, while integrating CARPL.ai’s orchestration capabilities with DeepHealth’s operating system.
What metrics will DeepHealth and CARPL.ai’s AI control system monitor?
The system will monitor performance and safety metrics including specificity, sensitivity, data drift, and model drift in clinical settings.
How will the DeepHealth-CARPL.ai partnership benefit radiologists?
Radiologists will gain access to FDA-cleared AI models and performance monitoring tools deeply integrated into their workflows, enabling more efficient and accurate image interpretation.
