QuickRad
by Aikenist Technologies Pvt
· Based in India →
— AI-powered radiology platform that accelerates MRI scanning 2x faster and reduces reporting time by 70%.
Aikenist generates revenue through tiered subscription plans (bronze, gold, and platinum) for its radiology workflow solutions, with pricing based on the number of scans per month.
Regulatory Status Disclosed
Overview
QuickRad is an end-to-end AI-integrated PACS (Picture Archiving and Communication System) and RIS (Radiology Information System) with a built-in diagnostic viewer. It offers FDA-approved tools for image storage, viewing, reporting, and analytics, making it a comprehensive platform for radiology departments and diagnostic centers. This web-based PACS is designed to optimize radiology workflows and enhance clinical efficiency, allowing seamless receipt, management, and processing of DICOM images from various modalities, including CT, MRI, ultrasound, and X-ray. QuickRad includes a modality-specific report editor with pre-built templates for consistent and efficient report generation, featuring voice-enabled editing. It also provides optional integration with FDA-cleared third-party AI models, enabling the visualization of AI-generated outputs directly within the platform. The system supports secure data transmission through HTTPS, AES 256 encryption, and multi-factor authentication (MFA), and is fully compliant with DICOM standards for interoperable image and data exchange.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- End-to-end AI-integrated PACS and RIS system
- Built-in diagnostic viewer (Zero Footprint Viewer)
- FDA-approved tools for image storage, viewing, reporting, and analytics
- Seamless receipt, management, and processing of DICOM images
- Modality-specific report editor with pre-built templates
- Voice-enabled editing features (Speech-to-Text Reporting)
- Optional integration with FDA-cleared third-party AI models
- Secure data transmission (HTTPS, AES 256 encryption, MFA)
- DICOM compliance
- AI-powered diagnostic algorithms for Brain, Spine, Lung, Abdomen
- Critical Alert Prioritization (Stroke, TBI, Oncology, Cardiology)
- Agentic PACS & RIS with smart routing
- Instant collaboration and sharing to Doctor & Patient portals
Use Cases
- Optimizing radiology workflows and enhancing clinical efficiency
- Image storage, viewing, reporting, and analytics for radiology departments and diagnostic centers
- Streamlining appointment scheduling, scanning, and reporting
- Accelerating diagnosis and improving outcomes across the radiology workflow
- Deployment for teleradiology providers, diagnostic centers, and hospitals
- Clinical decision support for accurate, consistent, and faster diagnosis
What Physicians Need to Know
DICOM Support & Standards
QuickRad is DICOM native and conformant, ensuring plug-and-play interoperability with any scanner, PACS, or RIS. It supports DICOM SR, WADO-RS, and HL7 FHIR for comprehensive health data exchange.
PACS Integration Method
QuickRad integrates natively with PACS, RIS, or scanners via DICOM, eliminating the need for proprietary connectors. Scans arrive via DICOM push or pull, and AI findings are packaged as DICOM SR overlays and structured reports, then automatically pushed to the PACS for radiologist review.
Reading Room Workflow Impact
QuickRad aims to reduce reporting time by up to 60% through AI-generated reports, speech-to-text functionality, and structured templates. It provides a zero-footprint universal viewer with AI-assisted tools for measurements, annotations, and quantitative analysis, and integrates AI outputs directly into the report draft. The platform is designed to streamline diagnostic paths with autonomous triage and reporting assistance, reducing manual steps and cognitive load for radiologists.
AI Model Architecture (deep learning approach)
QuickRad utilizes deep-learning reconstruction for image enhancement, boosting SNR, correcting artifacts, and normalizing contrast for consistent AI performance. It employs specialized deep-learning algorithms for multi-organ AI analysis, including brain, spine, chest, and abdomen. Segmentation, measurement, and detection models run in parallel on dedicated GPU hardware.
Processing Speed (per study)
QuickRad boasts a GPU inference speed of less than 1 second per study, end-to-end.
FDA Clearance Pathway (510k/De Novo)
QuickRad has received FDA 510(k) clearance, confirming its substantial equivalence to legally marketed predicate devices and enabling it to operate as a Class II medical device in the United States.
Supported Modalities (CT/MRI/X-ray/US)
QuickRad supports various modalities including CT, MRI, X-ray, and ultrasound. QuickScan, part of the Aikenist QuickSuite, supports MRI scans across 1.5T and 3.0T systems for brain, spine, MSK, abdomen, and chest. QuickDiag supports CT, MRI, and X-ray for AI analysis. Aiken Dental PACS, built on the QuickRad platform, handles intraoral and extraoral X-rays, CBCT, and panoramic studies.
Sensitivity & Specificity Data
QuickRad's AI models are multi-center validated, with a clinical sensitivity of 92%. For XraySuite (part of QuickDiag), chest X-ray analysis shows 98% accuracy, 98% sensitivity, and 92% specificity. For MSK fracture detection on X-rays, sensitivity is above 90% and specificity is above 80%.
RSNA/ACR Validation
Aikenist participates in RSNA, with their QuickSuite being showcased at RSNA 2025 to demonstrate AI integration into diagnostic radiology workflows. The company also aligns with FDA and CE-compliant workflows. While specific ACR validation for QuickRad isn't detailed, the ACR is actively involved in evaluating and implementing AI in radiology.
Physician Tip
QuickRad is designed to integrate seamlessly into existing radiology workflows, minimizing disruption. Its AI-powered reporting and image analysis tools can significantly reduce reporting times and enhance diagnostic accuracy. The zero-footprint viewer allows for flexible access, and the system's ability to prioritize critical cases can improve patient outcomes. Physicians should leverage the AI-generated structured reports and quantitative measurements to augment their clinical decision-making, while always maintaining clinical oversight. The platform's multi-modality support and native DICOM integration ensure compatibility with diverse imaging environments.
QuickRad is built for seamless integration with existing PACS, RIS, and scanners through native DICOM conformance, eliminating the need for proprietary connectors. It supports HL7 FHIR for comprehensive health data exchange. The platform is designed to unify multi-vendor PACS, viewers, AI, and GenAI tools, decision support, and analytics without replacing best-in-class systems. It also offers optional integration with FDA-cleared third-party AI models.
Details
| Category | Radiology & Imaging AI |
| Pricing | Aikenist generates revenue through tiered subscription plans (bronze, gold, and platinum) for its radiology workflow solutions, with pricing based on the number of scans per month. |
| Deployment | On-premise or cloud hosting (private cloud environments, major cloud platforms). |
| Compliance | |
| BAA Available | Yes AI-estimated |
| HIPAA Compliant | Yes AI-estimated |
| FDA Status | Cleared AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Radiology |
What the Web Says
QuickRad, developed by Aikenist Technologies, is an AI-powered PACS (Picture Archiving and Communication System) and RIS (Radiology Information System) designed to streamline radiology workflows. It aims to reduce reporting times and enhance diagnostic accuracy through features like AI-assisted reporting, speech recognition, and a zero-footprint viewer. The system has received FDA 510(k) clearance, allowing its expansion into North America, and is ISO 13485:2016 certified.
Overall: PositiveStrengths
- Significantly enhances reporting productivity with faster image loading times and intuitive workflows.
- Features AI-assisted tools for automated diagnosis, structured reporting, and pre-population of reports, potentially reducing reporting time by up to 60%.
- Includes a "Zero Footprint Viewer" with precision measurement tools, 3D rendering, and AI overlay annotations, accessible via a browser.
- Offers real-time image sharing and collaboration between radiologists, technicians, and referring physicians.
- Integrates with existing Hospital Information Systems (HIS) and Radiology Information Systems (RIS) for a smooth workflow.
- Supports deployment on-site, in private cloud environments, or on major cloud platforms, offering flexibility.
Limitations
- No specific cons were found in the provided search results regarding QuickRad by Aikenist.com.
Based on reviews from: G2, Capterra, Aikenist.com, AuntMinnie.com, KLASresearch.com, AI Health Review, Physician AI Tools, Scribd
Last updated: 2026-10-28
Ratings & Reviews
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Press & Coverage
AuntMinnie.com
Aikenist secures 510(k) clearance for AI PACS and RIS system
Aikenist Technologies received FDA 510(k) clearance for its QuickRad AI PACS and RIS with viewer product, a web-based PACS featuring a "Zero Footprint Viewer" with AI-assisted diagnosis, precision measurement tools, and structured reporting.
2025-03
Signify Research
New US PACS-RIS Entrant
India-based Aikenist Technologies obtained FDA 510(k) clearance for its QuickRad AI PACS and RIS plus viewer product, which includes measurement tools, structured reporting, and AI-assisted diagnosis, supporting third-party algorithms across various medical specialties.
2025-04
Digital Health News
Top 10 HealthTech AI Startups to Know: The Builders Reshaping India's Healthcare Future
Aikenist, founded in 2019, has developed an AI-based radiology suite, including QuickScan and QuickRad, to support diagnostic decision-making and provide faster interpretation, especially in environments with limited resources.
2026-03
StartUs Insights
Aikenist – AI-driven Radiology Suite
Aikenist enhances medical imaging processes with AI through products like QuickRad, QuickDiag, and QuickScan, which streamline radiology workflows, improve diagnosis, and optimize the workflow between diagnostic centers, clinics, and patients.
2024-06
Preqin
Aikenist Technologies Private Limited - overview
Aikenist raised seed funding in October 2024, led by Venture Catalysts, to enhance product development, expand its team, and improve infrastructure for its AI-based MRI solutions.
2024-10
Entrackr
Aikenist raises seed funding led by Venture Catalysts
Aikenist, a provider of Gen-AI-based smart workflow solutions, secured seed funding led by Venture Catalysts in October 2024, with participation from several investors including MARL Fund, Quant Fund, C-CAMP, and Nandak Growth Catalysts.
2024-10
aikenist.com
QuickRad: faster reporting. QuickFlow: connected workflow.
Aikenist's QuickRad focuses on faster reporting, while QuickFlow provides connected workflow orchestration, aiming to integrate intelligence into the imaging process from acquisition to sharing within a single ecosystem.
2026-10
NCG India
Aiken QuickRad - An end-to-end AI-integrated PACS and RIS system with a built-in diagnostic viewer.
Aiken QuickRad is an end-to-end AI-integrated PACS and RIS system with a built-in diagnostic viewer, offering FDA-approved tools for image storage, viewing, reporting, and analytics, making it a comprehensive platform for radiology departments.
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Frequently Asked Questions
QuickRad is an AI-powered PACS, RIS, and automated radiology reporting platform designed to connect every node of the radiology pipeline, from image acquisition to final report delivery. It offers a smart viewer for reviewing AI-highlighted findings, speech-to-text reporting, structured templates, and AI-generated reports, all within a DICOM-compliant cloud-based system. QuickRad aims to reduce reporting time by up to 60% and integrates with existing PACS and RIS systems, allowing AI overlays to appear directly in the radiologist's viewer without additional workflow steps.
QuickRad is FDA-cleared, HIPAA-compliant, CE-marked, ISO 13485 certified, and CDSCO licensed, ensuring data security and regulatory compliance. It utilizes AES-256 encryption for data at rest and TLS 1.3 in transit, with secure cloud infrastructure hosted on AWS and Azure, which maintain healthcare-grade compliance certifications. Patient data is de-identified before cloud processing, and the platform adheres to GDPR principles for data minimization, purpose limitation, and retention.
While specific accuracy metrics for QuickRad are not provided, AI-powered radiology tools generally aim to improve diagnostic accuracy and efficiency. Studies on similar AI systems have shown significant improvements in productivity, with some generative AI systems boosting radiologist efficiency by 15.5% to 40% without compromising accuracy, and even up to 80% in some cases. AI has also demonstrated high sensitivity and specificity in detecting various pathologies, such as 92.2% sensitivity and 95.6% specificity across 9 pathology categories in emergency chest CTs, and a high negative predictive value (NPV) for normal chest X-rays (99.43%).
Current AI systems, including QuickRad, are primarily image-based and may not inherently incorporate procedural or clinical information into diagnostic interpretation, which can lead to false positives if not integrated with clinical context. Standalone AI tools can also add complexity to the workflow if they require separate interfaces or handoffs. It's crucial for physicians to integrate AI-generated findings with bedside clinical assessment and, when necessary, confirmatory imaging.
QuickRad offers a comprehensive AI PACS, RIS, and reporting platform, distinguishing itself by integrating various aspects of the radiology workflow. Alternatives in the market often specialize in specific areas: some focus on acute triage (e.g., Aidoc, Viz.ai), others on finding detection per exam type (e.g., Annalise.ai, Lunit), and some on reporting and impressions (e.g., Rad AI, Nuance PowerScribe). QuickRad aims to provide an all-in-one solution, reducing the need for multiple standalone AI tools that can complicate workflows.
The pricing model for QuickRad is not explicitly detailed in the provided information. However, general imaging and diagnostics AI tools typically cost between $3 and $25 per study, with an average of $5-15 per study. Some vendors charge per radiologist per month, with plans ranging from $199 to $499 per radiologist per month when billed annually. Pricing can vary based on the volume of studies, modalities covered, number of AI models deployed, and PACS integration requirements.
Yes, QuickRad is designed for seamless integration with existing PACS and RIS systems through native DICOM integration. This allows AI overlays and findings to surface directly within the radiologist's viewer, eliminating the need for additional workflow steps or switching between different systems.
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| Last Verified: 2026-10-14 | First Added: 2026-04-17