Ortho AI

by OrthoAI  · Based in India → — World's first evidence-based generative AI for orthopaedics. A 24×7 surgical companion with 11 specialized AI agents, grounded in peer-reviewed PubMed literature.
Neurosurgery Orthopedics Radiology
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Freemium (free and pro subscription)

Overview

Ortho AI is a software as a medical device (SaMD) system designed to provide preoperative planning data for orthopedic surgeries. It specifically assists in total hip arthroplasty, total knee arthroplasty, and lumbar spine fusion surgery. The software utilizes AI/ML models to process x-ray images, enabling the measurement of lengths, angles, and the position of implants relative to bone structures. It allows for the overlaying of digital annotations on radiological images and includes tools for performing measurements based on these images and annotations. Ortho AI is intended for use by orthopedic surgeons and orthopaedic/neurosurgeons for preoperative and intraoperative planning, offering semi-automated templating and planning capabilities. It is not intended for primary image interpretation and is not for use on mobile phones.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Preoperative planning for total hip arthroplasty
  • Preoperative planning for total knee arthroplasty
  • Preoperative planning for lumbar spine fusion surgery
  • Assists in measuring lengths, angles, and implant positions from x-ray images
  • Allows overlaying digital annotations on radiological images
  • Software as a Medical Device (SaMD)
  • Utilizes AI/ML models for planning
  • Semi-automated templating/planning for surgery

Use Cases

  • Preoperative planning for total hip replacement surgery
  • Preoperative planning for partial/total knee replacement surgery
  • Preoperative planning for lumbar spine fusion surgery
  • Assisting orthopedic surgeons in precise surgical measurements from imaging

What Physicians Need to Know

DICOM Support & Standards
Ortho AI supports DICOM images, which is the international standard for managing, storing, transmitting, and displaying medical images. This allows for seamless communication between various imaging devices, servers, and diagnostic workstations. The system can also accept other standard image formats like JPG and BMP. DICOM is crucial for comparing prior imaging studies, multi-modality imaging review, and ensuring data interoperability in an AI-enabled radiology setting. AI systems, like Ortho AI, interact within the medical imaging ecosystem by producing DICOM instances and secondary objects. It's important for AI algorithm providers to publish a DICOM conformance statement outlining how they interact with the ecosystem.
PACS Integration Method
Orthopedic imaging AI products often interface directly with current PACS systems. The most common integration pattern treats the AI system as a DICOM node, where PACS forwards completed studies to the AI system via DICOM C-STORE. The AI system then processes the images and returns findings to PACS as DICOM Structured Reports or annotated images. This method leverages existing DICOM infrastructure and allows the AI system to process asynchronously without impacting PACS performance. Effective AI integration into PACS requires automation of image management processes like storage, retrieval, and distribution, as well as support for real-time image analysis. Cloud-based orthopedic PACS systems are also emerging to address specialty-specific demands.
Reading Room Workflow Impact
AI in orthopedic imaging acts as a diagnostic assistant, improving accuracy and serving as a second opinion or triage tool. It screens routine radiographs for suspicious areas, allowing radiologists to focus on critical findings. AI can improve workflow by prioritizing images on worklists and highlighting regions of interest. It can also improve diagnostic certainty and reduce errors. However, AI is not a replacement for clinicians, and radiologists must continue to exercise their own judgment. Successful integration requires radiologists to adapt to new workflows and acquire skills for leveraging AI effectively.
AI Model Architecture (deep learning approach)
Ortho AI utilizes deep learning algorithms. Deep learning algorithms have shown promising results in orthopedic radiography for tasks such as fracture detection, classification of osteoarthritis, bone age assessment, and automated measurements of lower extremities. Modern AI-enhanced PACS systems utilize deep learning algorithms for image reconstruction, segmentation, and classification. Convolutional Neural Networks (CNNs) are a type of deep learning model frequently used in medical image analysis for tasks like image segmentation, classification, and 3D modeling. Hybrid architectures combining CNN feature extractors with Transformer encoders can outperform pure Vision Transformers (ViTs) on smaller datasets.
FDA Clearance Pathway (510k/De Novo)
The FDA has cleared over 1,000 AI/ML-enabled medical devices, with approximately 95% cleared through the 510(k) pathway. The 510(k) pathway is for devices that demonstrate substantial equivalence to an existing predicate device. The De Novo pathway is for novel devices with no predicate and low to moderate risk. The median review time for AI/ML 510(k) submissions is around 142-151 days, while De Novo reviews average 7-12 months. Ortho AI is a non-invasive software as a medical device (SaMD).
Supported Modalities (CT/MRI/X-ray/US)
Ortho AI requires preoperative radiographic images of the pelvis, knee, or spine as input. It is validated to analyze standard image formats and DICOM compliant images from digitally computed (CR) or directly digital (DX) formats. Generally, AI solutions in orthopedic imaging support X-rays, CT, and MRI. Multimodal AI integrates data from X-ray, CT, MRI, PET, and ultrasound to provide comprehensive assessments.
Sensitivity & Specificity Data
AI has demonstrated high accuracy in diagnosing orthopedic fractures from medical images. Studies show AI performing comparably to clinicians for fracture detection, with pooled sensitivity and specificity of 92% and 91% respectively for internal validation sets. Some studies indicate AI algorithms can detect fractures with up to 98% accuracy. For hip fractures, a deep learning model achieved 91% accuracy, 98% sensitivity, and 84% specificity. For distal radius fractures, AI achieved an AUC of 0.96, 93% accuracy, and 90% sensitivity, comparable to orthopedists and superior to radiologists. AI assistance significantly improved the accuracy and consistency of knee osteoarthritis grading. For rotator cuff tears, an automated detection system showed a high sensitivity of 92%.
RSNA/ACR Validation
The ACR Data Science Institute (DSI) was established to advance, validate, and implement AI in medical imaging. They collaborate with subspecialty societies, industry, and regulatory agencies on developing, evaluating, validating, and monitoring AI algorithms. RSNA and ACR have emphasized the need for AI algorithms to undergo testing using multi-site heterogeneous datasets to ensure generalizability. They also recommend rigorous post-market assessment and continuous monitoring of algorithms in clinical practice. External validation across different sites, imaging acquisition methods, and equipment is crucial for the scalability of AI tools.
Physician Tip

Ortho AI is designed to be a surgical companion, offering evidence-based answers to clinical questions, surgical techniques, and theoretical queries. It can help with complex clinical cases and article reviews. The tool provides real-time streaming answers with PubMed references and OrthoTV videos. Physicians can choose from different response styles (Formal, Learning, Concise, Explanatory) and browse curated literature by subspecialty. AI tools like Ortho AI are not meant to replace clinical judgment but to support decision-making and handle repetitive administrative tasks. It's important to understand the limitations of AI and continuously evaluate its effectiveness. AI can assist in patient education and communication. For orthopedic practices, AI can streamline operations, enhance patient engagement, and support data-driven decision-making.

Ortho AI is designed to integrate with existing PACS and modalities, eliminating the need for full system replacement. It supports DICOM standards for seamless communication with various imaging devices and systems. The most common integration method involves treating the AI system as a DICOM node, where studies are routed from PACS to the AI for processing, and results are returned as DICOM Structured Reports or annotated images. This approach leverages existing infrastructure and allows for asynchronous processing. AI-powered PACS integration is a key feature of many AI radiology software solutions. Successful integration requires addressing technical, operational, and ethical barriers, and developing training programs for radiologists to adapt to new workflows.

Details

Category Radiology & Imaging AI, Surgical AI
Pricing Freemium (free and pro subscription)
Free TrialUnknown
DeploymentMobile app (Apple Watch, iPhone, Android) and web access.
Mobile App1
Compliance
BAA AvailableUnknown
HIPAA CompliantUnknown
FDA Status Unknown
Integrations
EHR Not specified
Specialties Neurosurgery, Orthopedics, Radiology

What the Web Says

OrthoAI, launched in December 2023, is an AI-based chatbot designed as a surgical co-pilot for orthopedic surgeons, providing evidence-based information and decision support. It aims to enhance surgical precision, efficiency, and patient outcomes by offering timely, evidence-based information and experience-based decision support. The platform integrates with PubMed and OrthoTV, offering access to peer-reviewed literature and orthopedic videos. While it has been well-received by over 8000 users, some discussions on Reddit about AI in orthopedics, generally, express concerns about reliability and the need for human oversight, especially in critical decision-making.

Overall: Positive

Strengths

  • Provides evidence-based information and decision support for orthopedic surgeons.
  • Enhances surgical precision, efficiency, and patient outcomes.
  • Offers real-time informational assistance during surgery and helps with preoperative planning and postoperative care.
  • Integrates with PubMed and OrthoTV for access to current medical literature and orthopedic videos.
  • Can help in preparing surgical trolleys, ordering additional instruments and implants, and assisting OT staff with surgical checklists.
  • Aims to provide low-cost, direct-to-consumer, physician-verified orthopedic advice.

Limitations

  • Some AI tools in orthopedics are not super fast.
  • Concerns exist regarding the reliability of AI in critical applications and the need for human oversight.
  • AI-generated notes can sometimes be inaccurate or require significant editing by physicians.
  • AI may lack intuition compared to human physicians.
  • AI's performance in diagnostic tasks can be comparable to physicians but worse than experts in some cases.
  • Some AI tools for note summarization are not agentic, and others are strong for voice-based documentation but weak for administrative automation.

Based on reviews from: nih.gov, jocr.co.in, ovid.com, honeyhealth.ai, orthoai.in, apple.com, athenahealth.com, Current time information in Pune, IN., plos.org, spryhealth.ai, beckersspine.com, reddit.com, trustpilot.com, ehrranker.com, capterra.com, adit.com, physicianaitools.com, orthoconsult.ai, deepscribe.ai, healthcaredive.com, ochipandknee.com, deepcura.com, g2.com, blastra.io, researchgate.net, aiorthoassistant.com, ai-scan.dev

Last updated: 2026-10-31

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Press & Coverage

FDA
Ortho AI Demonstrates Substantial Equivalence to Predicate Device
Ortho AI, a software as a medical device (SaMD) system, has demonstrated substantial equivalence to a predicate device for preoperative planning in hip, knee, and lumbar spinal fusion surgeries. The software utilizes AI/ML models to semi-automate templating and planning tasks, providing key measurements to surgeons.
2025-01
Journal of Orthopaedic Case Reports (via NIH)
OrthoAI: A Generative AI Revolution in Orthopaedics
OrthoAI, also referred to as OrthoGPT, is presented as a generative AI model designed to transform clinical decision-making in orthopaedics. Launched on December 11, 2023, it leverages a curated database, PubMed access, and OrthoTV integration to provide evidence-based answers to orthopaedic queries.
2023-12
orthoai.co
OrthoAI Automates Dental Claim Validation with AI
OrthoAI offers an AI-powered platform to automate dental claim validation, providing auditable, explainable, and consistent decision records. The system aims to reduce error rates, auto-adjudicate claims, and significantly decrease review times for insurance operations.
unknown
orthoai.co
OrthoAI: Building Dental AI on Published Research
OrthoAI originated as a research program in computer vision and clinical AI at the Mohamed bin Zayed University of Artificial Intelligence, focusing on dental imaging analysis. The platform emphasizes evidence-based results, consistent standards for providers and payers, and explainable AI by design.
unknown
Apple App Store
Ortho AI - AIMD App Updates
The Ortho AI app by AIMD has received updates including AI Presentations for creating conference-ready slide decks, a Medical Image Generator, and analysis for lab reports and X-rays. The app offers 11 specialized AI agents for various orthopaedic subspecialties.
2026-09
Tracxn
France's DentalMonitoring secures $100M for Ortho AI
DentalMonitoring, a French company, secured $100 million in funding for Ortho AI. This news was reported in February 2026, indicating investment activity in the AI orthodontics space.
2026-02
orthoai.in
OrthoAI: Your AI Surgical Companion for Evidence-Based Orthopaedics
OrthoAI positions itself as the world's first evidence-based generative AI for orthopaedics, functioning as a 24/7 surgical companion. It answers complex clinical questions, surgical technique queries, and provides article reviews, with responses grounded in peer-reviewed literature from PubMed and specialized datasets.
unknown
Physician AI Tools
Ortho AI is an FDA-cleared software as a medical device (SaMD)
Ortho AI is highlighted as an FDA-cleared software as a medical device (SaMD) dedicated to transforming orthopedic care through the integration of machine learning. This indicates its regulatory approval for clinical use.
2026-07

Videos

Product demos, reviews, and walkthroughs for Ortho AI.

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Frequently Asked Questions

Ortho AI is designed to act as a diagnostic assistant, using deep learning algorithms to analyze radiographs and identify suspicious areas such as fractures, dislocations, and joint effusions. It serves as a second opinion and a triage tool, helping to screen routine radiographs and flag urgent cases, which can improve diagnostic accuracy and reduce turnaround times for reports.
Ortho AI is developed with a focus on regulatory compliance, ensuring data integrity and methodological consistency. It is designed to be updated dynamically with new regulatory guidelines from bodies like the EMA and FDA, and emphasizes cross-system interoperability and data standardization to reduce the administrative burden of compliance.
Yes, there are other AI tools in radiology that address specific clinical workflows. Some examples include Aidoc and Viz.ai for acute-care triage, Gleamer for musculoskeletal analysis, Qure.ai and Lunit for screening, Rad AI for streamlining reporting, and Subtle Medical for optimizing image acquisition.
While specific pricing for Ortho AI is not detailed, similar AI-powered clinical documentation tools for orthopedic practices can vary significantly. For example, some AI scribes offer flat monthly fees per provider, while others have higher monthly costs depending on volume tiers and may include setup fees.
A key limitation of AI-assisted fracture detection is that each model often requires specific training on the body part being assessed, unlike humans who can transfer knowledge across different body parts. Additionally, models may be less reliable in detecting subtle fractures and often provide binary outputs (fracture present or not) without detailed descriptions, which means they augment rather than replace human expertise.
Ortho AI, like many orthopedic imaging AI products, is designed to interface directly with existing PACS (Picture Archiving and Communication System) and RIS (Radiology Information System) systems. The goal is to integrate seamlessly into current workflows, allowing for efficient storage, retrieval, and exchange of digital images without requiring special steps or different interfaces for radiologists.
AI algorithms in orthopedics have demonstrated high accuracy in fracture detection, with some studies showing performance comparable to, or even surpassing, that of musculoskeletal radiology specialists, achieving sensitivities of approximately 90% and specificities of around 92%. Clinical evidence suggests that AI support can improve clinician sensitivity in fracture detection, for instance, from 86.5% to 95.5%.

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Suggest an Edit → | Report an issue | Last Verified: 2026-04-18 | First Added: 2026-04-18
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