Most AI tools can answer questions. ORTHO-X is designed to run the case with you.
Orthopedic care does not happen in isolated prompts.
It happens across fracture images, WhatsApp messages, intraoperative decisions, follow-up assessments, and hallway discussions between surgeons trying to make the right call under real clinical pressure.
That is exactly the gap ORTHO-X is built to close.
ORTHO-X is not another medical chatbot. It is a shared orthopedic case workspace where surgeons can review the same fracture, discuss classification and fixation strategy, capture operative reasoning, follow outcomes over time, and turn the entire case into structured documentation — or even a publication-ready teaching case.
In the mockup below, a real proximal femoral nail case moves from initial WhatsApp intake to final publication, including the key intraoperative moment when residual varus was recognized and corrected before final fixation.
The workflow is simple:
WhatsApp case intake → ORTHO-X case coordinator → orthopedic AI specialists → shared surgeon discussion threads → structured documentation → JBJS-style publication output
Why this matters for orthopedic teams
Most digital tools in healthcare still break the case into fragments.
Images live in one system. Notes live in another. Team discussion happens in chat. Operative reasoning is rarely captured well. And by the time a case is worth teaching or publishing, the most valuable insights have already been lost.
ORTHO-X is designed to keep those layers together.
Instead of asking AI isolated questions, the whole team works inside a shared case environment with persistent threads, structured clinical state, media, and specialist AI agents that contribute to the discussion as the case evolves.
That means:
- the classification is captured
- the fixation decision is documented
- the intraoperative pivot is not lost
- the follow-up is linked to the original plan
- the teaching value of the case is preserved from the start
A real PFN case, not a generic demo
The below mockup follows a typical but high-stakes case: an unstable 31-A2 pertrochanteric fracture in an elderly osteoporotic patient.
At first glance, this looks like a common trauma case.
But anyone who treats these fractures knows that success is often decided by small technical details — reduction quality, avoiding varus, implant choice, cephalic element positioning, and recognizing risk before failure happens.
In the ORTHO-X flow:
- the case starts with a WhatsApp intake
- AI supports fracture classification
- surgeons discuss PFN versus alternatives
- intraoperative imaging triggers a warning about residual varus
- the reduction is corrected before final fixation
- follow-up is assessed in the same shared case thread
- the case is then converted into both a JBJS-style manuscript draft and an editorial teaching piece
This is the difference between an AI demo and a clinical workflow system.
Check out the clickable PFN case flow demo:
What makes ORTHO-X different
Most so-called “AI for orthopedics” products still behave like assistants.
ORTHO-X behaves more like a digital fracture board.
It brings together:
- shared case threads for multiple surgeons
- specialist orthopedic AI agents for classification, planning, outcome review, and publishing
- structured case memory that persists across the life of the case
- multimodal input including imaging, video, notes, and operative discussion
- publishing workflows that turn captured cases into teaching and editorial output
So instead of generating one answer, ORTHO-X helps capture the full arc of a case:
analysis, discussion, execution, reflection, and publication.
Why WhatsApp matters more than another dashboard
One of the biggest reasons clinical software fails is simple: it asks surgeons to change behavior.
ORTHO-X does the opposite.
The system is designed around the tools orthopedic teams already use to communicate — especially WhatsApp — and then adds the missing layer of structure, persistence, and AI-supported reasoning behind the scenes.
That means a fracture case can begin where surgeons already are, while still becoming a structured, searchable, collaborative case record.
In other words:
low-friction communication on the front end, high-value case intelligence on the back end.
From case discussion to case publication
Some cases should not end when the patient leaves the ward.
They should become:
- teaching files
- department learning material
- editorial content
- formal case reports
That is why ORTHO-X includes a publishing pathway.
Once a case has been captured properly — with imaging, classification, implant rationale, intraoperative turning points, and follow-up — the system can help package it into:
- a JBJS-style case report draft
- a multimedia teaching post for The Daily Fracture
- internal educational summaries for teams and trainees
This turns routine documentation into something much more valuable:
captured clinical learning.
The bigger idea
The point of ORTHO-X is not to replace orthopedic judgment.
It is to make orthopedic judgment more visible, more collaborative, more structured, and more reusable.
Because the real asset in modern fracture care is not just the final operative note.
It is the reasoning that got the team there.
And once that reasoning is captured well, it can improve care, accelerate teaching, strengthen collaboration, and create publishable knowledge from real cases.
Get in touch!
Want to see how ORTHO-X could work for your orthopedic team, trauma network, or implant education workflow?
We are actively shaping the first clinical and publishing workflows now.
Get in touch with MAIVAN.ai to explore pilot use cases, collaboration, or early access.
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