Build Computer-Assisted Surgical Planning and Intraoperative Guidance Software

1. 1

   Pick a procedure type and clinical partner

   Surgical software must be specific: choose a procedure type (for example an orthopedic or spine procedure common in ASCs) and partner with surgeons who will co-develop and validate it. Broad, unfocused surgical AI does not clear regulatory or clinical bars. The procedure and clinical partnership define the product.

2. 2

   Build planning and guidance that improves outcomes

   The software may help plan the procedure (imaging analysis, implant sizing, approach) and guide it intraoperatively, aiming for better, more consistent outcomes. Build to a clear, measurable clinical benefit. Without a demonstrable outcome improvement, neither regulators nor surgeons will adopt it.

3. 3

   Pursue the required regulatory clearance

   Clinical software that informs surgical decisions often qualifies as software as a medical device and may require FDA clearance (or equivalent), which drives the long timeline and high cost. Plan the regulatory pathway early with expert help. Selling unclear clinical software is not an option; clearance is the gate.

4. 4

   Validate rigorously and publish evidence

   Surgeons adopt on evidence, so rigorous validation and, ideally, published outcomes are part of the go-to-market, not an afterthought. Build the clinical evidence base as you build the product. Evidence is both the regulatory requirement and the sales asset.

5. 5

   Sell on outcomes and consistency

   Sell to ASCs, surgeons, and systems on improved outcomes, reduced variability, and the ability to bring more complex cases into the ambulatory setting safely. As high-value procedures migrate to ASCs, guidance that supports that migration is valuable. This is a long-horizon, high-value business, not a quick launch.

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Best for: Well-funded clinical-AI and medtech teams that can clear regulatory and validation requirements

Not for: Not for a bootstrapped or impatient team; regulatory clearance and clinical validation make this the longest, hardest AI build in this file. Not for a founder who wants patient risk-stratification or scheduling, which are separate, lighter cards. And not for anyone unwilling to partner deeply with surgeons and build a clinical evidence base.