The Role of AI and AI Agents in Orthopedics: Expert Insights

Enhatch's CTO, Devin White, shares his insights on the role of AI and AI agents in orthopedics.

He shares practical applications of AI in orthopedics, including automated bone segmentation, preoperative planning, and alignment assistance in joint replacements.

Devin also addresses key challenges in implementing AI in healthcare and offers strategic advice for medtech companies looking to leverage AI.

About Devin White

With over two decades of technology experience spanning CAD, product design, and software development, Devin White brings a unique perspective to medtech innovation.

Now, as Chief Technology Officer at Enhatch, Devin is applying this expertise to address complex surgical planning and workflow optimization challenges through innovative AI applications.

Devin has led diverse teams across multiple tech companies and has extensive experience turning innovative ideas into practical solutions.

In this interview, Devin shares valuable insights on the evolving role of AI and AI agents in orthopedics. His practical perspective clearly explains AI's current capabilities and future potential in orthopedic care.

Q: Can you tell us about your journey as a CTO and what led you to explore AI in orthopedics?

Devin White: My Journey as a CTO has been driven by a passion for leveraging technology to solve complex problems in the medical space.

With a background in CAD, product design, and software development, I've worked extensively in the medtech industry, focusing on surgical planning, preoperative workflows, and automation.

AI became a natural extension of this work as we sought to enhance decision-making, improve accuracy, and reduce time-consuming manual processes in orthopedic procedures.

Q: What are some key problems that AI and AI agents can solve in orthopedics? How do you see AI agents shaping the future of orthopedic surgeries over the next decade?

Devin White: AI and AI agents can address several challenges in orthopedics, including:

• Preoperative planning: Automating segmentation of CT/MRI scans, optimizing implant selection, and generating precise 3D models.
• Intraoperative guidance: Enhance navigation, predict outcomes, and improve alignment accuracy in joint replacements.
• Postoperative monitoring: AI-driven analytics to assess patient recovery, identify complications earlier, and optimize rehabilitation.
  

Over the next decade, AI agents will streamline workflows, integrate seamlessly with robotic surgery platforms, and support personalized treatment plans based on real-time patient data.

Q: Can you share an example where AI-driven automation is improving efficiency or accuracy in orthopedic procedures today?

Devin White: One example is the use of AI in automated bone segmentation from CT scans.

Traditionally, this has been a manual and time-intensive process requiring expert input. AI models can now segment bones in seconds, with high precision, allowing surgeons to visualize and plan procedures more efficiently.

Another application is AI-powered alignment assistance in knee and hip replacement surgeries, where machine learning models help ensure optimal implant positioning, reducing the risk of misalignment and improving patient outcomes.

Q: What are the biggest challenges in integrating AI agents into orthopedic workflows?

Devin White: When integrating AI and AI agents in orthopedic workflows one needs to think about some key aspects:

• Regulatory compliance: AI in medical applications must meet stringent FDA and CE approval standards, requiring explainability and validation.
• Clinical adoption: Surgeons and clinicians need to trust AI recommendations. Building trust requires ensuring transparency and minimizing the "black box" effect.
• Data variability: Medical imaging and patient anatomy vary significantly. This can make generalization across cases a challenge.

Q: How does one address concerns about AI accuracy and regulatory compliance in medical applications?

Devin White: When leveraging AI in medical applications, it is essential to ensure:

• Robust training datasets: Ensuring AI models are trained on diverse, high-quality datasets to improve generalization.
• Explainable AI: Providing clear insights into how AI makes decisions, improving clinician trust.
• Continuous validation: Conducting extensive testing and retrospective studies to demonstrate efficacy.

Q: What advice would you give to medtech companies looking to adopt AI-driven solutions in orthopedics?

Devin White: If you are a medtech company looking to leverage AI, I would recommend the following:

• Collaborate with clinicians: Engage orthopedic surgeons early to ensure the AI solution addresses real-world needs.
• Prioritize usability: AI should enhance, not complicate, clinical workflows. Intuitive interfaces and seamless integrations are key.
• Invest in validation: Prove efficacy through clinical studies and regulatory approvals before scaling.
• Plan for continuous learning: AI models should be adaptable, learning from new data and improving over time to stay relevant.