Across industries, conversations about AI and automation often fall into a familiar narrative: humans versus machines. But in reality, it’s not a competition—it’s a collaboration. Nowhere is this more important than in healthcare, where every decision carries profound, life-changing weight.

The healthcare sector is at a pivotal moment. AI is advancing at an unprecedented pace, yet the need for human judgment, empathy, and expertise remains as critical as ever. Rather than replacing clinicians, AI is emerging as a powerful ally—amplifying human capability, accelerating workflows, and unlocking new levels of personalized care.

At Axial3D, we’re witnessing this transformation firsthand. The narrative is shifting from replacement to reinforcement, as clinical teams adopt AI and automation not to do less, but to do more—with greater speed, accuracy, and precision for every patient.

Not a Battle—A Collaboration

AI in healthcare is doing more than turning heads—it’s transforming workflows, accelerating insights, and unlocking new levels of precision. And recent research shows just how impactful that human-AI partnership can be.

A 2024 meta-analysis published in npj Digital Medicine evaluated the effect of AI support in radiology and found some game-changing results: image reading time dropped by 27.2%, and the number of images needing human review was reduced by up to 61.7%. That’s not just about speed—it’s about freeing up clinicians to focus where their expertise matters most, without sacrificing diagnostic accuracy.

Beyond radiology, AI is automating some of the most time-intensive steps in surgical planning for medtech companies and hospitals. It can take complex DICOM imaging data and generate 3D anatomical models in minutes instead of days. It identifies anatomical structures, automates segmentation, and helps personalize how we approach each case. The result? More consistent preparation and better-informed decisions.

But none of this replaces the critical role of the surgeon. As our CTO Rory Hanratty shared during our Patient Specific Conversations series:

“The surgeon remains at the center of every clinical decision. AI and automation should remove friction—not remove control.”

In other words, AI can help speed up decision-making, but it doesn’t make the decisions. It’s here to support clinicians—not replace them—so they can treat patients with clarity, confidence, and care.

Scaling Personalized Care

One of the biggest challenges in healthcare has always been scaling personalized treatment. Customizing care for each patient traditionally demanded significant time, resources, and manual effort—making it difficult to keep pace with growing clinical demand. But AI is rewriting that equation.

By automating the conversion of medical imaging data into accurate, patient-specific 3D files, AI is making personalized surgical planning more accessible—not just for rare or complex cases, but for every patient. What once took days of manual segmentation and modeling can now happen in minutes, with AI handling the repetitive, time-intensive steps. That’s how scale becomes possible—without compromising quality.

Of course, the human element remains essential. Surgeons, radiologists, multidisciplinary teams and product development, teams use these AI-generated models to collaborate, strategize, and deliver the best outcome for each case. AI doesn’t replace human judgment—it empowers it with faster, clearer insights.

One area seeing particularly rapid progress is medical image segmentation. A groundbreaking 2024 study published on arXiv introduced the IMed-361M dataset—an unprecedented collection of over 361 million segmentation masks from 110 medical imaging datasets. This massive benchmark is fueling the development of foundation models in medical computer vision, enabling AI to identify anatomical structures with near-expert accuracy at scale.

These advances are helping engineers and clinicians generate high-resolution 3D anatomical models directly from standard imaging, speeding up diagnosis and surgical planning while ensuring every decision stays in the hands of those who know patients best. It’s not about choosing between AI or human expertise—it’s about combining the best of both to deliver smarter, more scalable care.

What Makes AI Work in the Real World

For AI to really move the needle in healthcare, it has to be implemented the right way. A 2024 analysis in Frontiers in Digital Health broke down what makes AI adoption successful in clinical settings—and it’s not just about having the flashiest tech.

They found that success hinges on three big areas:

• Setting the right policies
• Building smart, privacy-first technology
• Measuring the actual impact on care and costs

One of the biggest takeaways? The best AI systems don’t try to upend clinical workflows—they fit into them. That means being built around trust, transparency, and respect for patient privacy. When AI is designed to support (not replace) clinicians, it becomes a tool teams actually want to use. And that’s where the real transformation starts.

Why Human Interaction Still Matters

Even the most advanced algorithms can’t grasp the full context of a patient’s life. A 3D model might pinpoint the exact location of a tumor, fracture, or abnormality—but it’s the multidisciplinary team that factors in lifestyle, comorbidities, and personal goals to decide on the right treatment.

We’ve seen this firsthand. In one case, an 82-year-old patient with a leaking mitral valve needed urgent surgery. A 3D model generated through our cloud-based platform gave the team a fast, detailed view of the anatomy—but it was the collaboration between cardiologists, imaging specialists, and surgeons that determined the best path forward. AI provided clarity; clinicians provided care.

That same dynamic applies in medtech. As Richard Swanson from Onkos Surgical described in a recent webinar, it’s a “back-and-forth collaborative approach,” with the surgeon always driving clinical decisions with technology supporting and optimizing workflows.

At Axial3D, we make that collaboration easier. We take on the manual, time-consuming tasks—image acquisition, segmentation, landmarking and more —so medtech teams and surgeons can focus on design, preferences, and patient-specific planning. The result? Faster turnaround, more tailored solutions, and better alignment with the clinical team’s goals.

And behind the scenes, human expertise is built into every step. Engineers validate segmentations. Clinical advisors review edge cases. Our AI is there to accelerate—not override—human decision-making.

Whether in hospitals or medtech companies, the best outcomes happen when people and AI work together—each doing what they do best.

What Hospitals and Device Companies Are Comfortable With

Through our work with hospitals and medical device companies, we’ve learned that the most comfortable applications of AI are the ones that:

• Support, but don’t substitute the clinician’s expertise
• Speed up repetitive or time-consuming tasks, like image acquisition and  segmentation
• Fit into existing workflows, rather than disrupting them
• Maintain transparency and traceability, particularly for regulatory compliance
  

Healthcare leaders don’t want a black-box solution. They want to understand how AI fits into their chain of care, and how it complements—not competes with—the people delivering it.

AI as the Enabler of Personalized Care

At Axial3D, we often say that accuracy unlocks personalization—and with AI, we can now scale processes that used to take days of manual work. That means more patients can benefit from tailored care, not just the rare or complex cases. But it’s not just about speed or scale. Human interaction is still what makes personalization meaningful.

The most forward-thinking healthcare organizations aren’t asking whether to use AI—they’re asking how to use it to support clinical teams, improve workflows, and deliver better outcomes. Because when AI and human intelligence work together, everyone wins.