Robots reshape surgery and diagnosis

Medical robots are moving from specialist showcases into mainstream care, changing how operations are performed, how scans are read and how hospitals manage overstretched staff. The shift is being driven by better imaging, faster computing, advances in minimally invasive surgery and growing pressure on health systems to cut delays, improve precision and reduce administrative burden. At the same time, regulators and clinicians are warning that the rush to deploy artificial intelligence in medicine must not outpace safety checks, training and accountability.

Operating theatres remain the most visible front line of the transformation. Robotic-assisted systems, led by platforms such as Intuitive Surgical’s da Vinci line, are now used across urology, gynaecology, colorectal and thoracic procedures, giving surgeons enhanced vision, finer instrument control and improved ergonomics during delicate operations. Intuitive’s da Vinci 5 received CE mark approval in Europe in July 2025, and the company said in January 2026 that the system had also secured US clearance for cardiac procedures at a limited number of sites. NHS England has set out one of the clearest public signals of where the field may be heading, saying it expects robotic support for about 500,000 operations a year by 2035, up from 70,000 in 2023/24.

For patients, the appeal is practical rather than futuristic. Hospitals and surgeons say robotic assistance can mean smaller incisions, less blood loss, shorter hospital stays and quicker recovery in selected procedures. For clinicians, it can reduce physical strain and make complex keyhole surgery easier to perform with consistency. Yet the technology is still an extension of the surgeon rather than a replacement. Even where autonomous features are advancing, such as AI-guided camera control or experimental machine-led surgical steps, the operating room remains firmly centred on human oversight. Reuters reported in 2025 that an AI-guided camera system had enabled a surgeon in Chile to perform a gallbladder operation without a human camera assistant, and separately described an experimental robot carrying out part of a gallbladder procedure autonomously. Those developments point to where the field may move next, but not to a near-term handover of surgery to machines.

Diagnostics is the other major engine of change. The clearest pattern in the United States remains the heavy concentration of authorised AI-enabled devices in imaging and related clinical software. The FDA says its list of AI-enabled medical devices is intended to show which products are authorised for marketing, while a 2025 Nature Digital Medicine review of more than 1,000 FDA authorisations found quantitative image analysis remained the most common application. That is why radiology, mammography, cardiology and pathology have become key test beds for AI tools that flag abnormalities, prioritise urgent scans and support clinicians in detecting patterns that may be missed under pressure. The promise is not only speed, but consistency and earlier detection.

Beyond theatres and scan rooms, healthcare robotics is also reshaping the less glamorous but critical routines that keep hospitals running. Mobile service robots are being used to move medicines, specimens and supplies, while AI systems are being deployed to forecast bed demand, automate documentation and ease nursing workloads. Reuters reported in March 2025 that Apollo Hospitals was investing in AI tools partly to manage staff strain, especially among nurses. Diligent Robotics, whose Moxi robot works in hospitals, told Reuters in October 2025 that its machines had completed more than 1.25 million deliveries across more than 25 US hospitals. The value proposition here is straightforward: every non-clinical task handed off to software or machines can free staff for bedside care.

Still, the technology’s rapid spread has sharpened concerns over safety, bias and oversight. Reuters reported in February 2026 that the introduction of AI into certain surgical and diagnostic devices had been followed by complaints of malfunctions and injuries, including cases tied to navigation systems used in sinus procedures. The report also cited research suggesting AI-enabled devices have been recalled at higher rates than comparable non-AI products. Regulators are under pressure to move faster as submissions grow, but public trust depends on proving that tools work as claimed across different patient groups and real-world clinical settings, not only in tightly controlled development environments.

That is where governance is becoming as important as engineering. The World Health Organization has repeatedly argued that AI in health should be guided by ethics, equity and human control, and its latest guidance on large multimodal models adds to earlier warnings that opaque systems can deepen exclusion or error if health data are poor, skewed or weakly supervised. In Britain, the Royal College of Surgeons has also pointed to gaps in national protocols and minimum training requirements for robotic surgery, underlining a wider truth across health systems: buying machines is easier than building safe pathways around them.