By Lucas Najún Dubos, MD MSc & MBA, Healthcare & Life Sciences Partner at Globant
Artificial intelligence is starting to transform how surgery is delivered across the NHS.
While much of the public debate centres on chatbots or robotic assistance, some of the most impactful advances are happening behind the scenes.
In fact, just last year, researchers at Johns Hopkins University and Stanford University demonstrated AI’s potential by training surgical robots to perform surgical tasks, including lifting body tissue and suturing wounds with a needle.
AI is also being used to reduce cancellations, support clinical decisions, and help hospitals deliver care that is safer, faster and more sustainable.
Predictive scheduling helps tackle backlogs
NHS England’s waiting list stood at 7.39 million people in April 2025.
Although this marks a small improvement, in that snapshot 60.9 per cent of patients were within the 18-week referral-to-treatment standard, leaving roughly 2.9 million waiting longer, while 190,086 people had already waited more than a year for care.
These prolonged waits continue to carry clinical risk and remain a major operational challenge for trusts. These delays have clinical consequences and remain a major operational challenge.
One of the most common causes of delay is short-notice surgical cancellations.
The NHS cancels over 135,000 operations on the day of surgery annually, at a cost of around £400 million, largely due to scheduling issues, staff shortages or theatre bottlenecks.
With the NHS investing more than £34 million specifically to address surgical backlogs, improving scheduling has become a top priority.
AI offers a scalable way to address this. Predictive analytics can flag upcoming scheduling conflicts by analysing historic theatre data, real-time bed capacity, staff rotas and equipment status.
Lucas Najún Dubos
Specifically, AI can analyse the typical duration of different procedures and real-time hospital capacity to predict scheduling conflicts before they arise.
If a high-risk case is likely to overrun or require intensive recovery, AI can recommend rescheduling or resource adjustments before the day of surgery.
Hospitals like Addenbrooke’s in Cambridge are already piloting machine learning tools to streamline theatre utilisation and staffing models.
These tools can also support standby patient scheduling.
This is an approach already endorsed by the Elective Recovery Plan by identifying suitable candidates to fill cancelled slots with minimal notice, helping trusts maximise capacity.
Imaging intelligence supports clinical accuracy
Within the theatre itself, AI is improving how surgeons see and respond to anatomical complexity.
Tools such as AI-guided intraoperative imaging help clinicians visualise tissues and structures with higher clarity, supporting more precise incisions and reducing the likelihood of post-operative complications.
Hospitals such as Moorfields Eye Hospital have integrated deep learning into diagnostic imaging workflows, helping detect retinal abnormalities that would otherwise be missed.
These same imaging approaches are being translated to other surgical disciplines, including oncology and orthopaedics.
Pre-operatively, AI can also assist in treatment planning. Algorithms trained on thousands of previous cases can propose optimal surgical pathways tailored to the patient’s history, improving preparation and reducing in-theatre decision burden.
Importantly, AI complements clinical expertise. It acts as an intelligent support layer that sharpens decision-making without taking autonomy away from the surgical team.
Strengthening the NHS without stretching it
Staffing remains one of the NHS’s most pressing challenges.
As of March 2025, NHS England reports approximately 165,000 vacancies across the service, including over 31,000 unfilled nursing roles.
There are also significant gaps in specialties such as anaesthetics, radiology, and perioperative care.
These staffing shortfalls directly impact operating theatre throughput and take a toll on staff wellbeing.
AI doesn’t fix the workforce gap, but it helps NHS teams use the resources they do have more effectively.
Smart scheduling tools can redistribute staff during periods of peak demand, identify suitable replacements for last-minute absences, and design rotas that protect against burnout.
In 2024–2025, NHS England delivered 18 million treatments, setting a new record.
Technologies that reduce duplication, automate admin and pre-empt delays will be key to maintaining that momentum, especially as Trusts face continued cost pressures.
AI also lays the groundwork for broader digital transformation. With real-time visibility on patient flow and resource usage, NHS leaders can respond faster to disruption and allocate resources where they are needed most.
AI adoption depends on public trust
Despite growing support within the NHS, public perceptions of AI remain mixed.
A 2024 survey by the Health Foundation found that 76 per cent of NHS staff support AI in clinical care, compared with 54 per cent of the general public.
While many patients accept AI when guided by a clinician, concerns remain around data errors, loss of personal connection and reduced human oversight.
Among the public, 30 per cent worry about misdiagnosis, and 53 per cent feel AI could reduce personal interaction with doctors.
Notably, 65 per cent of NHS staff share those concerns.
These insights underscore the importance of responsible deployment.
Patients want reassurance that AI is not replacing human expertise. NHS AI strategies must ensure tools are subject to expert validation, transparent governance and designed with patient-centred values from the outset.
From innovation to impact
AI is already improving how the NHS schedules, plans and performs surgery.
It is not a speculative technology, it is a practical tool that can help hospitals meet rising demand while protecting care quality.
To scale its impact, NHS Trusts must embed AI into everyday operations while listening to the patients and clinicians it affects.
With the right safeguards, strong clinical leadership and an eye on equity, AI has the potential to become a cornerstone of a smarter, more resilient surgical future.
This is not about disruption for its own sake.
It’s about designing better systems to support the clinicians we trust and the patients who need them most.
