The NHS' plan to slow workforce growth while accelerating the use of artificial intelligence has sparked a familiar debate about whether technology will replace healthcare jobs. However, the binary question, ‘will AI replace clinicians?' arguably misses an important point. The NHS's productivity problem isn't necessarily solved by swapping people for software, but by redesigning how work is distributed across the sector.
AI's most valuable role is arguably as a ‘frontline redesign' tool, shifting appropriate, protocolised tasks that currently consume specialist capacity into community settings, with AI used to standardise quality, support decision-making and trigger escalation, so specialists can focus on complex care.
The technologies that change systems for the better are those that redesign work, not eliminate it.
Shifting the conversation from workforce numbers to redesign
The NHS is undoubtedly facing one of the most challenging periods in its history with severe budget constraints limiting new hires, while specialist services face growing backlogs.
The question we should be asking is not should we adopt AI instead of humans, rather how can we design workflows so patients can access the right care, in the right place, faster.
Ear and hearing health as a blueprint
As a former frontline ENT surgeon, I have seen first hand the growing pressure facing specialist services.
ENT has one of the longest waiting lists in the NHS, with 586,000 currently waiting for treatment, with a typical wait time of 14 weeks. Around 15,000 patients have waited for treatment for longer than a year. AI has a vital role to play in empowering these frontline services and reducing pressure on NHS staff.
While some patients require complex specialist intervention, many enter the system with common ear and hearing health concerns that can be managed appropriately through neighbourhood services, such as pharmacies or local audiologists, without reaching a hospital consultant.
However, all too often, specialist capacity is consumed by routine activity, serving to increase pressure on the NHS. The result is a bottleneck where patients wait longer, and hospitals struggle to focus resources where they can deliver the greatest impact.
The limits of AI without human judgment
There are certain tasks that AI is suitable for including reporting, summarising information, or administrative workflows. While these require little human input, there is still some oversight required to ensure the output is correct and useable, especially in clinical settings.
There are also tasks where AI is used, whereby even greater human input is required. This includes decision support and analysis and insight generation.
When it comes to ear and hearing healthcare, AI can be used to automatically assess image quality in real time and estimate wax levels to support more reliable examinations. Where findings suggest a potential need for specialist support, the system highlights this, supporting timely and appropriate onward referral.
If neighbourhood settings, such as pharmacies, are equipped with the right technology, we can unlock a genuinely different model of care, one that brings services closer to patients. This provides healthcare professionals confidence to manage appropriate cases locally, while preserving specialist capacity for the most complex cases.
The future of healthcare will be built around ‘top of licence' working
Automation via AI serves to shift clinicians' focus from screens and technology back to patients by eliminating repetitive, low-value administrative tasks. When people are overwhelmed with these types of tasks, they have less time to focus on vital, human centred work, such as giving patient advice. For example, if pharmacists are inundated with manual data processing such as reviewing a large number of images to check infections for wax, they then have less time for consultation where alternative symptoms might be identified.
Without AI to assist them, there is a risk that pharmacists and audiologists could default to what is called ‘defensive medicine', whereby they refer many more patients to specialists clinics just to be safe.
AI helps break that cycle. By supporting frontline practitioners with evidence-based assessments, standardised decision-making and clear escalation pathways, it gives them the confidence to manage appropriate cases closer to home while ensuring patients who need specialist intervention are identified quickly. The result is a more efficient use of clinical expertise, shorter waiting times and a system that directs specialist capacity where it can deliver the greatest value.
Looking ahead
It is clear the notion of simply replacing healthcare professionals with technology is a redundant one. AI is most effective when it is combined with human expertise and insight. Effective redesign of the workforce, rather than replacing clinicians with technology, will be key in ensuring patients can not only access care more easily in their communities, but that healthcare practitioners' skills are used in a way that has the greatest impact.
