Beyond Chatbots: How AI Is Quietly Transforming UK Policing and Healthcare

• by Anthony Orjally
• Advent IM Blog

When most people hear the words “artificial intelligence,” their minds jump to large language models (LLMs) and chatbots. The headlines tend to focus on generative AI, whether for its potential or its pitfalls. Yet the reality in the UK is that some of the most powerful applications of AI are happening well outside the world of conversational tools.

Two areas where AI is already making a tangible difference are policing and healthcare. Both are sectors where data volumes are immense, decisions carry high stakes, and public trust is critical. And in both cases, AI is being deployed not to replace professionals, but to augment them.

AI in policing: risk, resources and responsibility

Policing in the UK is data-rich but resource-stretched. Forces are turning to AI and machine learning to help identify patterns in criminal behaviour, anticipate risks, and support decisions that must often be made rapidly.

• West Midlands Police and NDAS: Working with partners, the force tested models to spot individuals at higher risk of violent reoffending. The intent was to enable earlier interventions before crime occurs, a shift from purely reactive policing to proactive harm reduction. This type of system has national implications, as other forces look to adopt similar approaches. But the pilot also drew scrutiny over transparency and potential bias. It highlighted the need for clear oversight mechanisms, rigorous auditing, and robust governance if public trust is to be maintained.
• Durham Constabulary’s Harm Assessment Risk Tool (HART): Developed with academic support, HART was used to estimate the likelihood of reoffending among individuals in custody. It was never intended to replace human judgment, but to support decisions such as whether diversion into rehabilitation programmes was appropriate. Adjustments were made to the model after concerns were raised about potential unfairness. This demonstrates both the utility of such systems and the essential role of ethical review.

More broadly, forces across the UK have experimented with AI-based hotspot mapping, network analysis, and anomaly detection in financial crime. Each initiative has shown potential value, but all have underlined the same governance lesson: policing must not only use AI responsibly, it must be seen to do so. Without transparency, explainability, and accountability, even effective tools risk losing legitimacy.

AI in healthcare: precision, efficiency and patient outcomes

In healthcare, the story is less about predictive risk and more about clinical and operational efficiency. NHS trusts and research partners are already embedding AI in areas where speed and accuracy are vital.

• Breast cancer screening: In the East Midlands, an NHS consortium has partnered with technology providers to use AI in mammography. These systems review mammograms to support radiologists in spotting cancers earlier and with greater consistency. Early results suggest both improved detection rates and reduced waiting times. Importantly, these AI tools are CE-marked, which means they have been assessed against regulatory standards for clinical safety.
• Chest X-ray triage: Trials in Somerset and West Yorkshire used deep learning to identify routine X-rays that showed no abnormalities. By automatically ruling out the normal cases, the system freed up radiologists to focus on the more complex scans. This has accelerated reporting times and helped reduce backlogs, a persistent challenge for the NHS.
• Predictive health coaching: Another UK trial applied AI to patient data to predict which individuals were most likely to require unplanned hospital admission. Those identified were offered proactive health coaching. The intervention led to measurable reductions in emergency attendances and admissions, proving that AI can add value not only in diagnosis but also in population health management.

Here too, the emphasis is on augmentation, not replacement. AI helps clinicians handle workload pressures, provides faster insights, and supports better patient outcomes. But the same caveats apply: these tools must be transparent, explainable, and accountable. Patients and professionals alike need confidence that the technology is being applied safely and fairly.

Governance first, not last

The thread running through both policing and healthcare is that AI is only as valuable as the governance wrapped around it. Bias in training data, lack of explainability, and questions about accountability are not abstract problems—they can directly affect liberty, health outcomes, and public confidence.

For UK organisations, the lesson is clear. AI must not be treated as a bolt-on capability that is adopted and regulated later. Governance, risk, and compliance (GRC) should be built into every deployment from the outset. That means:

• Rigorous risk assessment before systems are rolled out.
• Independent auditing and continuous monitoring.
• Transparency about how models work and how outputs are used.
• Clear lines of accountability for decisions influenced by AI.

AI in policing and healthcare is not about futuristic speculation—it is already here, working quietly in the background. From helping police forces allocate resources and reduce reoffending, to supporting clinicians in spotting cancers earlier and clearing diagnostic backlogs, these systems are proving their worth.

But the benefits come with obligations. Without governance, public trust is easily eroded. With governance, AI can deliver safer communities, healthier patients, and more resilient services.

The UK now faces a critical task: to lead not just in adopting AI, but in demonstrating how to deploy it responsibly. That, ultimately, will be the difference between technology that is accepted—and technology that is resisted.

Written by Ellie Hurst, Commercial Director.