AI in Healthcare: 10 Promising Interventions for Improved Patient Care

At its core, Artificial Intelligence involves using digital technology to perform tasks that traditionally required human intelligence, such as facial recognition and navigation systems. Most AI systems operate by analyzing vast datasets, identifying patterns, learning from this data, and continuously improving over time. There are two primary types of AI currently in use: Generative AI, exemplified by tools like ChatGPT, which creates new content (text, images, music) based on learned patterns; and Predictive AI, which leverages extensive historical data to make accurate forecasts and estimations about future events. In the healthcare domain, the majority of AI applications are based on predictive AI, utilizing carefully curated data from hospitals and research trials. These systems are instrumental in identifying individuals at high risk for specific conditions, aiding in disease diagnosis, and tailoring treatments to individual patients.

Beyond initial diagnosis, AI is also showing promise in predicting the progression of chronic conditions, allowing for proactive interventions. Wet age-related macular degeneration (wet AMD), a leading cause of sight loss in the UK, can progress rapidly, making early detection and intervention crucial. AI models have demonstrated a superior ability to predict whether individuals with wet AMD in one eye will develop the condition in the second eye within six months, outperforming clinical experts. This risk stratification capability is vital for directing healthcare resources effectively and minimizing the impact of sight loss on patients' lives. Similarly, for ulcerative colitis, a long-term condition causing bowel inflammation, AI is being used to analyze biopsy samples. Researchers have developed an AI tool that can accurately distinguish between remission and active disease, and predict inflammation with a degree of accuracy comparable to pathologists. This technology has the potential to expedite and standardize the assessment of ulcerative colitis, providing doctors with more accurate prognostic information and improving patient management.

The efficiency of healthcare services is a constant focus, and AI offers innovative solutions for optimizing patient flow and resource allocation. One significant area of impact is reducing the pressure on Accident & Emergency (A&E) departments. Ambulances transport a substantial number of individuals to A&E each month, and AI can assist paramedics in predicting which patients do not require hospital admission. By analyzing over 100,000 linked ambulance and A&E records, an AI model has been developed that can predict avoidable A&E attendances with high accuracy, considering factors like mobility, vital signs, and allergic reactions. This model has demonstrated fairness across different demographics. While further refinement is needed to define 'avoidable' trips more robustly, this application has the potential to streamline ambulance services and improve overall efficiency.

The UK's extensive national health data provides a rich foundation for developing AI tools. However, ensuring the safety, transparency, and fairness of these applications is paramount. Access to data must be strictly regulated and data must be kept secure. To improve AI algorithms and prevent biases, innovations need to be based on data from diverse and varied sources. Early AI applications sometimes failed to account for population diversity, leading to suboptimal performance, a challenge that researchers are now actively addressing. Building public and professional trust in AI is essential, as is ensuring that these technologies do not exacerbate existing healthcare inequalities. Research is actively exploring methods to develop AI innovations that avoid embedding such biases. Furthermore, the NHS workforce requires preparation and understanding of AI's potential impact on care pathways and user experiences. The NHS Long Term Plan recognizes AI as a key component of digitally-enabled care, and regulators are establishing safety standards for AI innovations. The government's National AI Strategy and research funding through the NIHR are supporting the development and real-world testing of AI in healthcare.

The ten examples presented in this collection represent a current snapshot of the extensive research dedicated to addressing critical health challenges through AI. They contribute to a growing body of evidence demonstrating the significant benefits that this digitally enhanced analytical capacity can bring to the NHS. Previous studies have already shown AI's utility in helping GPs identify patients at risk of cancer in primary care and in developing specific tools for high-risk colon and skin cancer detection. The research collectively highlights AI's potential to make healthcare services more efficient, enabling better prediction of patient needs. It can empower doctors to diagnose conditions earlier and more accurately, and facilitate the delivery of personalized treatments. AI can assist in selecting patients most likely to benefit from specific therapies, identify diseases at an early stage, and improve service efficiency through enhanced need prediction. While all discussed technologies require further research to fully understand their impact on routine clinical practice, long-term patient outcomes, and cost-effectiveness, careful regulation remains essential. The public and NHS staff need to trust technology-enabled care, and the high-quality, transparent, and inclusive research showcased here is instrumental in building that trust and realizing AI's transformative potential in healthcare.