Artificial intelligence (AI) has come a long way in healthcare. What started as a tool used mainly for streamlining admin or aiding diagnosis via medical imaging, is now taking on a much larger role, fundamentally reshaping how care is designed, delivered, and experienced. Whether it’s tailoring treatment plans or helping systems learn from every patient interaction, AI is fast becoming an essential tool for healthcare workers across the globe.
At the heart of this shift are breakthroughs in machine learning and natural language processing (NLP). NLP is what helps systems understand clinical language, turning previously unstructured information into structured information. This structured data can reveal hidden patterns and help guide next steps, from diagnosis to optimising treatment protocols. These technologies can now tap into a vast range of data sources, from medical records and imaging to genetic information and wearable sensors, to help clinicians act quickly and accurately.
From retrospective to real-time intelligence
Traditionally, data in healthcare has been used to look back, reviewing patient outcomes and identifying long-term health trends. But with the help of AI, clinicians are moving beyond retrospective analysis toward real-time insights and predictive intelligence.
AI is transforming healthcare by boosting the accuracy and efficiency of predictive models. By processing vast and complex datasets, AI can uncover patterns and correlations that may be missed by human researchers and clinicians. This allows for more precise forecasting and timely interventions. Today’s AI models can analyse multimodal data, ranging from clinical records and genomic and epigenetic profiles to medical imaging and social determinants of health, to guide more personalised and proactive care.
Predictive analytics, in particular, is helping healthcare systems anticipate patient risks before symptoms emerge. For instance, AI has been used to identify individuals at high risk of cardiac conditions. A recent study analysing over 240,000 ambulatory ECGs demonstrated that an AI algorithm could accurately flag patients likely to develop a life-threatening heart rhythm within two weeks. With an accuracy rate exceeding 70%, this tool enables clinicians to intervene early and potentially prevent cardiac arrest.
Importantly, through these types of studies we are starting to understand where more nuanced research may be needed. For example, women present with different symptoms to those classically used via patient history to suggest a diagnosis of myocardial infarction (heart attack). Currently, women experience fewer cardiac events but face more adverse outcomes partly due to delayed diagnosis. Using historic data, researchers are starting to understand these differences and develop sex-specific diagnostic and treatment guidelines.
Optimising clinician time and outcomes
Beyond risk prediction, AI is also making strides in operational efficiency and clinical precision. By automating time-intensive processes and enhancing the accuracy and consistency of complex clinical tasks, AI allows clinicians to focus more on patient care and less on administrative or repetitive duties. This is particularly valuable in environments where resources are stretched, and demand continues to grow.
AI algorithms can process medical imaging and pathology samples at speeds and volumes far beyond human capability, helping to reduce turnaround times and ease diagnostic bottlenecks. NLP tools further enhance efficiency by transcribing and coding clinical notes in real-time, streamlining documentation while improving data accuracy and consistency across systems.
Crucially, AI also plays an essential role in managing the ever-growing volume of patient data. These systems can efficiently organise, categorise, and process structured records such as treatment histories and activity data. They can also extract key insights from unstructured sources like clinical notes and discharge summaries, making critical patient information more accessible and actionable at the point of care.
These benefits aren’t just theoretical, they’re already transforming care in practice. In high-specialty fields like fertility, for instance, AI-driven tools are actively reshaping clinical workflows and outcomes.
Take the collaboration between BJSS and CARE Fertility, for example. Together, we developed a deep learning model that applies cutting-edge computer vision and time-series analysis. Trained on nearly 500 million embryo images, the model accurately identifies key stages of embryonic development exploring the historic data CARE had using new technologies and techniques
The impact of this model is twofold: embryologists save the equivalent of six months per year in manual assessment time, and embryo selection becomes more consistent, improving the likelihood of successful pregnancies.
Rethinking where and how care happens
But AI isn’t just changing what healthcare looks like, it is also changing where it happens. With the rise of wearable devices like Oura Rings and Fitbits, continuous remote monitoring is fast becoming the norm. These devices, powered by AI-driven predictive models and anomaly detection, can track vital signs and behaviour patterns in real-time, often identifying early warning signs before symptoms are noticeable. Recently NHS England announced the roll out of one such tool which predicts the risk of falls in care homes by 94%.
These tools don’t just enable earlier clinical intervention; they also empower patients to play a more active role in managing their own health. With access to real-time metrics such as heart rate, sleep quality and activity levels, individuals are better equipped to understand their health, recognise potential issues, and make lifestyle adjustments proactively.
The benefits extend beyond individual empowerment. For the healthcare system, this technology supports a more preventative model of care, one that can help reduce hospital admissions, lower costs, and free up resources. It also supports continuity of care for patients with chronic conditions, allowing clinicians to monitor health trends over time and adjust treatment plans.
Building an intelligence-driven AI future
We’re still in the early stages of AI’s journey in healthcare, but the direction is clear. The fusion of artificial intelligence, real-time analytics, and clinical expertise is laying the foundation of a new intelligence driven AI future. This is a future where treatment is predictive, not reactive; care is personalised, and the system itself is capable of learning, adapting, and improving over time.
As adoption accelerates, developers, clinicians and healthcare leaders must consider not just the technical capabilities of AI, but also the clinical, ethical, and social contexts in which it operates. Building trust, transparency, and equity into these systems is essential. In an area where the regulatory frameworks frequently lag behind innovation, we should all be aiming for use cases and implementations which strengthen public trust rather than just being something which is ‘above board’.
