AI Could Help Detect Disease Before Symptoms Appear

Artificial intelligence could support a shift from treating established illness to detecting early changes before symptoms appear and tissue damage becomes difficult to reverse. A framework published in BMJ Health & Care Informatics brings together predictive modelling, multimodal health data and preventive treatment in one clinical approach. It proposes regular monitoring over time, followed by more detailed testing and treatment for people whose risk increases. The framework also makes clear that accurate prediction is not enough. Models must be tested in real clinical settings, linked to effective interventions and introduced through systems that can manage data quality, privacy, bias and false positives. 

Detecting Risk Before Disease Becomes Established 

Conventional care often starts when symptoms, abnormal laboratory findings or other clinical signs are already present. By then, molecular and cellular changes may have progressed to tissue damage that is hard to reverse. Many common biomarkers are essential in acute care, but they often rise only after injury has occurred. Screening based on a narrow set of risk factors can also miss people who later develop disease. 

Must Read: AI Pushes Medical Training Towards Adaptive Learning 

The proposed approach treats health and disease as a gradual process rather than a simple healthy-or-ill divide. It aims to detect changes from a person’s own baseline before clear disease appears. This could allow earlier action while biological changes are still potentially modifiable. 

Recent models show how this might work. Electronic health record data have been used to predict type 1 diabetes shortly before diagnosis in children. Other models have forecast Alzheimer’s disease and pancreatic cancer several years before diagnosis. A generative transformer trained on UK Biobank data and validated in Danish population registries predicted future rates for more than 1,000 diseases, although performance differed between conditions and fell over longer prediction periods. 

These examples show that clinical records, laboratory tests, medical history and lifestyle information can contribute to earlier risk assessment. However, most findings come from retrospective work. Strong model performance alone does not prove that a tool will improve outcomes in routine care. 

Creating a Practical Monitoring Pathway 

The framework proposes a funnel-shaped pathway. Broad monitoring would identify people whose risk appears to be increasing, while more detailed testing would be reserved for those most likely to benefit. Possible inputs include routine health checks, existing clinical encounters, targeted screening and continuous data from wearable or ambient devices. 

Linked health records could help track changes over time and identify unusual patterns before irreversible disease develops. This could support more personalised decisions than relying only on population reference ranges. Routine data may already offer useful signals. Periodic complete blood counts can define stable, patient-specific ranges and may help estimate future risk for several chronic conditions in apparently healthy adults. 

Wearables can add continuous information on movement, physiology and environmental exposure. Advances in protein sensing may also support real-time biomarker monitoring. Sleep study data have been used to predict later risks including dementia, stroke, cardiovascular disease and chronic kidney disease. 

Combining several data types may improve prediction, but adding more information does not always produce a better model. In Parkinson’s disease, a model combining accelerometry, genetics, lifestyle, blood biochemistry and early symptoms did not outperform accelerometry alone. Some data sources may therefore provide overlapping information. Each disease context requires careful selection of data that adds clear value. Cost, feasibility and the burden of collecting extra information also need to be considered during development and validation. 

Linking Prediction to Safe Preventive Care 

Clinical use requires more than accurate algorithms. Prospective studies must show that predictions relate to meaningful outcomes across different population groups and improve care in real settings. Current evidence is largely based on US and European cohorts, with limited diversity and generalisability. Health systems also need reliable standards for data quality, access, storage and interoperability. 

Models should be interpretable so clinicians can understand the main reasons behind a risk estimate. Privacy, bias, accessibility and equity also need to be addressed before wider use. 

Disease prevalence is another important issue. In rare diseases and presymptomatic states, even a well-performing model can produce too many false positives. Longitudinal screening may improve precision by updating risk over time and recommending further testing only when it is likely to be useful. 

Prediction must also lead to an intervention that can change the course of disease. In type 1 diabetes, teplizumab delayed progression to clinical disease in high-risk people with presymptomatic disease. However, current screening based mainly on family history, autoantibodies and abnormal glucose regulation can miss many future cases or identify disease after major beta-cell loss. The framework therefore supports multimodal risk assessment to identify the best time for preventive treatment. 

Not every disease can be prevented. The approach should focus on conditions with early biological changes that are both measurable and reversible. It must also limit overtreatment, account for lead-time bias and reduce the psychological burden of being told that disease may develop. 

AI could help medicine move from reacting to established disease towards tracking health over time and acting earlier. The proposed approach combines regular monitoring, personalised risk assessment, staged testing and preventive treatment. Its value, however, still needs to be confirmed in real clinical practice. Progress depends on diverse data, interoperable systems, understandable models and treatments that can safely modify early disease processes. The key question is not only whether disease can be predicted, but whether the prediction is reliable and useful enough to justify action without causing unnecessary harm. 

Source: BMJ Health & Care Informatics