Anshika Jain
For decades, improvements in healthcare have been measured largely through life expectancy, mortality rates, and the ability to treat life-threatening diseases. These measures remain essential, but they do not fully explain whether people are spending their additional years in good health. A person may live longer while experiencing chronic pain, reduced mobility, cognitive difficulties, or a growing dependence on others for everyday activities. Another person may have the same life expectancy but remain physically active, mentally engaged, socially connected, and independent for a greater proportion of life. This distinction lies at the centre of the healthspan measurement problem.
Healthspan generally refers to the period of life spent in good health, while lifespan refers to the total duration of life. However, healthspan is more difficult to define and measure than lifespan because health is not a single condition that can be captured by one number. It includes physical functioning, mental and cognitive abilities, the presence and severity of diseases, the capacity to perform daily activities, and the ability to participate in meaningful social life. These dimensions may change at different rates, making it difficult to determine precisely when a person stops being healthy or begins to experience a sustained decline.
Artificial intelligence (AI) offers new ways to analyse these complex patterns. By integrating medical records, wearable-device measurements, functional assessments, laboratory results, and information about daily behaviour, AI systems may help researchers estimate how well people are functioning as they age. Yet this possibility raises an important question: can AI distinguish between simply extending life and extending the years in which people retain their capabilities, independence, and quality of life?
The answer depends not only on the sophistication of algorithms but also on the quality of the underlying definition of healthspan. AI can detect patterns in data, estimate future risks, and identify combinations of factors associated with functional decline. It cannot automatically determine what a good life means, decide which limitations matter most to an individual, or transform an incomplete measurement system into a reliable definition of healthy ageing. Solving the healthspan measurement problem therefore requires a combination of biomedical research, meaningful outcome measures, responsible AI design, and long-term validation.
Understanding the Difference Between Lifespan and Healthspan
Lifespan Measures Duration, While Healthspan Measures Capability
Lifespan is comparatively straightforward to calculate because it can be measured as the interval between birth and death. Life expectancy estimates the average number of years a person can expect to live under specified mortality conditions. Although estimating life expectancy for a population requires statistical modelling, the underlying outcome is clearly defined.
Healthspan is more complicated because there is no universally accepted boundary between healthy and unhealthy life. Some researchers focus on the period without major chronic disease, while others emphasise functional ability, disability-free survival, cognitive health, or a combination of these outcomes. Two individuals with the same diagnosis may have very different experiences. One person with diabetes, for example, may maintain mobility and independence for decades, while another may develop complications that substantially limit everyday functioning.
This distinction shows why the absence of disease cannot serve as the only definition of healthspan. Many chronic conditions can be managed successfully, and a diagnosis does not necessarily mean that a person has lost the ability to live independently or participate in ordinary activities. Conversely, someone may have no formally diagnosed major disease but still experience frailty, persistent fatigue, impaired balance, or difficulties with memory and concentration.
A useful healthspan framework must therefore distinguish between disease status and the practical consequences of disease. It should measure not only what conditions a person has but also what that person can do, how well they can perform those activities, and whether their capabilities are changing over time.
The Importance of Functional Health
Functional health describes a person’s ability to carry out physical, cognitive, and everyday tasks. Physical functioning may include walking, climbing stairs, maintaining balance, lifting objects, and recovering after exertion. Cognitive functioning may involve memory, attention, planning, decision-making, and the ability to manage complex tasks. Everyday functioning can include preparing meals, managing medication, maintaining personal hygiene, travelling independently, and handling financial responsibilities.
These capabilities provide a practical perspective on ageing because they connect medical conditions with real-life outcomes. A laboratory value may indicate a physiological risk, but it does not necessarily show whether an individual can walk to a local shop, return to work after an illness, or continue living independently.
Functional health is also multidimensional. A person may retain strong cognitive abilities while experiencing reduced physical mobility. Another may remain physically active but develop difficulties with memory or decision-making. An overall healthspan score that combines these dimensions without preserving their individual meaning could conceal important changes.
For this reason, researchers need measurement systems that can represent health as a changing profile rather than a single fixed label. AI may help analyse these profiles, but the selection of relevant dimensions must be grounded in clinical evidence and the lived experiences of the people being assessed.
Why Measuring Healthspan Remains Difficult
Different Definitions Produce Different Results
One of the biggest obstacles to measuring healthspan is that different definitions can produce different estimates of how long people remain healthy. Disease-free life expectancy counts years without specified diseases. Disability-free life expectancy focuses on whether people experience limitations in everyday activities. Self-rated healthy life expectancy uses people’s assessments of their own health, while quality-adjusted approaches combine survival with measures of health-related quality of life. These indicators answer related but different questions, so they should not be treated as interchangeable.
Consider a hypothetical population in which medical treatment enables people to live longer with chronic disease. If healthspan is defined only as years without a diagnosis, the population may appear to have little improvement. If the measure focuses on functional independence, however, the same population might demonstrate meaningful progress because people are living longer without severe disability. A different measure based on self-reported health could produce another result, influenced by expectations, cultural attitudes, and how individuals interpret their symptoms.
This is not merely a technical disagreement. Governments use population health indicators to evaluate policies, healthcare systems use them to assess outcomes, and researchers use them to determine whether interventions improve ageing. If the definition changes between studies, it becomes difficult to compare results or establish whether a treatment has genuinely extended healthy life.
The World Health Organization has highlighted the need for agreed metrics, consistent analytical approaches, and monitoring across different population groups. Its work on measuring healthy ageing also emphasises functional ability rather than relying exclusively on disease status.
Health Changes Gradually, Not at a Single Moment
A second difficulty is that healthspan rarely ends at a clearly identifiable point. Some people experience a sudden decline after a stroke, serious injury, or major illness. Others experience gradual changes in strength, balance, energy, memory, or endurance over many years. A person might remain independent in most activities while needing increasing assistance with a few demanding tasks.
A binary classification of healthy or unhealthy cannot adequately represent this gradual process. It may label a person as healthy until a threshold is crossed, even when important functional decline has already begun. Alternatively, it may classify someone as unhealthy after a diagnosis despite stable functioning and a high degree of independence.
A better approach would measure changes in capability over time. Instead of asking only whether a person meets a definition of good health today, researchers could examine how their physical and cognitive abilities evolve, how quickly they recover after illness, and whether their capacity to perform valued activities is being maintained. This longitudinal perspective is particularly important when evaluating interventions intended to preserve health over several years.
How AI Could Improve Healthspan Measurement
Combining Data From Multiple Sources
AI could make healthspan assessment more informative by integrating data that are usually collected separately. Electronic health records provide information about diagnoses, medication use, hospital admissions, laboratory measurements, and treatment history. Functional assessments describe mobility, strength, cognition, and independence. Wearable devices can provide repeated measurements of activity, sleep patterns, heart rate, and other physiological signals. Questionnaires can capture pain, fatigue, mood, perceived health, and limitations that may not appear in clinical records.
When analysed together, these sources may reveal patterns that individual measurements miss. For example, a person’s medical record might show stable chronic conditions, while wearable data indicate a sustained decline in daily movement. If that decline coincides with increasing fatigue and reduced performance on mobility assessments, the combined evidence could signal an emerging functional problem that warrants further evaluation.
Machine-learning systems can analyse large numbers of variables, identify non-linear relationships, and estimate which combinations of features are associated with later outcomes. Longitudinal models can also distinguish a temporary change from a persistent trend, provided that the data are sufficiently frequent and reliable. However, a change in movement or sleep does not automatically indicate disease or accelerated ageing. Work schedules, weather, travel, medication, device placement, and personal routines can all affect the signals. AI therefore needs contextual information and appropriate clinical interpretation rather than treating every statistical change as a health problem.
Measuring Intrinsic Capacity and Functional Ability
A useful foundation for AI-based healthspan measurement is the World Health Organization’s concept of intrinsic capacity. This refers to the combination of an individual’s physical and mental capacities, including abilities related to movement, cognition, sensory function, and vitality. Functional ability depends not only on intrinsic capacity but also on the environment in which a person lives and the interaction between the two.
This distinction is important because a person’s ability to function does not depend solely on their biological condition. Someone with limited mobility may remain independent in a well-designed home and community with accessible transport, assistive technology, and appropriate support. Another person with similar physical capacity may face substantial restrictions because their environment includes stairs, unsafe streets, inaccessible public services, or limited social support.
An AI model that treats disability as a direct reflection of biological ageing could therefore misinterpret environmental barriers as personal decline. A more complete system would assess both the person’s capabilities and the conditions that enable or restrict their use. It could help identify whether a difficulty is more likely to require clinical assessment, rehabilitation, environmental adaptation, or additional social support.
The goal should not be to create a universal score that reduces every aspect of human functioning to one number. Instead, AI could help construct a multidimensional profile that shows where capabilities are stable, where they are changing, and which factors may be contributing to those changes.
Detecting Trajectories Rather Than Isolated Measurements
A single health assessment provides a snapshot. Repeated measurements can show a trajectory. This distinction is essential because a low measurement on one day may reflect temporary illness, fatigue, or measurement error rather than a lasting reduction in capacity.
AI models could estimate an individual’s typical range, identify sustained deviations, and compare changes across several domains. For example, an isolated decline in activity after a respiratory infection may be expected during recovery. A persistent decline accompanied by reduced walking speed, worsening balance, and increasing difficulty with daily tasks may suggest a more consequential change.
Such systems could also estimate uncertainty. Rather than declaring that a person has entered a period of poor health, a model could indicate that the observed pattern is associated with an elevated likelihood of future functional decline and explain which measurements contributed most strongly to the estimate. Clinicians could then decide whether additional testing or intervention is appropriate.
Researchers are already applying machine learning to frailty prediction, a related but narrower problem. A 2026 systematic review and meta-analysis reported promising performance across some models but also identified limitations in overall quality and applicability. The findings illustrate both the potential of predictive methods and the need for stronger validation before such systems are used routinely.
The Difference Between Predicting Decline and Measuring Healthspan
A Risk Score Is Not a Healthspan Measure
AI systems can be accurate at predicting a particular event without fully measuring the broader concept that researchers care about. A model trained to predict hospital admission may identify people at increased risk of hospitalisation. A model trained to predict frailty may estimate the probability of meeting a frailty definition. Neither result automatically reveals how many years a person will live with good functional ability.
This distinction matters because prediction and measurement serve different purposes. Prediction estimates the likelihood of a defined future event. Measurement attempts to represent a health state or a change in that state. Estimating healthspan requires an explicit definition of which outcomes count, how changes are assessed, and how death is incorporated into the analysis.
Suppose an AI system predicts that an individual has a high probability of developing mobility limitations within five years. That result could be valuable for prevention, but it does not establish that the person’s current healthspan has ended. Nor does it show how much an intervention might extend their years of independent living. To answer those questions, researchers would need appropriate longitudinal outcomes and evidence that the model performs reliably in the population where it is used.
Distinguishing Association From Causation
AI may identify that certain patterns are associated with better health outcomes, but association alone cannot establish that changing those patterns will improve healthspan. People who maintain higher physical activity, for instance, may also differ in income, access to healthcare, housing, nutrition, social connection, and pre-existing health. A model might assign importance to one factor while overlooking the broader conditions that influence both the factor and the outcome.
Causal analysis requires additional methods and carefully designed studies. Researchers may use longitudinal designs, natural experiments, clinical trials, or causal inference techniques to examine whether an intervention changes the outcome of interest. Even then, results need to be interpreted in light of the population studied, the intervention delivered, and the length of follow-up.
For healthspan research, this means an AI-generated recommendation should not be considered effective simply because the model predicts that it will be. If a digital programme claims to improve healthy ageing, researchers should assess whether participants actually retain or improve meaningful capabilities compared with an appropriate comparison group. They should also examine whether any benefits persist and whether the programme produces unintended harms.
What Should a Reliable AI Healthspan Framework Measure?
Physical Capacity and Everyday Independence
Physical healthspan assessment should extend beyond the presence of cardiovascular disease, diabetes, arthritis, or other diagnoses. It should also examine the functional consequences of these conditions. Relevant measures may include walking speed, balance, lower-body strength, endurance, the ability to rise from a chair, and the capacity to perform everyday tasks.
These measures are useful because they can reveal changes that matter to daily life. However, they must be interpreted in context. Walking speed may be influenced by pain, an injury, a temporary illness, or the environment in which a test is performed. A meaningful assessment should therefore combine standardised tests with information about an individual’s baseline and circumstances.
AI could help researchers identify patterns across multiple physical measures and determine whether changes are persistent. It could also assist in evaluating whether rehabilitation, exercise programmes, or changes in the living environment are associated with improved functional outcomes. The objective should be to measure capabilities that matter to people, rather than maximising a numerical score without evidence of practical benefit.
Cognitive, Emotional, and Social Functioning
Healthspan also includes the ability to think, learn, remember, make decisions, regulate emotions, and maintain relationships. Cognitive difficulties may affect medication management, financial decisions, communication, and independent living. Emotional distress may reduce participation in everyday activities, while social isolation can limit access to support and meaningful engagement.
These dimensions are harder to measure continuously than steps or heart rate. A decline in smartphone use, for example, could reflect cognitive difficulties, but it could equally reflect a change in personal preference, internet access, device familiarity, or social routine. Inferring mental health or cognitive ability from digital behaviour without appropriate validation risks creating misleading conclusions.
A robust system should combine validated assessments with appropriate clinical information and, where relevant, self-reported experiences. It should also recognise that social participation is shaped by circumstances. A person may have a small social network by choice and still experience strong wellbeing, while another may want more contact but face barriers to participation.
The WHO framework of functional ability is useful here because it emphasises people’s ability to meet needs, make decisions, remain mobile, maintain relationships, and contribute to society. It does not define healthy ageing as the absence of all disease or impairment.
Recovery and Resilience After Illness
Another important dimension is the ability to recover after a health challenge. Two people of the same age with similar diagnoses may have different recovery patterns following surgery, infection, or hospitalisation. One may return to their previous level of functioning within weeks, while another may experience prolonged weakness or require lasting assistance.
AI could help model these recovery trajectories by analysing pre-event function, the severity of illness, treatment, rehabilitation, and repeated measurements after discharge. Such analysis might help identify individuals who need closer follow-up or additional rehabilitation. It could also provide a more nuanced view of healthspan by examining whether people return to their previous level of functioning rather than merely recording whether they survived the event.
However, recovery is not a single biological property. It depends on the nature of the illness, the availability of support, the person’s goals, and the practical demands of their environment. Models should therefore compare people in clinically meaningful contexts and avoid interpreting every slow recovery as evidence of accelerated ageing.
The Risk of Turning Healthspan Into an Oversimplified Score
Why One Number Can Hide Important Differences
A single healthspan score is attractive because it appears easy to communicate and compare. A dashboard could show whether a person’s estimated healthspan is improving, remaining stable, or declining. Yet combining multiple dimensions into one number requires decisions about their relative importance.
Should a small decline in memory carry the same weight as a major reduction in mobility? How should researchers compare chronic pain with loss of hearing, or reduced endurance with difficulty maintaining social relationships? Should a score prioritise the ability to work, independence in personal care, or the activities that an individual personally values?
There is no purely technical answer to these questions. The choice of weights reflects clinical priorities, research goals, and values. If an algorithm gives more importance to measures that are easy to collect, it may systematically undervalue experiences that are difficult to quantify, such as emotional wellbeing or the ability to maintain meaningful relationships.
A more transparent approach would preserve the underlying dimensions and show how each contributes to the overall assessment. A summary score may still be useful for specific research purposes, but it should not replace the detailed profile or be presented as a definitive measurement of a person’s worth, vitality, or future.
Bias and Unequal Representation
AI healthspan models may perform differently across age groups, sexes, socioeconomic circumstances, cultural settings, and levels of disability. If training data largely represent people who have regular access to healthcare and wearable devices, the resulting model may be less reliable for people with limited access to medical services or technology.
Missing data can also be informative. An absence of recent clinical measurements might indicate that a person is healthy and has no need for medical care. It could instead reflect financial barriers, geographical isolation, limited mobility, or difficulties accessing services. If an algorithm interprets missing measurements as evidence of low risk, it may underestimate the needs of disadvantaged populations.
The WHO has emphasised that differences in health and functional ability in older age reflect the cumulative effects of social, economic, biological, and environmental conditions across life.
AI models should therefore be evaluated across diverse populations, and their performance should be monitored after deployment. Researchers need to examine not only average accuracy but also whether errors are concentrated among particular groups.
Privacy is another essential consideration. Healthspan modelling may require long-term data from medical records, connected devices, surveys, and other personal sources. These records should be collected and used with appropriate consent, security safeguards, access controls, and clear limits on secondary use. A system designed to support healthy ageing should not become a tool for discriminatory insurance decisions, employment screening, or unjustified surveillance.
How Researchers Could Validate AI-Based Healthspan Measurement
Establishing Clear Outcomes Before Training Models
The first step is to define the outcome before selecting the algorithm. Researchers should specify whether they intend to measure disability-free years, years with preserved functional ability, changes in intrinsic capacity, quality-adjusted survival, or another clearly described concept. They should explain how each outcome is assessed and how death, temporary disability, recovery, and long-term impairment are handled.
This definition should be established before model performance is evaluated. Otherwise, researchers risk choosing whichever outcome produces the most impressive results rather than the outcome that best answers the scientific question. Standardised measures also make it easier to compare findings across studies and healthcare systems.
The WHO’s 2025 framework for measuring progress during the United Nations Decade of Healthy Ageing reinforces the importance of clear indicators and stronger monitoring systems. Its guidance provides a broader foundation for tracking healthy ageing at different levels, from individual experiences to population-level progress.
Testing Performance Beyond the Original Dataset
A model may perform well on the data used to develop it but perform less reliably in another hospital, region, or population. Researchers should therefore test it on independent datasets and examine whether predictions remain accurate over time. They should also evaluate calibration, which indicates whether predicted risks correspond to observed outcomes, rather than relying only on a measure of discrimination between higher-risk and lower-risk individuals.
A model intended to estimate changes in functional ability should be assessed against repeated functional measurements. If its purpose is to estimate healthy life expectancy, researchers must evaluate how well its estimates align with observed health and mortality outcomes over an appropriate period. These are different tasks and require different validation strategies.
Research reviews of machine-learning applications in geriatric care have identified limitations including inconsistent reporting, small samples, insufficient external validation, and concerns about clinical applicability. These issues show why promising predictive performance alone is not enough to establish that an AI system is ready for routine use.
Measuring Whether AI Actually Improves Outcomes
The final test is whether using AI improves decisions and outcomes. A system might identify people at elevated risk, but its practical value depends on whether that information leads to appropriate support and better health. Researchers should assess whether the tool helps clinicians identify functional decline earlier, guides useful interventions, reduces avoidable disability, or improves the number of years people can live with capabilities they value.
Evaluation should also consider harms. False alarms may create anxiety and unnecessary testing, while missed warnings could delay care. Automated scores may influence clinical decisions in ways that are difficult for patients to challenge. Human oversight, clear communication, and the ability to question or correct an assessment should therefore remain part of the implementation process.
The most convincing evidence would come from prospective studies that compare outcomes when AI-supported assessment is used with outcomes under appropriate usual-care arrangements. These studies should measure both intended benefits and unintended consequences, including whether improvements are distributed fairly across different groups.
The Future: From Longevity Statistics to Functional Longevity
The next generation of healthspan research may move beyond asking how long people live and towards understanding how their capabilities change throughout life. AI could help researchers integrate clinical assessments, physiological measurements, personal experiences, and environmental factors into more comprehensive models of healthy ageing. These models may eventually support earlier identification of functional decline, more personalised prevention strategies, and better evaluation of interventions intended to preserve independence.
Achieving this goal will require collaboration among clinicians, data scientists, ageing researchers, public health specialists, and the people whose lives are being measured. Standardised definitions will make results more comparable, while longitudinal studies and independent validation will establish whether AI estimates are reliable. Equally important, researchers must preserve the distinction between predicting a medical outcome and measuring the broader experience of living well.
The central challenge is not simply to create a more powerful algorithm. It is to determine which dimensions of health matter, how they should be measured, and how changes in those dimensions relate to the lives people want to lead. An AI system can help identify patterns and quantify uncertainty, but it cannot independently decide what meaningful functioning should mean for every individual.
Ultimately, the healthspan measurement problem asks a question that is both scientific and human: are additional years accompanied by additional opportunities to think, move, recover, participate, and live independently? AI may help researchers answer this question more precisely, provided that its predictions are grounded in valid measures and tested against real outcomes. The most meaningful progress will occur when longer life and better functioning are evaluated together, rather than treating survival alone as the final measure of success.