(2024) 22:354
Chen et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03566-x

BMC Medicine

REVIEW

Open AccessIntegrating machine learning and artificial
intelligence in life‑course epidemiology:
pathways to innovative public health solutions
Shanquan Chen1*† , Jiazhou Yu2† , Sarah Chamouni1, Yuqi Wang3 and Yunfei Li4*   

Abstract
The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology
offers remarkable opportunities to advance our understanding of the complex interplay between biological, social,and environmental factors that shape health trajectories across the lifespan. This perspective summarizes the current
applications, discusses future potential and challenges, and provides recommendations for harnessing ML and AI
technologies to develop innovative public health solutions. ML and AI have been increasingly applied in epidemiological studies, demonstrating their ability to handle large, complex datasets, identify intricate patterns and associations, integrate multiple and multimodal data types, improve predictive accuracy, and enhance causal inference
methods. In life-course epidemiology, these techniques can help identify sensitive periods and critical windows
for intervention, model complex interactions between risk factors, predict individual and population-level disease risk
trajectories, and strengthen causal inference in observational studies. By leveraging the five principles of life-course
research proposed by Elder and Shanahan—lifespan development, agency, time and place, timing, and linked lives—
we discuss a framework for applying ML and AI to uncover novel insights and inform targeted interventions. However,the successful integration of these technologies faces challenges related to data quality, model interpretability, bias,
privacy, and equity. To fully realize the potential of ML and AI in life-course epidemiology, fostering interdisciplinary
collaborations, developing standardized guidelines, advocating for their integration in public health decision-making,prioritizing fairness, and investing in training and capacity building are essential. By responsibly harnessing the powerof ML and AI, we can take significant steps towards creating healthier and more equitable futures across the life
course.
Keywords Machine learning, Artificial intelligence, Life course, Epidemiology, Public health†

Shanquan Chen and Jiazhou Yu contributed equally to this work.

*Correspondence:
Shanquan Chen
Shanquan.chen@lshtm.ac.uk
Yunfei Li
yunfei.li@ki.se
1
Faculty of Epidemiology and Population Health, London Schoolof Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
2
Department of Medicine and Therapeutics, The Chinese Universityof Hong Kong, Hong Kong SAR, China
3
Department of Computer Science, University College London,
London WC1E 6BT, UK
4
Department of Neurobiology, Care Sciences and Society, Karolinska
Institutet, Stockholm 171 64, Sweden© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

Chen et al. BMC Medicine(2024) 22:354

Background
Life-course epidemiology is a field of study that examines
the long-term effects of biological, behavioral, and social
exposures during gestation, childhood, adolescence, andadulthood on the development of chronic diseases laterin life [1]. This approach recognizes that health and disease are influenced by the complex interplay of various
factors across an individual’s life span and that the timing
and duration of these exposures can have critical implications for future health outcomes [1].
The importance of life-course epidemiology in understanding chronic diseases lies in its ability to provide
a comprehensive framework for investigating the origins and trajectories of these conditions. As defined by
Elder and Shanahan, the life-course approach is basedon five key principles: lifespan development, agency,
time and place, timing, and linked lives (Fig. 1) [2]. Lifespan development recognizes that human development
and aging are ongoing processes that occur throughout
an individual’s life, rather than being limited to specificstages. Agency acknowledges that individuals have thecapacity to make choices and take actions that shape
their lives, albeit within the constraints of their environmental, social, and historical contexts. The principle of time and place emphasizes that each person’s life
course is embedded within and influenced by the specifichistorical era and location in which they live. Timing is
crucial, as the same events and behaviors can have varying effects depending on when they occur in an individual’s life course. Finally, linked lives underscores the

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interconnectedness of human experiences, as people
influence each other through shared and interdependent
relationships. By applying these principles, researchers
can identify sensitive periods and critical windows during which interventions may be most effective in preventing or mitigating the risk of chronic diseases.
In recent years, machine learning (ML) and artificial
intelligence (AI) have emerged as powerful tools in epidemiological research. ML is a subfield of AI which refers
to the ability of computers to draw conclusions (ie, learn)from data without being directly programmed and builds
from traditional statistical methods [3]. These techniques
offer the ability to handle vast amounts of complex, highdimensional data, identify intricate patterns and associations, and develop predictive models that can inform
personalized interventions and public health strategies. ML and AI can integrate multiple data types, suchas electronic health records (EHRs), genomic data, andenvironmental exposures, to provide a more comprehensive understanding of the factors contributing to healthoutcomes across the life course. Moreover, advanced ML
and AI techniques can analyze multimodal data, including structured and unstructured text, audio, image, andvideo data, enabling the examination of diverse information sources such as MRI scans, X-rays, recordings ofheartbeats or respiratory sounds, and physical activity
and behavioral patterns. Furthermore, these approaches
can enhance causal inference methods, allowing
researchers to better estimate the effects of exposures on
health outcomes in observational settings.

Fig. 1 Five principles of the life-course approach proposed by Elder and Shanahan

Chen et al. BMC Medicine(2024) 22:354

The integration of ML and AI techniques in life-course
epidemiology has the potential to revolutionize ourunderstanding of the complex determinants of diseases
and inform the development of more targeted and effective public health interventions. By leveraging the powerof these innovative tools, researchers can uncover novel
risk factors, identify critical windows for intervention,and predict individual disease trajectories with greater
precision.
This perspective aims to summarize the current applications and discuss future potential and challenges
of integrating ML and AI in life-course epidemiology
research, and to discuss the applications of such technologies to advance public health solutions. This perspective
will also discuss the benefits and limitations of current
applications, highlight opportunities for identifying sensitive periods, modeling complex interactions, predicting
disease risk trajectories, and enhancing causal inference
methods. Further, it will address the challenges and ethical considerations associated with the use of ML and AI
in life-course research, and provide recommendations for
future directions.

Current applications of ML and AI in epidemiology
ML and AI have been increasingly applied in various
areas of epidemiological studies, demonstrating theirpotential to advance our understanding of health and diseases. These techniques offer several key benefits, including the ability to handle large, complex datasets, identify
intricate patterns and associations, and develop accurate
predictive models.
One notable application of ML and AI in epidemiology is in the prediction of cardiovascular disease risk.
Researchers have developed ML models that integrate
clinical, genetic, and lifestyle factors to predict an individual’s risk of developing cardiovascular disease [4–7].
For example, a study utilized 216,152 retinal photographsfrom datasets in South Korea, Singapore, and the United
Kingdom to train and validate deep learning algorithms[6]. The retinal photograph-derived coronary artery calcium scores were found to be comparable to those measured by CT scans [6]. Ward et al. (2020) used EHRs of
262,923 individuals to train and validate ML models,demonstrating performance that was comparable to or
better than traditional equations for atherosclerotic cardiovascular disease risk [7].
Another area where ML and AI have shown promise is
the early detection and prognosis of cancer. These techniques have been applied to predict cancer prognosis andestimate treatment response based on genomic and clinical data [8–14]. For instance, Lu et al. (2021) developed
a deep learning network to predict early on-treatment

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response in metastatic colorectal cancer, which outperformed traditional methods [13].
ML and AI have also been employed to predict theonset and progression of neurodegenerative diseases,
such as Alzheimer’s disease. By integrating neuroimaging, genetic, and clinical data, researchers have developedmodels that can identify individuals at high risk of developing Alzheimer’s disease [15–18]. Bhagwat et al. (2018)used a neural-network algorithm to predict the conversion from mild cognitive impairment to Alzheimer’s disease with as high as 0.90 accuracy [18].
In the realm of infectious diseases, ML and AI havebeen applied to predict disease outbreaks and identify
high-risk areas [19, 20]. Bengtsson et al. (2015) utilized
mobile phone data and machine learning techniques topredict the spatial spread of cholera in Haiti following the2010 earthquake [20]. Similarly, these techniques havebeen used to assess the health impacts of environmental exposures, such as air pollution, by estimating daily
pollutant concentrations and providing high-resolution
exposure assessments for epidemiological studies [21].
Moreover, Odlum and Yoon (2015) leveraged natural
language processing (NLP) techniques on data extracted
from the social media platform Twitter to develop a realtime model for Ebola outbreak surveillance during theearly stage of the 2014 epidemic [22]. Their study showcased the potential of applying advanced computational
methods to unconventional data sources for enhanced
disease monitoring and early detection.
Furthermore, ML and AI have been employed to investigate the social determinants of health and identify populations at high risk of adverse health outcomes [23]. By
analyzing EHRs and integrating data on social and environmental factors, researchers have developed models
that predict an individual’s risk of experiencing healthdisparities or poor health outcomes [24].
There are cases where ML and AI models in epidemiology have been successfully implemented to assist
resource allocation and decision-making in practice.
During the COVID-19 pandemic when the healthcare
systems were strained by increased healthcare demand,clinicians in emergency departments faced significant
challenges in patient disposition decision based on thepatient’s initial symptoms and limited information. In
response, Hinson et al. (2022) developed a ML algorithm to predict near-term clinical deterioration in
emergency patients with real-time EHR data [25]. This
tool was rapidly integrated into clinical practice to support care decisions within the Johns Hopkins Health
System, contributing to more consistent and reliabledisposition decision and improved bed allocation during the pandemic [26]. To inform resource allocationand enhance precision medicine in cardiovascularChen et al. BMC Medicine(2024) 22:354

diseases, Ye et al. (2018) developed and validated a ML
model on EHRs of more than 1.5 million individuals
to predict the risk of incidence essential hypertension
within the next year [27]. Demonstrating excellent performance, this model has been deployed in Maine, theUnited States, to assist healthcare providers in identifying high-risk populations and promoting individualized
treatment [27].
In the end, ML and AI can also contribute to causal
inference by identifying potential causal pathways andcontrolling for confounding factors in observational
studies. For example, Kang et al. (2021) utilized a deeplearning-based causal inference framework to estimate
the causal effect of air pollution on COVID-19 severity
while adjusting for confounding factors such as socioeconomic status and weather conditions [28]. Similarly,
Chu et al. (2020) proposed an adversarial deep treatment
effect prediction (ADTEP) model to predict treatment
effects using heterogeneous EHR data [29].
In summary, the application of ML and AI in epidemiology offers several key benefits, including:
1. Handling large, complex datasets: ML and AI canprocess vast amounts of high-dimensional data, making them valuable tools for extracting meaningful
insights from diverse data sources.
2. Identifying complex patterns: These techniques canuncover intricate, non-linear relationships between
exposures and health outcomes, which may not beeasily identified using traditional statistical methods.
3. Integrating multiple and multimodal data types: ML
and AI can incorporate data from various sources,
such as EHRs, genomic data, and environmental
exposures, as well as structured and unstructured
text, audio, image, and video data, to provide a morecomprehensive understanding of the factors influencing health outcomes.
4. Improving predictive accuracy: These approaches
often achieve higher predictive accuracy than traditional methods, particularly when dealing with complex datasets, enabling the development of more precise risk prediction models.
5. Enhancing causal inference: While primarily used forprediction, ML and AI can also contribute to causal
inference by identifying potential causal pathways
and controlling for confounding factors in observational studies.
By leveraging these benefits, the application of ML
and AI in epidemiology has the potential to advanceour understanding of disease risk factors, improve early
detection and prognosis, and, thereby, inform targetedinterventions to promote population health.

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Opportunities for ML and AI in life‑course
epidemiology
In life-course epidemiology that considers long-term
effects of biological, behavioral, and social exposuresduring gestation, childhood, adolescence, and adulthood, ML and AI offer numerous opportunities by enabling researchers to identify sensitive periods, model
complex interactions, predict disease risk trajectories,and enhance causal inference methods.
Identifying sensitive periods and critical windows
for intervention

ML and AI can help identify sensitive periods andcritical windows for intervention by analyzing longitudinal data on growth and development of exposureand health outcomes. Unsupervised learning techniques, such as clustering and latent class analysis, canuncover distinct subgroups of individuals with similar
developmental trajectories, which may inform the timing of interventions [30, 31]. Additionally, ML and AI
allow for integration of multiple data types, including
EHRs, genomic data, and environmental exposures,and of multimodal data, such as kinds of informationfrom individual’s various types of records, thus providing a comprehensive perspective of the determinantsof health outcomes across the different stages of life
course [32]. Furthermore, potential causal pathways
and mechanisms underlying the associations between
exposures during critical windows of early life and later
health outcomes can be better established by applying
causal discovery algorithms or Mendelian randomization techniques [33].
Modeling complex interactions between biological, social,and environmental factors

ML and AI techniques, such as deep learning and agentbased modeling, can capture the complex, non-linear
associations between multiple risk factors and healthoutcomes across the life course. These approaches canhelp researchers understand how individual-level exposures and experiences at different life stages interact
to shape population-level patterns of health and disease. For example, deep learning algorithms can model
the hierarchical and temporal relationships between
genetic susceptibility, early life adversity, and adult lifestyle factors to predict the risk of developing chronic
diseases. Agent-based models can simulate the spreadof infectious diseases through a population, taking into
account individual susceptibility, contact patterns, andenvironmental conditions [34].

Chen et al. BMC Medicine(2024) 22:354

Predicting individual and population‑level disease risk
trajectories

ML and AI can be used to develop personalized risk
prediction models that estimate an individual’s likelihood of developing a particular disease based on theirunique combination of risk factors and exposures acrossthe lifespan. By effectively combining data from various
sources such as genomic data, EHRs, and lifestyle factors,
these models can provide accurate prediction of diseaserisks at different stages at an individual’s lifespan. At the
population level, ML and AI can identify high-risk subgroups and distinct disease trajectories associated withspecific combinations of early life exposures, socioeconomic factors, and health behaviors [4, 9, 16, 30, 31].
This information can guide the development of targetedinterventions and policies to prevent and manage chronic
diseases.
Enhancing causal inference methods in observational
studies

ML and AI techniques can strengthen causal inference methods in life-course epidemiology by helping
researchers adjust for confounding factors and estimate
causal effects in observational studies. Propensity score
methods, which estimate the probability of an individual
receiving a particular treatment or exposure based on
their observed characteristics, can be enhanced using ML
algorithms to more accurately balance the distribution ofpotential confounders between exposed and unexposedgroups [35]. Instrumental variable methods, which use
factors associated with the exposure but not the outcome
to estimate causal effects, can be improved by using ML
to identify and validate potential instrumental variables[36]. Additionally, advanced ML techniques, such as
causal forests, can directly estimate heterogeneous treatment effects and minimize bias in observational studies[37–39].
By harnessing the power of ML and AI in these key
areas, life-course epidemiology can gain novel insights
into the complex determinants of health and diseaseacross the lifespan, ultimately informing the developmentof more effective, personalized interventions and publichealth strategies.

Framework for harnessing ML and AI technologies
to advance public health solutions
The five principles outlined by Elder and Shanahan offer
a robust conceptual framework for comprehending the
intricate and ever-changing aspects of health and disease
throughout an individual’s life course [2]. These principles also serve as a foundation for harnessing the potential of ML and AI to identify previously unknown risk

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factors, predict disease progression, and guide the development of targeted interventions. By leveraging the fiveprinciples of life-course research, we discuss the applications of ML and AI in life-course epidemiology based on
the framework proposed by Elder and Shanahan [2].
1. Lifespan development: This principle emphasizes
that human development and aging are lifelong processes, highlighting the importance of examining
the cumulative effects of exposures and experiences
across the entire life course. ML and AI techniques,
such as deep learning and longitudinal modeling, cananalyze large, complex datasets spanning multiple life
stages to identify patterns and trajectories of healthand disease over time [30, 31].
2. Agency: This principle recognizes that individuals
have the capacity to make choices and take actions
that shape their health and well-being within theconstraints of their social and environmental contexts. ML and AI techniques, such as decision trees
and reinforcement learning, can model the complex
interactions between individual agency and socialand environmental factors to identify key intervention points for promoting health and preventing disease [40].
3. Time and place: This principle emphasizes that every
individual life course is embedded within and influenced by its specific historical and geographic context. ML and AI techniques, such as spatial modelingand time series analysis, can analyze the effects ofplace and time on health outcomes and identify key
contextual factors that may influence the effectiveness of public health interventions [39, 41]. Recurrent neural networks (RNNs) and long short-term
memory (LSTM) networks have been extensively
employed for temporal data analysis, enabling thecapture of dependencies and patterns over time
[42–44]. Particularly, in recent years, novel architectures like Transformers have emerged and gained
prominence due to their capacity to handle longrange dependencies and facilitate parallel processing. Transformers, exemplified by the Bidirectional
Encoder Representations from Transformers (BERT)
model, have exhibited superior performance across awide range of natural language processing tasks andhave been successfully adapted for time series analysis [45, 46].
4. Timing: This principle recognizes that the same
events and experiences can have different effects
on health depending on when they occur in thelife course. ML and AI techniques, such as survival
analysis (e.g. Survival Forest) and Bayesian networks,
can model the time-varying effects of exposures onChen et al. BMC Medicine(2024) 22:354

health outcomes and identify optimal timing and targeting of interventions [37, 38].
5. Linked lives: This principle emphasizes that individuals are embedded within social networks andrelationships that shape their exposures, behaviors,and outcomes. ML and AI techniques, such as social
network analysis and agent-based modeling, canmodel the complex interactions between individuals
and their social environments to identify key leverage points for interventions that promote health andwell-being across communities [40, 47]. Moreover,large language models (LLMs) can be integrated
into this principle to analyze social media data andpatient-generated content, providing insights into thesocial and environmental factors influencing healthoutcomes across communities and networks [23, 48–
52].
The power of ML and AI techniques also enable
itself to integrate multiple principles of the life-course
approach simultaneously, enabling researchers to develop
more comprehensive models of health trajectories. For
instance, causal inference methods enhanced by ML,
such as causal forests, can simultaneously address multiple principles by estimating heterogeneous treatment
effects across different life stages, social contexts, andindividual characteristics [37–39].

Challenges and ethical considerations
The integration of ML and AI in life-course epidemiology
presents several challenges and ethical considerations
that must be addressed to ensure the responsible andeffective use of these technologies.
Data quality, harmonization, and integration

One major challenge is ensuring the quality, harmonization, and integration of data across multiple cohorts
and sources [53]. Some data sources used for training
ML models, such as EHRs collected for administrative
purposes, might not be gathered with the necessary frequency, granularity, or bandwidth that align with information needs of science and learning, and may therefore
present challenges in generating accurate and reliablealgorithms [54]. Models trained on small sample sizes
or data of suboptimal quality involving missing values,
inaccuracies, and inconsistencies can lead to unreliablepredictions and biased outcomes. Unlike certain healthspecialties such as dermatology or ophthalmology, where
ML and AI have been successfully adopted due to theirreliance on visual evaluation and pattern recognition[11], the application of ML and AI in epidemiology presents unique challenges. Life-course studies often involve
data collected using different methods, at different time

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points, and from diverse populations. Ensuring the comparability and interoperability of these data is crucial fordeveloping robust and generalizable ML and AI models.
This requires close collaboration between researchers,
data managers, and IT professionals to establish common
data standards and protocols. Furthermore, life-course
studies often involve factors with complex interactions
and dynamic nature, as well as social phenomena thatare inherently difficult to quantify and model. Whendealing with dynamic variables, it is essential to retrain
and reevaluate the models to account for new trends andchanges, requiring ongoing monitoring and updates tomaintain accuracy and relevance.
Interpretability and explainability

Another significant challenge is the interpretability andexplainability of ML and AI models. As these algorithms
become increasingly complex, it can be difficult to understand how they arrive at their predictions or decisions.
This “black box” nature of some ML models raises concerns about their transparency and accountability, particularly in the context of public health interventions that
can have far-reaching consequences [55, 56]. Researchers
must strive to develop models that are not only accurate
but also interpretable, allowing for clear communicationof their underlying logic and limitations to policymakers,
healthcare providers, and the public. A case study indicated that when dealing with complex social factors, integrating predictive ML models with explanatory models
can enhance understanding and prediction of outcomes[57].
Bias and generalizability

Bias and generalizability are critical issues in the application of ML and AI to life-course epidemiology. If the
training data used to develop these models are not representative of the broader population or if they containhistorical biases, the resulting algorithms may perpetuate
or even amplify these biases [58]. Results generated fromthese algorithms can lead to unintended consequences,such as the exacerbation of health disparities or the misallocation of resources. For example, EHR data are often
a complex product of biological, socio-economic conditions as well as prior practices of providers and healthsystems [54]. Researchers must be vigilant in identifying
and mitigating potential sources of bias in different data
sources to ensure that their models are equitable andgeneralizable to diverse populations.
Integration with domain knowledge

While ML and AI can identify novel patterns and associations in data, they do not necessarily provide insights
into the underlying biological or clinical mechanisms.

Chen et al. BMC Medicine(2024) 22:354

Integrating ML and AI findings with existing domain
knowledge and expert interpretation is essential for
ensuring the validity and relevance of the results [59, 60].
This requires close collaboration between data scientists,
epidemiologists, and clinical experts, as well as a willingness to iterate between data-driven and hypothesisdriven approaches.
Privacy and ethical concerns

The use of sensitive data in life-course studies poses significant privacy concerns and ethical challenges [61, 62].
These studies often involve the collection and analysis of highly personal information, such as genetic data,
medical records, and social media activity. Ensuring thesecurity and confidentiality of these data is paramount,requiring robust data governance frameworks and strict
adherence to ethical guidelines. Researchers must also
grapple with the potential unintended consequences of
their work, such as the stigmatization of certain groups
or the misuse of predictive models for discriminatory
purposes. Strategies such as adopting synthetic data fortraining models provide new opportunities to improvethe diversity and robustness of ML and AI models by
reducing patient privacy concerns and facilitating data
sharing while maintaining the original distribution ofdata [63].
Computational resources and expertise

Applying ML and AI techniques to large-scale epidemiological data can be computationally intensive and require
specialized expertise in data science and programming.
Access to high-performance computing resources andqualified personnel may be a barrier for some researchgroups, particularly in low- and middle-income settings.
Building capacity and infrastructure for ML and AI in
epidemiological research is an important challenge that
requires ongoing investment and support.
Potential overreliance on ML and AI

While ML and AI offer significant opportunities foradvancing research, clinical practice, and policymaking,it is crucial to recognize that not all ML and AI models
outperform traditional models in healthcare and publichealth areas [64, 65]. A salient illustration of this phenomenon is the “Fragile Families Challenge,” whereindiverse state-of-the-art ML models were employed to
predict individual life outcomes, including psychological and socio-economic status [66]. The resultant performance exhibited only marginal improvement over
simple benchmark models [66]. This limited prediction
value highlights the potential limitations of ML models
when applied to complex social phenomena, contrasting with their success in physical and biological contexts

Page 7 of 11[66, 67]. Prior to the adoption of ML and AI technologies, particularly in applied domains, it appears requisite
to conduct a critical evaluation of these techniques vis-àvis traditional models. This is especially pertinent in scenarios where reliable or superior alternatives are extant[64]. While a consensus has yet to emerge, such evaluation seems essential to preclude unnecessary investmentin these sophisticated models and to mitigate excessive
consumption of finite computational resources.
Despite the demonstrated methodological efficacy of
ML and AI models, it is imperative to acknowledge that
their implementation may not necessarily yield net positive outcomes for patients. A systematic review of 65 randomized controlled trials evaluating AI-based prediction
tools revealed that approximately 40% of the tools, which
had previously exhibited satisfactory performance inobservational model development or validation studies,failed to demonstrate statistically significant clinical benefits for patients when compared to standard clinical care
protocols [68]. The dynamic nature of clinical practice,fluctuations in the prevalence of comorbidities, and various socio-environmental factors can precipitate shifts inthe distribution of patient characteristics. Consequently,
these changes necessitate the periodic retraining and reevaluation of AI systems to maintain their relevance andefficacy [69, 70].
Addressing these challenges and ethical considerations
will require ongoing collaboration and dialogue among
researchers, policymakers, and community stakeholders.
It will be essential to develop guidelines and best practices for the responsible use of ML and AI in life course
epidemiology, ensuring that these technologies areapplied in a manner that is transparent, accountable, andaligned with public values and priorities. By proactively
addressing these issues, we can harness the power of ML
and AI to advance our understanding of health and disease across the life course while safeguarding the rights
and wellbeing of individuals and communities.

Future directions and recommendations
To fully realize the potential of ML and AI in life-course
epidemiology and advance public health solutions,
we discuss the following recommendations for future
research and practice:
Foster interdisciplinary collaborations

Collaboration between epidemiologists, data scientists,and public health professionals is crucial for the successful integration of ML and AI in life-course research.
These collaborations will enable the exchange of knowledge, skills, and expertise necessary to develop andapply cutting-edge ML and AI techniques to complex
life-course data. Epidemiologists contribute a deep

Chen et al. BMC Medicine(2024) 22:354

understanding of the biological, social, and environmental factors influencing health trajectories, while data
scientists bring advanced computational and analytical skills. Public health professionals provide invaluable
insights into the practical implications and translational
potential of research findings. By working together, these
multidisciplinary teams can drive innovation, uncovernovel insights, and ultimately improve population healthoutcomes.
Develop standardized guidelines and best practices

Establishing standardized guidelines and best practices
for using ML and AI in life-course research is essential
to ensure the reliability, reproducibility, and ethical application of these technologies. Clear protocols should be
developed for data collection, preprocessing, and analysis, as well as guidelines for model development, validation, and reporting. These standards should be createdthrough a collaborative process involving researchers,
professional societies, and other stakeholders, drawing
on existing best practices in epidemiology, data science,and bioethics. By promoting transparency, consistency,
and rigor in the use of ML and AI, the credibility andimpact of life course research can be enhanced. In themeantime, it is important to be aware of the limits of ML
and AI applications. Guidelines should be formulated to
instruct the necessity and appropriateness of adopting
ML and AI in different areas.
Advocate for the integration of ML and AI in public healthdecision‑making

Translating research findings into actionable policies andinterventions is the key to realizing the full potential of
ML and AI in life-course epidemiology. This requires
close collaboration between researchers, policymakers, and community stakeholders to ensure that ML andAI models are developed and applied in a manner that
addresses real-world health challenges and promotes
health equity [71]. In order to generate results that cantranslate to advances that promote population healthand health systems, the learning initiatives must alignwith questions that impact the important aspects ofclinical practice or health-related decision-making [54].
Researchers should strive to communicate their findings
in a clear, accessible language and engage with decisionmakers and the public to build trust and understanding of these technologies. Policymakers, in turn, should
invest in the infrastructure and resources necessary tosupport the use of ML and AI in public health, including
data systems, computational tools, and workforce development. By working together, the power of ML and AI
can be harnessed to inform evidence-based policies andPage 8 of 11

interventions that improve health outcomes across thelife course.
Prioritize equity and fairness in ML and AI applications

As ML and AI technologies become increasingly integrated into life-course research and public health practice, it is crucial to prioritize equity and fairness in theirdevelopment and application [58, 72]. Researchers should
assess the data comprehensively given the specific purposes, while actively work to identify and mitigate potential sources of bias in their data and models, ensuring thatthe benefits of these technologies are distributed equitably across diverse populations. This may involve developing new methods for bias detection and correction, as
well as engaging with communities and stakeholders tounderstand their needs and concerns. Policymakers andfunding agencies should also prioritize research and initiatives that focus on addressing health disparities andpromoting health equity through the use of ML and AI.
Invest in training and capacity building

To fully capitalize on the potential of ML and AI in lifecourse epidemiology, it is essential to invest in training
and capacity building for researchers, public health professionals, and policymakers. This may involve developing new educational programs and curricula that
integrate data science and computational skills withdomain expertise in epidemiology and public health. It
may also require establishing new funding mechanisms
and support structures to enable researchers to accessthe computational resources and expertise necessary
to apply ML and AI techniques to their data. Building askilled and diverse workforce that can effectively leveragethese technologies will be critical for driving innovationand progress in life course research and public healthpractice.
By pursuing these recommendations and prioritizing
interdisciplinary collaboration, standardization, integration, equity, and capacity building, the field of life course
epidemiology can harness the full potential of ML and
AI to advance our understanding of health and diseaseacross the lifespan and develop more effective, equitable,
and evidence-based public health solutions.

Conclusions
The integration of ML and AI in life-course epidemiology
presents a remarkable opportunity to advance our understanding of the complex interplay between biological,
social, and environmental factors that shape health trajectories across the lifespan. Leveraging these powerful
technologies to analyze diverse datasets can yield novel
insights, improve disease risk prediction, and inform thedevelopment of targeted interventions.

Chen et al. BMC Medicine(2024) 22:354

However, the realization of this potential is contingent upon addressing the significant challenges associated with the use of ML and AI, including issues related
to data quality, model interpretability, bias, privacy,
and equity. To fully harness the power of these technologies, it is imperative to foster interdisciplinary collaborations, establish standardized guidelines and bestpractices, advocate for the integration of ML and AI
into public health decision-making processes, prioritize
fairness in their application, and invest in training andcapacity building.
It is important to acknowledge that this perspective
paper, while striving for a balanced and comprehensive discussion, is not a systematic review. As such, theinformation presented may be subject to selection bias.
Our narrative approach, while broad, may not capture
all relevant studies or viewpoints. The examples citedwere selected based on relevance and impact, but may
not represent an exhaustive body of evidence. Readers
should consider this limitation when interpreting theconclusions and recommendations presented.
As we look to the future, we should be guided by avision of harnessing data, technology, and innovation
to promote health, prevent disease, and reduce inequities across the life course. By working collaboratively toresponsibly integrate ML and AI into life-course epidemiology, we can take a significant step towards creatinga healthier and more equitable future for all.
Abbreviations
AI	Artificial intelligence
EHR	Electronic health record
LLM	Large language models
ML	Machine learning
NLP	Natural language processing
Acknowledgements
None
Author’ contributions
SC1: conceptualization, writing-original draft. JY: writing-reviewing and editing. SC2: writing-reviewing and editing. YW: writing-reviewing and editing.
YL: writing-reviewing and editing. All authors read and approved the final
manuscript.
Funding
Open access funding provided by Karolinska Institute. SC’s research was
supported by the PENDA, funded by the UK Foreign, Commonwealth andDevelopment Office.
Availability of data and materials
Not applicable.
Data availability
No datasets were generated or analysed during the current study.

Declarations
Ethics approval and consent to participate
Not applicable.

Page 9 of 11

Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 22 May 2024 Accepted: 19 August 2024

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