INTRODUCTION

Healthcare has unique features in protecting and improving human life, from diagnosis to treatment [1]. Many diseases are major public health concerns because they are leading causes of death and morbidity. Examples include heart disease, stroke, and cancer, all of which have preventable origins. The underlying causal factors of these conditions, such as genetic factors, behaviors, environment, and other chronic conditions, allow medical practitioners to take these factors into consideration when creating treatment plans for their patients [2,3].

Traditional medicine is generally based on a standard of care for the entire patient population. This approach uses a “one size fits all” approach where a particular drug is used to treat all patients suffering from a particular disease [4]. However, this approach is fraught with several problems such as only a certain percentage of these patients respond to a particular drug and another significant subset develops side effects. These individual differences may be due to factors such as genetic differences, age, gender, comorbidities, and environment. This also leads to increased costs and low patient and doctor satisfaction [5]. Personalized medicine (PM), sometimes called precision medicine or even stratified medicine, is the process of tailoring medical decisions and interventions to an individual. Strictly speaking, PM is used for treatments designed for individual patients, whereas precision medicine refers to treatments tailored to subgroups of patients with similar genetic, clinical, environmental, and lifestyle characteristics [6,7]. Through personalized features such as the patient’s molecular profile, PM can both lead to a more successful outcome and minimize harmful side effects. By eliminating the “trial-and-error” approach to disease treatment, it also contributes to the selection of a therapy or treatment protocol, which can help control costs [8,9]. Overall, the primary goal of personalized and preventive health care is to determine a strategy for intervention before the disease occurs or worsens through detailed analysis of factors such as genetic, behavioral, environmental, and individual risk factors that may be associated with the individual [10]. Figure 1 provides an overview of the comparison between traditional therapy and PM.

PM can also be applied to population-based approaches, adding significant preventive aspect to the overall concept [5]. PM is emerging as a modern form of healthcare, where big data is utilized alongside various tools to achieve the goals of precision medicine. The process of precision medicine begins by gathering genomic, clinical, environmental, and lifestyle data from patients. Advanced tools then analyze this data to identify patterns, correlations, and potential disease triggers. The insights gained help stratify patients and guide the development of personalized treatment plans for them [6,9]. It is important to note that analyzing big data used in PM can be challenging with conventional software or hardware. Big data in health and medicine encompasses large amounts of data from individual clinical information, subpopulation studies, complex biomolecular analyses, radiological exams, and social and environmental factors (Table 1) [11].

Rapid advances in molecular biology, genetic testing, and analysis technology have made it possible to collect large volumes of data and combine them with clinical, pharmacological, and socioeconomic information [5]. In recent years, the use of artificial intelligence (AI) in medicine has already found widespread use, and its rapid integration into this field has opened new horizons with the potential to significantly increase diagnostic sensitivity, treatment efficacy, and overall patient care [12,13]. The use of algorithms developed on the basis of AI and its sub-models such as machine learning (ML) and deep learning (DL) has transformed the decision-making process in the field of medicine. This transformation has been made possible by enabling the creation of personalized models based on the characteristics of the patient [14,15]. These algorithms can also significantly contribute to mining patient medical records and identifying those most likely to develop certain conditions [12].

This article aimes to explore the transformative potential and future directions of AI in PM. It also highlights its role in preventive medicine based on its predictive potential for individuals or groups at risk of certain diseases.

ARTIFICIAL INTELLIGENCE APPLICATIONS IN PERSONALIZED AND PREVENTIVE MEDICINE

AI has the potential to significantly improve healthcare. This potential is thought to be achieved through three primary mechanisms [2]:

First, AI has the potential to make medical interventions more accurate, precise, and personalized to the individual by integrating and analyzing large amounts of personal, social, clinical, genomic, and epidemiological data. This potential could enable it to match or surpass humans in performing certain health tasks, as well as develop a more detailed understanding of diseases and more effective drugs and treatments. Second, AI-powered clinical decision-making enables healthcare interventions to be faster, more productive, and more efficient. Finally, AI algorithms have a significant role to play in disease prevention and early intervention, as they have the potential to examine factors such as population demographics, disease prevalence, and geographic distribution to identify patients or groups of patients at higher risk for certain conditions.

Although the healthcare sector lags behind other sectors due to reasons such as the need for an interdisciplinary approach and the need for knowledge from many fields and many people, rapidly developing technology is changing the way we practice medicine [16]. The role of AI in preventive healthcare and its contribution to automating the personalization of treatment is attracting greater attention day by day [17]. AI tools analyze big data by mimicking human intelligence and provide important insights to healthcare professionals, significantly contributing to improving health outcomes, including personalized patient care and treatment options [18]. They determine environmental and occupational health risks and ultimately provide the ability to monitor and control them. Integrating AI into clinical practice can accelerate both preventive medical decision-making and help customize disease prevention strategies by enabling early disease diagnosis and patient risk prediction [19]. Moreover, AI tools can facilitate improved access to care and health outcomes across different population groups, rather than exacerbating health disparities [20].

The Transformative Impact of Artificial Intelligence in Personalized Medicine

Through the analysis of data on individual-related characteristics such as genes, medical history, lifestyle, and environmental factors, AI algorithms can forecast the likelihood of certain diseases or conditions, allowing healthcare professionals to plan personalized prevention strategies for individuals. By tailoring interventions that reduce the risk of developing certain diseases in a timely, accurate, and cost-effective manner [1,19,20].

Genomic data integrated with AI models can determine genetic risk scores for complex diseases, such as individuals’ risk of developing coronary artery disease, obesity, diabetes, and certain cancers, which can guide early lifestyle changes or preventive treatment in a personalized manner [21,22]. AI offers unprecedented opportunities to integrate large-scale data from electronic health records (EHRs), wearable devices, genome sequencing, and social determinants of health (SDOH), enabling detailed risk stratification and early intervention [23]. Applications of AI in clinical fields are presented below, but are not limited to these examples.

Oncology is an important application area of AI. ML-based approaches allow for the diagnosis and subtyping of various new tumor entities, as well as prognosis prediction, through the analysis of data related to DNA methylation, known as epigenetic modification [22]. By combining data on gene mutations, copy number variations, and epigenetic modifications with AI-powered models, biomarkers associated with treatment efficacy can be identified. AI-enabled personalized oncology enables targeted therapies that increase treatment efficacy while minimizing side effects [24]. One important example is the use of HER2/neu expression as a biomarker for response to trastuzumab treatment in breast cancer [25]. AI-based radiomics play an important role in improving non-invasive tumor characterization and diagnostic accuracy, while robotic surgery reduces recovery times by providing higher precision in oncology procedures [26,27].

AI applications have found significant use in the field of cardiology for predictive diagnostics, real-time monitoring, and developing personalized treatment strategies. DL algorithms are used to detect arrhythmias, predict the progression of heart disease, and optimize patient management. AI-enabled wearables alert doctors to abnormalities before critical events occur. AI-based risk stratification models using patient-specific data help guide preventive interventions in cardiovascular diseases [28–30].

AI-enabled imaging analysis and predictive modeling improve patient outcomes by supporting early intervention strategies for neurological diseases, increasing diagnostic accuracy for neurodegenerative diseases such as Alzheimer’s and Parkinson’s. Automated MRI interpretation provides sensitivity that can indicate early disease onset. AI-enabled brain-computer interfaces are gaining importance for the recovery of motor functions in patients with neurological disorders [31,32].

AI-driven applications also play a vital role in the field of infectious diseases. For example, AI is essential in monitoring and managing infectious diseases through predictive modeling of outbreaks, vaccine development, and personalized treatment recommendations based on pathogen resistance models. AI-driven genomic surveillance helps track the spread of infectious agents, enabling timely control strategies [33,34].

AI, such as DL models, can analyze data such as genetic variations in genes encoding drug metabolizing enzymes and drug targets. With this strategy, called the pharmacogenomic approach, AI models can recommend the most appropriate drugs and dosages for each patient, minimize side effects, and maximize therapeutic benefits. In addition, pharmacogenomic data can also predict adverse drug reactions, allowing healthcare personnel to apply preventive measures or alternative treatment strategies for these individuals [9,35]. Many tumors, especially aggressive ones, contain morphological, genotypic, and phenotypic heterogeneity that plays an important role in their resistance and treatment failure. To overcome this challenge, more advanced models are being evaluated as identifying synergistic drug combinations may provide an effective way to reposition drugs [9,36]. AI can help manage chronic diseases such as diabetes and coronary diseases while also tailoring lifestyle changes (dietary modifications, physical activity regimens and sleep hygiene) according to individual risk profiles [37,38]. Table 2 summarized AI applications in PM and preventive medicine.

Artificial Intelligence in Preventive Medicine

AI applications offer real-time insights into epidemics, disease spread, and disease agents, as well as individuals or groups at risk of developing certain diseases. This allows for proactive and early intervention on an individual or specific group level, leading to positive outcomes for preventive medicine. By prioritizing health and well-being support early on, the demand for health services can be decreased, freeing up resources to address other areas of need [2].

By involving patients as active participants in research and care, a significant preventive aspect can be added to the overall concept [5]. ML models excel at integrating heterogeneous data such as EHRs, claims data, demographics, and SDOH to identify individuals and communities at high-risk [39,40]. AI-driven predictive models can incorporate SDOH indicators (factors such as housing, income, education, and environmental exposures) to improve risk estimates and promote health equity, ultimately guiding tailored interventions based on specific populations [41]. AI-based predictive analytics support strategic planning for healthcare resources, such as predicting increases in hospitalizations in a timely manner, administering vaccines most effectively, or helping deploy mobile clinics to underserved areas [42,43]. Early identification of high-risk individuals allows clinicians to initiate screening programs or preventive treatments earlier, including for other family members at risk [44]. Studies have shown that AI-driven alerts from wearable devices can improve patient engagement in preventive care, ultimately enabling timely interventions and reducing time and the number of hospitalizations [24]. AI has also been used for early warning systems in infectious disease outbreaks, as in the case of the coronavirus disease 2019 (COVID-19) pandemic [36,45]. Additionally, some AI models can analyze patient data such as age, medical history, and surgery type along with genomic data to predict the risk of complications such as infection or bleeding. This allows for reduced risks of complications and improved overall outcomes, with additional monitoring or preventive measures recommended for high-risk patients [37].

AI-powered platforms provide seamless automated scheduling that allows patients to easily book appointments online. Reminders are important tools for improving patient compliance with treatment plans and reducing the burden on healthcare providers. By analyzing patient data such as demographics, medical history, and past responses to appointment reminders, AI can determine the most effective reminder strategy for each patient [37].

In summary, precision medicine provides deeper insights into disease mechanisms, leading to accurate diagnoses. It can potentially minimize healthcare costs by enabling the right treatment the first time. Preventive medicine aims to reduce the risk of disease before it develops and recommends a proactive approach. AI algorithms increasingly support preventive medicine by identifying high-risk individuals, tracking patient health metrics, and making predictions based on individuals’ lifestyle and clinical data.

Emerging Technologies in Healthcare

The rapid advancement of technology has ushered in a new era of innovation that is enabling healthcare systems to deliver more accurate diagnoses, improve treatment outcomes, and increase patient engagement. The integration of these emerging digital technologies into healthcare is playing a significant role in reshaping healthcare by improving patient outcomes and enhancing clinical workflows. Key innovations include, but are not limited to [1,46,47]:

• (1) Telemedicine and digital health platforms: These systems enable timely intervention and facilitate treatment management by proactively alerting healthcare providers to potential risk factors and providing remote healthcare monitoring services.

• (2) Virtual reality and augmented reality: These technologies have emerged as powerful tools for improving medical education, surgical simulations, and patient experiences.

• (3) Digital twins: AI-powered digital twin models replicate a patient’s physiology, allowing healthcare providers to simulate potential treatment outcomes prior to therapy.

• (4) Internet of things (IoT): IoT devices track patients’ vital signs in real time, allowing healthcare providers to promptly recommend lifestyle adjustments and preventive treatments.

Challenges and Concerns Regarding Artificial Intelligence in Personalized and Preventive Medicine

AI-powered solutions in healthcare offer unprecedented opportunities to improve diagnostic accuracy, streamline workflows, and provide personalized treatment plans. However, they also bring out significant ethical, technical, societal, and regulatory challenges that need to be addressed (Table 3) [48,49].

Data privacy, transparency, and liability

Data privacy and protection are crucial consideration in implementation of AI in healthcare. AI relies on analyzing vast amounts of data to enhance healthcare professionals’ decision-making abilities. This heavy reliance on data analysis poses risk of breaches, misuse, or unauthorized access [50].

Unlike traditional healthcare, AI-driven healthcare is also undergoing rapid evolution, and there are legal gaps in liability and accountability. The need to define roles and responsibilities with clear guidelines to ensure accountability and safety remains a challenge [51]. Clear guidelines are needed to determine accountability and responsibility, and regulatory bodies such as the Food and Drug Administration in the United States and the Medicines and Healthcare products Regulatory Agency in the United Kingdom are attempting to establish frameworks [52,53].

Patients have the right to understand the rationale behind their care, and transparency and explainability are very important in this regard [12]. Yet many AI models are “black boxes” in nature, where the logic behind their predictions remains unclear, limiting transparency in the context of PM. Some researchers advocate “glass box” models that prioritize transparency and allow for strengthening the explainability of AI [54,55].

Algorithmic bias

AI systems perform their tasks within a framework of specific algorithms and undergo a continuous learning process. Therefore, these algorithms are only as unbiased as the data on which they are trained. Underrepresentation of certain groups in algorithmic datasets can lead to less accurate predictions or inadequate recommendations for these populations [1]. It is possible to reduce bias by training AI tools on diverse datasets and regularly auditing the models [41]. Several laws and regulations are underway that aim to address the alarming and unintended consequence of AI systems in healthcare, including bias [56,57].

Regulatory policy

The safeguarding of sensitive patient data is a major concern in integration of AI in healthcare. For a robust regulatory framework, a multidisciplinary approach involving clinicians, data scientists, and regulatory agencies is essential to ensure that AI solutions are both effective and practical. Regulatory bodies, such as the Health Insurance Portability and Accountability Act in the United States and the General Data Protection Regulation in the European Union, are working to establish stringent regulations to protect patient privacy and security [56,58].

Workforce and accessibility

Data is indispensable for the use of AI technologies in healthcare. Implementing it in practice requires new skill sets for clinicians and other healthcare professionals [59]. In the long term, AI solutions provide opportunities in terms of workforce training and costs associated with implementation. However, their initial implementations often require significant investments in infrastructure, training, and maintenance, which presents challenges, especially in low-resource environments. To achieve cost-effective goals, investing in comprehensive and ongoing training programs and implementation strategies are vital steps [59,60,61].

While the transformative potential of AI in PM and preventive medicine is clear, there are concerns that need to be addressed. By addressing these concerns through collaboration between technologists, policymakers, and healthcare professionals, AI can become the cornerstone of realizing this potential in the future.

DISCUSSION

PM offers various assurances, such as predicting diseases, making precise diagnoses, and optimizing treatments without compromising accuracy, cost-effectiveness, and speed [62]. While health care PM is still a young field in some countries, it has been on the agenda for a long time [4,63]. In recent years, there has been a significant increase in the volume, speed, and variety of data in the biomedical field, necessitating technological infrastructure to manage data collection, storage, and standardization effectively and rapidly. Advances in science and technology have enabled the integration of computer technology into healthcare, contributing significantly to the improvement and implementation of precision medicine, especially through advances in genomics and bioinformatics. Additionally, clinicians require advanced tools and algorithms based on real-time analysis of patient data to embrace PM, recommend potential treatments, and implement them optimally [7,62].

As AI technology advances, the rapid integration of various digital technologies into healthcare is becoming possible, improving patient outcomes and enhancing clinical workflows. AI enhances telemedicine platforms that are integrated with IoT. For example, wearable devices play a key role in identifying early warning signs of conditions such as cardiovascular disease, diabetes, and hypertension through real-time monitoring. Ultimately, this data enables healthcare providers to promptly recommend lifestyle adjustments and preventive treatments [1,46,47]. AI technologies have increasingly been applied to interpret complex medical data, including genetic information, predict disease risk, develop personalized treatment plans, and discover new therapeutic drugs [7]. The integration of PM and AI in healthcare enables the design of customized treatment plans that maximize safety and efficiency, predicting disease risk and providing more accurate diagnosis before symptoms appear [64]. AI technology such as ML and DL is essential in specific areas of PM, contributing to high-performance information analysis. The synergy between AI/ML algorithms and PM helps clinicians, physicians, and researchers solve complex problems in personalized healthcare, ultimately aiding in treatment planning and risk prediction [63].

Currently, ML, DL and AI approaches are at their peak in developing effective precision medicine for various complex diseases. By analyzing large genomic databases, AI/ML significantly reduces the cost of trial and error in the drug discovery process [45]. Early detection of Alzheimer’s disease could save as much as US$7 trillion in the United States alone due to the slow progression of the disease [65].

Re-profiling known therapeutic drugs or existing drugs currently used to treat other diseases has gained a new dimension in drug discovery and development processes, reducing risks, and providing more safety to patients. ML methods, such as the logistic regression algorithm, play a significant role in determining drug-disease relationships, while tree-based methods predict high variations. Support vector machine is another approach used to predict therapeutic classes of a drug by integrating various drug features including molecular targets, chemical structures, and gene expression data [66–68].

While AI-enabled solutions can increase diagnostic accuracy, streamline workflows, and improve personalized treatment plans, they also raise ethical, technical, and societal issues. AI is designed to analyze large data sets to enhance professionals’ decision-making abilities, often containing sensitive personal information that must be protected from unauthorized access (Table 4) [50,69]. Addressing these challenges requires an interdisciplinary approach, the development of rigorous AI techniques, and the need for regionally specific and internationally harmonized regulatory frameworks [48,49].

One of the major concerns that hinders the widespread adoption of AI-enabled PM is data privacy and security. AI models require vast amounts of patient data, which raises concerns about data ownership, privacy, and potential misuse [70]. Robust regulatory frameworks are essential to address these concerns.

Algorithmic bias is another significant challenge. This bias can lead to incorrect or inequitable healthcare decisions, especially those that disproportionately impact underserved populations [1]. Ensuring diversity in training datasets and implementing fairness measures are crucial to reducing bias.

Integrating AI into existing healthcare workflows remains a challenge. A multidisciplinary approach involving clinicians, data scientists, and regulatory agencies is essential [11].

Future Directions

The integration of AI into preventive and PM signifies a significant shift in disease prevention, diagnosis, and treatment strategies. Future advancements in AI-driven insights will aim to enhance healthcare systems by seamlessly integrating clinical workflows with unparalleled precision and scalability tailored to patient needs [12,19]. Over time, AI technologies will be capable of synthesizing genomic, environmental, and lifestyle data to forecast disease progression and improve patient outcomes [22].

AI-enabled platforms will predict disease risks and offer actionable recommendations on an individual basis. Wearable technologies and the advancement of real-time data analytics will also contribute to this transformation [38]. The integration of telemedicine and remote monitoring solutions with AI is expected to enhance healthcare accessibility, particularly in underserved and resource-limited areas [42]. Collaborative efforts among technologists, policymakers, and healthcare professionals will be crucial for ensuring transparent, unbiased, and accessible AI-enabled precision medicine [49,55]. AI models with enhanced capabilities to interpret and integrate omics data and complex biological structures will revolutionize the understanding of rare diseases, drug discovery, and personalized treatments [9,36].

In terms of the potential contribution of AI to healthcare services, real-time patient monitoring systems that predict sudden health deterioration can be considered among the most promising developments. Advanced AI models that enhance precision medicine strategies by integrating genomic, environmental, and behavioral data, along with predictive analytics systems designed to identify preventive care strategies may also be seen as promising advancements.

While the potential for AI to drive further innovation in healthcare remains significant, applications of AI in preventive medicine are still developing. As these tools evolve, they are likely to play an important role in improving proactive healthcare strategies. Advanced digital health platforms, enhanced wearable device capabilities, and sophisticated predictive analytics models can offer preventative solutions by enabling early disease detection.

CONCLUSION

Integrating AI into preventive medicine and PM not only transform healthcare but also set a new standard for meeting individual and population health needs. By leveraging AI’s ability to analyze vast amounts of genomic, environmental, and lifestyle data, healthcare practitioners can now predict disease risks, customize interventions, and improve patient outcomes with unprecedented accuracy and efficiency. The successful integration of AI into healthcare offers the opportunity to optimize diagnostic precision and therapeutic approaches. However, concerns about data privacy, algorithmic bias, transparency, and regulatory frameworks remain critical hurdles in the application of AI to PM that must be overcome. Continued workforce education, investments in research and infrastructure are crucial in diverse healthcare settings. Addressing frameworks that prioritize ethical issues and inclusivity will require interdisciplinary collaboration among healthcare providers, clinicians, and policymakers. In order to achieve successful integration, we must overcome technological, ethical and regulatory hurdles. In addition, to fully realize the potential of AI in PM, it is essential to strengthen data privacy policies, address algorithmic bias, and ensure clinical validation.

Ethics Statement

Ethical review and approval were waived for this study, because this study was not involving humans nor animals.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

None.

Author Contributions

Both authors contributed equally to conceiving the study, analyzing the data, and writing this paper.

Figure 1

Diagrammatic summary of traditional therapy versus personalized medicine. Upper: Traditional medicine is typically based on a standard of care for the entire patient population. Lower: The core principle of personalized medicine is to customize medical treatments for individual patients, considering their specific genetic variations, clinical factors, environment, and lifestyle. Drawing with Adobe Creative Suite Package ([Illustrator, version 28.7.1 and Photoshop, version 25.12] [Adobe Systems Incorporated, San Jose, CA, USA]). Adapted from [6] and [9].

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