Health Intelligence Research Articles

Artificial Intelligence in public health: A case study

Artificial Intelligence technology has become an innovative tool in global healthcare that helps to manage various problems of scale and complexity. Its use in public health is multifaceted and multivariate extending across early outbreak detection, risk modeling and prediction, efficient vaccine logistics and administration, reducing health disparities, aiding assist in diagnosis, everyday monitoring of chronic diseases, telemedicine and virtual chatbots especially for elderly care, mental health and oncology patients, and community engagement. Despite these huge generational advantages, multiple challenges exist in data quality, security and misuse, individual privacy, contextual application, accountability and oversight and remain primary concerns for universal adoption of artificial intelligence. There is a need for safeguards and a global regulatory framework to responsibly harvest the benefits of artificial intelligence for the advancement of public and community health.

Digital health and some thematic shifts in bioethics in academic publications after the pandemic

Abstract Background Digital health (DH) presents vast opportunities, but also dynamic shift in viewpoints, which is reflected in academic works. Since DH concerns patient data, a major issue is complying with the bioethical principles, and ensuring patients’ privacy and security. The aim of this research is to explore avenues, in which ethics and privacy are reflected in DH, especially in terms of research, legislation, artificial intelligence (AI) and blockchain applications in medicine and public health. Methods Literature search for full text publications in PubMed, Scopus and ScienceDirect focused on ethics and privacy in DH in the period 2019-2023 found 325 articles in English authored by academicians. Six thematic categories were identified: ethics (fundamental principles; ethics in using novel DH tools); privacy (in use of apps/wearables; as a challenge/barrier to DH interventions/technologies); ethics/privacy in: DH research (data gathering, use, sharing); policy and legislation (for DH applications); AI and blockchains (in medicine and public health). Results Articles were from 32 countries: USA-24.3%; UK-13.5%; Germany-7.7%; Canada-7.4%; Australia-6.7%; Netherlands, Italy, Switzerland-4.7% each; China, India-3.0% each and 20.3% from 22 other countries; 86.5% in medical/DH journals. Only 10.2% were published in 2019; 17.8% - 2020; 19.4% - 2021; 27.1% - 2022 and 25.5% - 2023. Privacy was discussed in 26.5%; ethics-23.7%; research-19.4%; AI-15.1%; policy and legislation-9.5% and blockchains-5.5% of the articles. The publication activity of all six categories increased after the unfold of the pandemic in 2020. In 2023 publications for ethics (13%), privacy (5.8%) and research (6.3%) decreased; for legislation (12.9%), AI (14.3%) and blockchains in healthcare (16.8%) increased. Conclusions The pandemic and its aftermath present a change in academic interest. Traditional ethical fundaments in DH slightly lose position in favor of top notch technologies and apt legislation. Key messages • The pandemic and modern technologies enforced new digital health applications. Further research will reveal the needs of AI and blockchain applications respecting privacy and the ethical principles. • Besides existing legislation and regulations, in order to catch up with the dynamic developments in DH, the legal base needs to become more flexible. Technologies change, privacy importance does not.

Navigating the AI Wave: Overcoming Barriers and Unleashing the Potential of Artificial Intelligence in Transforming European Public Health

Abstract Artificial Intelligence (AI) is not just a future promise but an urgent necessity for modern public health. By providing invaluable insights into disease patterns, therapeutic interventions, and overall public health management, AI has the potential to revolutionise healthcare. To tackle essential public health functions effectively, harnessing AI must become a top priority. However, there is an urgent need for a cohesive strategy across Europe. Currently, varying readiness levels among European nations regarding AI adoption in health result in uneven progress across the continent. This disparity must be addressed to ensure all countries benefit equally from AI advancements. Recognising this, the World Health Organization European Regional Office launched a regional report on digital health in 2023. The report evaluated the integration of big data and advanced analytics, including AI, in health systems. Findings revealed that while 60% of Member States have a national data strategy, only 35% have a policy regulating big data and AI in health, and 38% lack both. This highlights a critical gap that must be filled urgently. Our upcoming session will address these challenges head-on. Organised to provide visionary insights, practical applications, and a landscape view of artificial intelligence in public health, the presentations will be followed by a roundtable discussion in which panellists will delve into practical challenges surrounding AI adoption. They will reflect on the profound impact AI could have on the future of European health systems and offer pragmatic and responsible steps forward, culminating in achievable recommendations for public health professionals. To enhance the session, we will utilise existing Generative AI tools to provide a real-time summary of the plenary and reinforce the call to action in alignment with panellists’ recommendations. This approach ensures that the session discusses the urgent need for AI in public health and actively demonstrates its practical applications. Moderators Natasha Azzopardi Muscat Director, Division of Country Health Policies and Systems, WHO Regional Office for Europe Dimitra Panteli Programme Manager/Senior Health Systems Analyst, European Observatory on Health Systems and Policies Facilitator Stefan Buttigieg Vice-President, EUPHA Digital health section Speakers/Panellists Martin McKee Professor of European Public Health, London School of Hygiene & Tropical Medicine, UK Katharina Ladewig Director, Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Germany Marco Marsella Director Digital, EU4Health and Health systems modernisation, Directorate-General for Health and Food Safety (DG SANTE), European Commission Keyrellous Adib Technical Officer Data Science and Digital Health, WHO Regional Office for Europe

The information layer: shaping data into meaning with interactive tools

Abstract Valorization and interpretation of health data are contingent upon a robust analytical framework. This framework encompasses essential processes such as data cleaning, validation, and the critical evaluation of bias and missing data. A nuanced understanding of these factors is crucial for contextualizing data through stratification, developing comprehensive analysis plans, and addressing interactions, covariates, and confounders. Such meticulous methodologies guard against ‘data fishing expeditions’ that could otherwise lead to erroneous conclusions. Healthybelgium.be exemplifies our approach for deciding on appropriate levels of data aggregation and stratification. This study serves as a foundation for deriving conclusions and formulating hypotheses regarding the health status of the national and various sub-populations. Our discussion will cover the limitations of the questions that can be answered with the available data, the resulting opportunities for data enhancement, and the subsequent valorization of findings for knowledge advancement. Furthermore, we will present the interactive tools (through the use of R Shiny) developed by Sciensano to disseminate information broadly, making it accessible and comprehensible to diverse audiences. These tools embody a balance of strength and limitation, facilitating an informed dialogue among stakeholders. The ongoing refinement of these tools, influenced by continuous learning and insights, exemplifies our commitment to evolving public health intelligence.

Lifecycles, pipelines, and value chains: toward a focus on events in responsible artificial intelligence for health

Lifecycles, pipelines, and value chains: toward a focus on events in responsible artificial intelligence for health

The Revised Declaration of Helsinki—Considerations for the Future of Artificial Intelligence in Health and Medical Research

This Viewpoint summarizes recent updates to the Declaration of Helsinki, discusses its relevance in the context of artificial intelligence (AI) in health research, and highlights issues that could affect its future implementation as the use of AI in research increases.

Adolescent risk behaviours are associated with educational attainment in early adulthood: results from the Raine Study cohort

BackgroundHigher educational attainment is important for economic wellbeing and associated with better health and longevity. Previous research focused on intelligence, socioeconomic status and mental health or individual risk behaviours as predictors of educational attainment, but the role of multiple domains of adolescent risk behaviours is less clear. This study examined the association between multiple domains of risk behaviour in adolescence and educational attainment by 22 years-of-age.MethodsYoung people (Generation 2, Gen2) and their parents (Generation 1, Gen1) participating in the Raine Study completed questionnaires at years 1, 5, 8, 10 (Gen1 only), 14, 17 (both) and 22 (Gen2 only). The Raine Study is an ongoing longitudinal study initiated in Perth, Western Australia, between 1989 and 1991. The 1,102 Gen2 participants who responded to questions about highest educational attainment were included in this study. The association between Gen2 self-reported risk behaviours (including age at commencement of drinking alcohol, smoking, sexual intercourse and drug use) and educational attainment (defined as self-reported years of completed high school: ≤10, 11, 12 or tertiary education (> 12)) at year 22, after adjusting for mother’s age and combined parental education level, participant sex, and family income, educational performance and adolescent mental health, was explored using ordinal regression models and presented as odds ratios (OR) with 95% confidence intervals (CI).ResultsOrdinal models suggested that never smoking or starting older than 18 compared with smoking before age 15 (OR 2.02, 95%CI: 1.28–2.14); first drinking alcohol between 15 and 17 years compared with younger than 15 (OR 1.52, 95%CI: 1.08–2.14); and, first sexual intercourse aged ≥ 18 years compared with under 15 (OR 1.67, 95%CI: 1.08–2.57) were associated with higher levels of educational attainment at 22-year follow-up. Additionally, lower (“better”) behavioural scores increased the odds of higher levels of attainment.ConclusionsAbsence of health risk behaviours at a younger age or later commencement was associated with higher educational attainment. Evidence-based interventions that address the societal influences underpinning risk behaviours in adolescents may support longer school retention.

Innovative Approaches to Strengthening Preventative Care in Contemporary Healthcare: A Systematic Review

Preventative care plays a critical role in reducing disease burden, improving patient outcomes, and lowering healthcare costs in contemporary medicine. However, the effective implementation of preventative care strategies faces numerous challenges, including technological, financial, and social barriers. This systematic review explores innovative approaches aimed at strengthening preventative care in modern healthcare systems. Key innovations include the use of telemedicine, artificial intelligence (AI), personalized medicine, and community-based public health initiatives. Telemedicine and AI have revolutionized early detection and risk assessment, while personalized medicine and genomic screening allow for tailored preventative interventions. Additionally, behavioral economics and health education programs have proven effective in promoting healthier lifestyles and improving adherence to preventative measures. The review evaluates the impact of these innovations on patient outcomes and highlights both the benefits and challenges of implementing these approaches. While the advancements offer promising results, further research is needed to address issues related to equity, ethical concerns, and global applicability. This review provides valuable insights for healthcare providers, policymakers, and researchers looking to enhance the effectiveness of preventative care strategies in contemporary healthcare systems.

The advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries

The advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries

Biobanking, digital health and privacy: the choices of 1410 volunteers and neurological patients regarding limitations on use of data and biological samples, return of results and sharing

BackgroundThe growing diffusion of artificial intelligence, data science and digital health has highlighted the role of collection of data and biological samples, thus raising legal and ethical concerns regarding its use and dissemination. Further, the expansion of biobanking, from the basic collection of frozen specimens to the virtual biobanks of specimens and associated data that exist today, has given a revolutionary potential on healthcare systems, particularly in the field of neurological diseases, due to the inaccessibility of central nervous system and the need of non-invasive investigation approaches. Informed Consent (IC) is considered mandatory in all research studies and specimen collections, and must specifically take into account the ethical respect to the individuals to whom the used biological material and data belong.MethodsWe evaluated the attitudes of patients with neurological diseases (NP) and healthy volunteers (HV) towards the donation of biological samples to a biobank for future research studies on neurological diseases, and limitations on the use of data, related to the requirements set by the General Data Protection Regulation (GDPR). The study involved a total of 1454 subjects, including 502 HVs and 952 NPs, recruited at Santa Lucia Foundation IRCCS, Rome, from 2020 to 2024.ResultsWe found that (i) almost all subjects agreed with the participation in biobanking (ii) and authorization to genetic studies (HV = 99.1%; NP = 98.3%); Regarding the return of results, (iii) we found a statistically significant difference between NP and HV, the latter preferring not to be informed of potential results (HV = 43%; NP = 11.3%; p < 0.0001); (iv) a small number limited the sharing inside European Union (EU) (HV = 4.6%; NP = 6.6%), whereas patients were more likely to refuse transfer outside EU (HV = 7.4%; NP = 10.7% p = 0.05); (v) nearly all patients agreed with the use of additional health data from EMR for research purposes (98.9%).ConclusionsConsent for the donation of material for research purposes is crucial for biobanking and biomedical research studies that use biological material of human origin. Here, we have shown that choices regarding participation in a neurological biobank can be different between HVs and NPs, even if the benefit for research and scientific progress is recognized. NP have a strong interest in being informed of possible results but limit sharing of samples, highlighting a perception of greater individual or relative benefit, while HV prefer a wide dissemination and sharing of data but not to have the return of the results, favoring a possible benefit for society and knowledge. The results underline the need to carefully manage biological material and data collected in biobanks, in compliance with the GDPR and the specific requests of donors.

EARTHQUAKE-RESISTANT BUILDING DESIGN: INNOVATIONS AND CHALLENGES

This study provides a comprehensive systematic review of innovations in earthquake-resistant building design, focusing on advancements in materials, technologies, and methodologies aimed at enhancing structural resilience. A total of 32 peer-reviewed articles were analyzed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The findings highlight the critical role of advanced materials such as fiber-reinforced polymers (FRPs) and shape memory alloys (SMAs) in improving seismic performance, particularly through enhanced energy dissipation and structural flexibility. Technological integrations like Building Information Modeling (BIM), artificial intelligence (AI), and structural health monitoring (SHM) systems were identified as transformative tools that optimize design processes, predict structural vulnerabilities, and enable real-time risk management. Advanced simulation techniques, including finite element analysis (FEA) and computational fluid dynamics (CFD), were shown to significantly improve the accuracy and efficiency of seismic design. Despite these innovations, challenges related to cost, regulatory inconsistencies, and limited access to cutting-edge technologies persist, particularly in developing regions. The study concludes that while these advancements have revolutionized earthquake-resistant design, further efforts are needed to address these barriers and promote global resilience to seismic hazards.

The promise of artificial intelligence in health: Portrayals of emerging healthcare technologies

AbstractEmerging technologies of artificial intelligence (AI) and automated decision‐making (ADM) promise to advance many industries. Healthcare is a key locus for new developments, where operational improvements are magnified by the bigger‐picture promise of improved care and outcomes for patients. Forming the zeitgeist of contemporary sociotechnical innovation in healthcare, media portrayals of these technologies can shape how they are implemented, experienced and understood across healthcare systems. This article identifies current applications of AI and ADM within Australian healthcare contexts and analyses how these technologies are being portrayed within news and industry media. It offers a categorisation of leading applications of AI and ADM: monitoring and tracking, data management and analysis, cloud computing, and robotics. Discussing how AI and ADM are depicted in relation to health and care practices, it examines the sense of promise that is enlivened in these representations. The article concludes by considering the implications of promissory discourses for how technologies are understood and integrated into practices and sites of healthcare.

The Impact of Life Skills Training (LST) on Emotional Intelligence of High School Students

Emotional Intelligence (EI) has a major role in one’s mental health and Life Skills Training (LST) (LST) could be viewed as a bridge relating Emotional Intelligence and mental health. The purpose of this study was to investigate the effect of Life Skills Training (LST) on the Emotional Intelligence and Life skills of adolescent students. Using a pretest posttest control group design this hypothesis was analyzed.

Mapping vulnerability to climate-related hazards to inform local authority action in adaptation: A feasibility study

BackgroundLocal authorities have a crucial role in building community resilience to the health effects of a changing climate. Support in achieving local action can be provided through improving available public health intelligence to inform decision making. We aimed to co-develop with a local authority a tool mapping vulnerability to climate related hazards. MethodsWe conducted a feasibility study, exploring through stakeholder engagement local priorities and levers for action in adaptation that could be informed by provision of increased intelligence. This informed co-development of a proof-of-concept tool. ResultsStakeholders reported needs in better understanding the intersection between vulnerability and hazard to facilitate partnership working, decision making, and targeting of interventions. We developed a mapping tool, using nationally available data, overlaying a vulnerability index with hazard (heat and flooding) exposure. ConclusionsMapping tools are feasible methods by which public health intelligence to support climate change adaptation planning can be shared. Barriers to action may result from the complexity of vulnerability, concerns of unintended consequences, and resource constraints. Co-development with local expertise is necessary to ensure that outputs add value to local response. This tool will now be piloted to gather feedback on useability, usefulness, and potential improvements.

Machine learning and natural language processing to assess the emotional impact of influencers' mental health content on Instagram.

This study aims to examine, through artificial intelligence, specifically machine learning, the emotional impact generated by disclosures about mental health on social media. In contrast to previous research, which primarily focused on identifying psychopathologies, our study investigates the emotional response to mental health-related content on Instagram, particularly content created by influencers/celebrities. This platform, especially favored by the youth, is the stage where these influencers exert significant social impact, and where their analysis holds strong relevance. Analyzing mental health with machine learning techniques on Instagram is unprecedented, as all existing research has primarily focused on Twitter. This research involves creating a new corpus labelled with responses to mental health posts made by influencers/celebrities on Instagram, categorized by emotions such as love/admiration, anger/contempt/mockery, gratitude, identification/empathy, and sadness. The study is complemented by modelling a set of machine learning algorithms to efficiently detect the emotions arising when faced with these mental health disclosures on Instagram, using the previous corpus. Results have shown that machine learning algorithms can effectively detect such emotional responses. Traditional techniques, such as Random Forest, showed decent performance with low computational loads (around 50%), while deep learning and Bidirectional Encoder Representation from Transformers (BERT) algorithms achieved very good results. In particular, the BERT models reached accuracy levels between 86-90%, and the deep learning model achieved 72% accuracy. These results are satisfactory, considering that predicting emotions, especially in social networks, is challenging due to factors such as the subjectivity of emotion interpretation, the variability of emotions between individuals, and the interpretation of emotions in different cultures and communities. This cross-cutting research between mental health and artificial intelligence allows us to understand the emotional impact generated by mental health content on social networks, especially content generated by influential celebrities among young people. The application of machine learning allows us to understand the emotional reactions of society to messages related to mental health, which is highly innovative and socially relevant given the importance of the phenomenon in societies. In fact, the proposed algorithms' high accuracy (86-90%) in social contexts like mental health, where detecting negative emotions is crucial, presents a promising research avenue. Achieving such levels of accuracy is highly valuable due to the significant implications of false positives or false negatives in this social context.

The Role of Public Health in Promoting Health Surveillance

Public health plays an important role in promoting health surveillance by systematically monitoring, collecting, analyzing, and interpreting health data to guide interventions, policies, and practices. Health surveillance systems are essential for early detection of outbreaks, monitoring disease trends, and evaluating the effectiveness of health promotion initiatives. This paper traces the evolution of health surveillance, examines the role of public health in implementing surveillance systems, and highlights the challenges faced, particularly in the Asia Pacific region. Emphasis is placed on the importance of integrating data from various sources and employing both quantitative and qualitative tools for robust health intelligence. Recommendations for strengthening surveillance systems, including the use of digital tools and better resource allocation, are also discussed. Keywords: Public health, health surveillance, disease monitoring, health promotion, epidemic intelligence, data analysis.

Unveiling spatio-temporal mysteries: A quest to decode India's Dengue and Malaria trend (2003-2022)

Dengue and malaria are two mosquito-borne diseases that are dangerous globally, especially in tropical and subtropical regions. In India, these two diseases pose severe health issues as they account for 74.37 % of the total vector-borne disease burden in the country. The present study examined the spatio-temporal patterns of prevalence of dengue and malaria across all states in India. Data related to epidemiological statistics were obtained from the Central Bureau of Health Intelligence (CBHI) and the National Vector Borne Disease Control Program (NVBDCP) for 2003–2017 and 2018–2022, respectively. In this study, we have utilized the Mann-Kendall test, Modified Mann-Kendall test, Sens's slope, Innovative trend analysis, and Percent Bias for trend analysis. Furthermore, a hotspot analysis was conducted to compare and examine the evolving patterns of these diseases over space and time. The Mann-Kendall test showed a significant increase in dengue cases throughout India, with Sen's slope showing the fastest growth in Punjab. West Bengal exhibited the most significant ITA slope increase. The PBIAS slope showed a gradual rise from the southern to the northern and north-eastern states. Mann-Kendall results indicated a statistically significant decline in malaria cases, dropping mostly in Odisha, followed by the northern, southern, and north-eastern states. Only Mizoram displayed an insignificant upward trend in malaria cases. Hotspot analysis revealed that dengue fever hotspots expanded in India's central, western, and northern regions, affecting 66.72 % of the country, whereas significant coldspots remain unchanged. Malaria hotspots covered 47.46 % of north-eastern, eastern coastal, and northern areas, while coldspots almost remained unchanged. This study provides valuable insights for health authorities to prioritize and identify the regions that need immediate intervention regarding these two mosquito-borne diseases.

The scale and timing of child protection system contacts from 0-18 years: a study of 1.9 million Australian children

IntroductionTo prevent abuse, neglect and poor outcomes for children and families, including child protection intervention, maltreatment, and removals, it is critical to understand the scale and timing of child protection system contacts to identify opportunities for early prevention. Objectives and ApproachWe quantified the cumulative incidence of child protection contacts during childhood, including initial reports, screened-in reports, investigations, substantiations and removal into out-of-home-care, among all children in two Australian states. We used child protection system data linked for the South Australian (SA) BEBOLD and NSW Child E-Cohort data platforms as the numerator, and census data for the denominator, for 571,497 SA children (birth-years 1991-2019) and 1,362,505 NSW children (birth-years 2005-2018). ResultsOf children born in 2005 (NSW SA): Two in five children were reported to child protection by age 14; one in ten children were the subject of a child protection investigation in NSW and SA; one in twelve children in NSW and one in 17 in SA, were substantiated at least once; and one in 35 children in NSW and one in 40 in SA were removed into out-of-home care at least once by age 14. Approximately half of all types of child protection contact first occurred by the child’s fourth birthday. Conclusions/implicationsThe scale of lifecourse child protection contact underscores the opportunities for early health and social supports to prevent child maltreatment and escalating child protection intervention. Child protection reports also represent an asset to generate public health intelligence to inform early prevention.

Association between emotional intelligence and mental health status based on sex differences among undergraduates in Vietnam

Public health concerns about mental health problems have recently received attention, particularly since the COVID-19 pandemic. Emotional intelligence (EI) is a significant factor linked to mental health status, including depression, anxiety, and stress (DAS), with sex playing a significant role in both DAS and EI status. This study aimed to describe DAS and EI status and assess sex differences in the associations between EI and DAS among undergraduate students. A cross-sectional study was conducted across three educational institutions in Dong Thap province, southern Vietnam, using the validated Schutte Self-Report Emotional Intelligence Test (SSEIT) and the 21-item Depression, Anxiety, and Stress Scale (DASS-21). Spearman correlation analysis was used to identify correlations between DAS and EI, and the sex differences in the associations between DAS and the four SSEIT constructs - emotion perception (EP), utilizing emotion (UE), managing self-relevant emotion (MSE), and managing others' emotion (MOE) - were examined using linear regression models. Among the 674 participants, 51.6% experienced mental health problems (64.1% for females, 35.9% for males), 47.0% experienced anxiety, 32.5% experienced depression, and 27.4% experienced stress. The average EI score was 119.3 ± 18.9 (120.0 ± 19.1 for females, 118.0 ± 18.5 for males), with 58.9% of participants having normal EI, while 29.2% and 11.9% had low and high EI, respectively. Negative correlations were observed between EI scores and depression (R=-0.16, p&lt;0.001) and anxiety (R=-0.09, p=0.020), especially among females. Findings indicated that MSE showed significant associations with DAS, especially in females (all β&lt;-0.318, all p&lt;0.001). Our study highlights the importance of considering sex in mental health prevention programs targeting EI skills to manage students' self-relevant emotions better.

Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems.

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, drug-drug interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.