Empowering public health: Leveraging AI for early detection, treatment, and disease prevention in communities – A scoping review

ABSTRACT

India’s healthcare system faces substantial challenges, including a high burden of communicable and non-communicable diseases, limited access to healthcare in rural areas, and a shortage of skilled healthcare professionals. Artificial intelligence (AI) offers promising solutions to address these gaps by enhancing diagnostic accuracy, improving disease prediction, and optimizing treatment management. This scoping review examines AI’s role in early detection, treatment, and disease prevention in community health settings. A comprehensive literature search was conducted in PubMed, Embase, Scopus, and Google Scholar from January 2013 to July 2024. Eligible studies focused on the application of AI in public health, emphasizing early detection, disease prevention, and treatment interventions. Data on AI models, health outcomes, and performance metrics were extracted and analyzed in line with PRISMA-ScR guidelines. Forty-eight studies were analyzed and categorized into diagnostic accuracy, disease prediction, treatment management, and clinical validation. AI-based tools, such as AIDMAN for malaria detection, demonstrated high diagnostic accuracy (95%) and AUC (0.96). Predictive models for chronic kidney disease (93% accuracy) and diabetes (91% accuracy) showed substantial promise. TB screening using AI-powered cough analysis achieved 86% accuracy. The studies also emphasized AI’s role in managing chronic diseases, facilitating early interventions, and reducing healthcare burdens in resource-limited settings. AI has the potential to revolutionize healthcare delivery in India, particularly in underserved regions, by enhancing early detection and treatment. However, challenges related to data privacy, algorithmic bias, and infrastructure require attention. Continued research and policy development are essential to fully harness AI’s capabilities in improving public health outcomes.

Introduction

The growing population, diverse healthcare needs, and limited resources constrain the performance of health systems in India. Despite significant progress, communicable diseases such as tuberculosis and malaria remain prevalent, especially in rural areas, alongside a rising burden of non-communicable diseases (NCDs) such as cardiovascular disease, diabetes, and cancer. Cardiovascular diseases alone account for nearly 28% of NCD-related deaths.[1] Emerging challenges, including antimicrobial resistance and environmental pollution, further strain the healthcare system. Additionally, the shortage of healthcare professionals and facilities, particularly in rural areas, hampers the delivery of optimal care. India currently faces a deficit of 6 million healthcare workers, with a patient-to-doctor ratio far below WHO recommendations. This shortage contributes to diagnostic delays and health inequities, particularly in cancer care, where 70% of cancer-related deaths result from late diagnoses.[2]

Financial constraints add complexity, with healthcare spending in India at only 1.6% of GDP, well below the global average. Despite investments, the shortage of human resources limits coverage and leads to delayed diagnoses, further burdening an already overstretched system. In light of these challenges, the integration of artificial intelligence (AI) has the potential to revolutionize healthcare, with estimates suggesting it could reduce costs by up to $150 billion annually by 2026.[3]

AI technologies, including machine learning (ML) and deep learning (DL), automate human-like tasks, with ML analyzing historical data for future predictions and DL handling complex tasks such as image classification. AI and big data analytics enable early detection, precision treatment, and disease prevention with minimal intervention, offering a path toward universal health coverage in India.

This scoping review explores the role of AI in community health, focusing on its applications in prediction, diagnosis, prevention, and treatment through personalized healthcare plans. Additionally, the review examines AI’s potential in advancing public health education and health management. By assessing both the opportunities and challenges of integrating AI into community health systems, the review provides actionable insights for public health professionals, policymakers, and developers to enhance AI-driven healthcare interventions and improve overall public health outcomes.

Materials and Methods

Search strategy

A comprehensive search strategy was developed to identify relevant published studies between January 1, 2013 and July 1, 2024, in line with the scope of this scoping review. The databases searched included Medline through PubMed, Embase, Scopus, Google Scholar (first 100 results), and ResearchGate (first 100 results). The search strategy utilized a combination of Medical Subject Headings (MeSH) terms and free-text keywords to ensure inclusivity and precision. The MeSH terms included: “Public Health,” “Artificial Intelligence,” “Disease Diagnosis,” “Treatment Outcome,” “Disease Prevention,” “Community Health Services,” “Primary Health Care,” “Global,” and “India.” Free-text keywords such as “AI,” “Artificial Intelligence,” “Machine Learning,” “Community Health,” “Public Health,” “Early Diagnosis,” “Disease Prediction,” “Preventive Care,” “Community-based AI,” “Health Technology,” and “Digital Health Innovation” were incorporated to capture broader and nuanced concepts. This strategy aimed to focus on studies assessing the role of AI in public health, particularly in the domains of disease prediction, diagnostic accuracy, treatment management, and prevention strategies within community-based settings including India. This study adhered to the PRISMA-ScR guidelines.[4] A scoping review was more suitable than a systematic review for this study as we wanted to investigate the broadness and the features of the available literature related to the aim of the study. This procedure allowed us to integrate results from studies having different designs in the review updating the attributes of the topic. This review included studies published from 2013 onward, reflecting a deliberate choice to focus on the last decade. This period was selected to ensure the inclusion of the most up-to-date and relevant evidence, as the field of AI has undergone rapid advancements during this time. Key developments in ML algorithms, computational power, and their applications to healthcare have significantly shaped the utility of AI in public health. Studies published prior to 2013 were excluded, as they are less likely to align with the current technological landscape and practical applications of AI in community health settings.

Selection criteria

The inclusion criteria for this scoping review were designed to comprehensively capture relevant studies addressing the application of AI in community health. Articles were included if they specifically explored the use of AI in diagnostic accuracy, disease prediction, treatment management, or clinical validation within community-based healthcare settings. To ensure the inclusion of recent advancements, only peer-reviewed original research articles, such as cross-sectional studies, randomized controlled trials, cohort studies, and systematic reviews, published within the last 10 years (2013–2024), were considered. The review included studies conducted globally, with particular emphasis on applications in low- and middle-income countries. Articles were required to report measurable outcomes, such as accuracy, sensitivity, specificity, or effectiveness of AI models in healthcare delivery, public health education, or disease prevention. To maintain consistency, only studies published in English were included, and full-text accessibility through institutional or open-access databases was ensured.

Data extraction

Data extraction was systematically carried out to gather pertinent information from each study, including the author (s), year of publication, study objectives, AI models or applications utilized, evaluated health parameters, and key findings. Data extraction was performed separately by two reviewers who used the same template in an effort to achieve uniformity and correctness. Characteristics of the population of interest such as the sample size and the intervention or technique of AI employed in the study were included. The anticipated outcome measures such as the rates of early detection, efficacy of the treatment, and prevention of the disease were extracted. Additionally, the key findings and conclusions drawn from each study were recorded.

Study inclusion

The processes of title and abstract selection were conducted independently by the two reviewers (SN and NV) with the assistance of another reviewer (RAD) in cases of disagreement. For each of the selected titles/abstracts, full texts were sourced and their bibliographic references were searched for additional relevant studies. The authors of the articles of interest were also contacted in any case full text were not available.

Data collection methods

The results were compiled using a narrative synthesis approach due to the diverse character of the included studies. A comprehensive risk of bias evaluation was not carried out because of the variety of study types and AI models used. Rather, important themes and trends found in all the investigations were combined and given in a descriptive manner.

Results

Description of findings

A total of 834 records [Figure 1] were identified through database searching, with an additional 12 records from other sources. After removing duplicates, 704 records were screened, of which 641 were excluded for not meeting the inclusion criteria. Subsequently, 63 full-text articles were assessed for eligibility, and 15 were excluded for reasons such as being review articles (7), partial use of AI (4), unavailability of full text (1), and being conference papers (3). Finally, 48 studies were included in the qualitative synthesis. The studies were grouped into four key domains based on the utility of AI in community-based health: a) diagnostic accuracy, focusing on AI’s ability to precisely identify diseases through clinical tests and imaging; b) disease prediction, assessing AI’s role in forecasting health conditions based on patient and environmental data; c) treatment and management, involving the optimization of personalized care plans and therapeutic monitoring; and d) clinical validation, evaluating the real-world effectiveness and reliability of AI systems in community health settings. All the studies included in this review are listed in Supplementary File Table S1.

Figure 1.

Flow of studies included in the review

Diagnostic accuracy

AI has demonstrated substantial improvements in diagnostic accuracy across various diseases. Liu et al. (2023)[5] reported that AIDMAN, an AI-based system for diagnosing malaria, achieved 95% accuracy, with 94% sensitivity and an AUC of 0.96, comparable to standard microscopy. Similarly, Yang et al. (2020)[6] used DL to develop a mobile app for malaria diagnosis, reporting 93% accuracy and 92% sensitivity. In dermatology, Nei et al. (2023)[7] and Esteva et al. (2017)[8] achieved a diagnostic accuracy of 89% and 87%, respectively, using convolutional neural networks to distinguish between benign and malignant lesions. These results highlight AI’s efficacy in image-based diagnoses such as skin cancer and radiology [Figure 2].

Figure 2.

AI diagnostic accuracy for malaria and skin cancer detection

Disease prediction and early detection

AI is increasingly employed for early detection and disease prediction. Choi et al. (2022)[9] used recurrent neural networks for heart failure prediction, achieving 85% sensitivity and 88% specificity, with an AUC of 0.89. Singh et al. (2022)[10] demonstrated AI’s effectiveness in the early detection of chronic kidney disease (CKD), with 93% accuracy and an AUC of 0.93. Additionally, Fan et al. (2021)[11] and Montagna et al. (2023)[12] used ML to predict diabetes complications and hypertension, with accuracies of 91% and 82%, respectively. In obstetrics, Wu et al. (2021)[13] predicted gestational diabetes mellitus (GDM) using logistic regression and deep neural networks, achieving accuracy rates of 80% and 77%, respectively. These models emphasize AI’s role in advancing preventive medicine.

Treatment and management

AI is also proving valuable in managing chronic diseases such as diabetes and tuberculosis (TB). Dagliati et al. (2017)[14] applied artificial neural networks to predict diabetic complications, with 88% accuracy. For TB, Chen et al. (2023)[15] used ML to differentiate active from latent TB, achieving 89% sensitivity and an AUC of 0.90. Yellapu et al. (2022)[16] reported that the Swaasa AI platform, using cough sound analysis for TB screening, had an accuracy of 86%. These findings suggest that AI can enhance treatment decisions through clinical, audio, and imaging data, though successful implementation depends on integration into clinical systems.

Clinical validation and outcomes

AI models have been validated in real-world clinical settings. Rajkomar et al. (2018)[17] demonstrated that DL algorithms could predict clinical events from electronic health records with 92% accuracy. In oncology, Chen et al. (2021)[18] showed AI’s effectiveness in cancer diagnosis, offering significant advancements in personalized care. For tuberculosis, Luo et al. (2022)[19] reported AI models achieving 92% sensitivity and 91% specificity. These outcomes affirm AI’s potential to improve diagnostic accuracy and clinical outcomes, strengthening public health systems.

The role of artificial intelligence in public health education and health management

AI is transforming both public health education and health management. In education, AI enhances skill development by acting as intelligent tutors and decision-making aids, providing personalized learning experiences by analyzing individual learning patterns. AI also automates routine tasks such as grading and answering frequently asked questions, freeing educators to focus on more complex activities.[20] Additionally, AI-driven e-learning platforms foster equitable access to resources, promoting a more inclusive educational environment.[21] Although AI has greatly advanced theoretical training and real-time data analysis in public health education, there is still untapped potential in integrating AI for practical training, enabling real-time data access, and improving content delivery.[22]

In health management, AI is revolutionizing decision-making systems, quality control of medical records, and personalized health interventions.[23] ML algorithms analyze large datasets to forecast public health threats and disease outbreaks, enabling timely and effective action by healthcare professionals. AI is also making strides in mental health, with tools such as the Wysa chatbot, providing emotional support and guiding users when professional help is needed.[24] In cardiovascular care, AI devices such as AliveCor detect heart rhythm disturbances and offer personalized health recommendations.[25] AI-powered diagnostic systems are improving the precision of disease detection, often surpassing human specialists in identifying early signs of conditions such as cancer. Wearable and mobile health devices, integrated with AI, gather real-time data and provide personalized recommendations, enhancing proactive health management and early detection of potential health risks.[26]

Discussion

Summary of findings

The field of healthcare has been changed with the introduction of AI owing to its broad reference for diagnostic and prognostic improvement. The efficacy of new diagnostic systems such as the malaria detection hybrid AIDMAN and DL systems in identifying TB and skin lesions has improved the diagnostic power of medical images. These innovations offer the option of immediate initiation of treatment, which is critical for managing and controlling diseases. AI is also involved in the prevention processes for such clinical red flags by assisting in screening for patients with diabetic retinopathy and in predicting chronic kidney disorders. Furthermore, AI has predicted the occurrence of future complications arising from diseases and even assisted in drug development. Systems such as DeepMalaria aid in the prediction of anti-malarial compounds, while AI systems help in devising personalized therapy for diabetes management. In addition, disease surveillance assisted by AI is important in the early detection and prevention of both communicable diseases and NCDs, particularly at the primary healthcare level in low- and middle-income countries. Additionally, solutions powered by AI are beneficial to the management of the health system by providing services that include screening for diabetic retinopathy and maternal health programs to achieve better health outcomes.

Algorithm bias and its generalizability

While AI holds promise for improving accessibility, equity, and efficiency in healthcare, several challenges persist. Protecting patient data is crucial due to the large amounts of sensitive information involved, necessitating robust data governance policies and compliance with regulations such as the General Data Protection Regulation (GDPR).[27] Algorithmic bias is another major concern, as biased or inadequate training data can lead to unfavorable outcomes for certain population groups. To mitigate this, it is essential to assess data quality before and during AI model development. Additionally, the digital divide marked by poor infrastructure and limited health and digital literacy hinders AI adoption, particularly in marginalized communities.[28] Ensuring universal access to AI-driven healthcare is vital to prevent widening inequities. In India, AI faces unique challenges, especially in rural areas, where only 37% of the population has access to health facilities within a 5-km radius.[29] The fragmented healthcare data system, coupled with the country’s socioeconomic and cultural diversity, exacerbates algorithmic bias and limits generalizability.[30] Moreover, capacity building within the healthcare workforce is needed to develop skills for AI use, and inadequate financial resources further obstruct AI integration into the health system.[31]

Global perspective

AI systems are being integrated into public health facilities across countries around the world. Within the borders of the United States, the All of Us Research Program of the US National Institutes of Health intends to develop a heterogeneous database that considers such parameters as the analysis of health data with the help of AI.[32] Now the services provided by the National Health Service of the United Kingdom (NHS) have a technological dimension in which AI tools are used for early diagnosis and development of personalized treatment plans.[33] The Healthy China 2030 initiative focuses on communicable disease control and management through the application of AI technologies.[34] The Canadian Virtual Hospice used AI in the enhancement of the palliative care delivery system.[35] These global endeavors portray the potential role of AI in the field of public health by improving the early detection, monitoring, diagnostics, and treatment as well as the efficiency of healthcare systems. Cross-comparing these interventions in conjunction with the Indian context could assist Indian policymakers and other stakeholders in devising the implementation strategies for AI in public health in India.

Ethical challenges and frameworks for AI integration in healthcare and telemedicine

While AI holds great promise in healthcare, it also presents significant ethical challenges, particularly around issues such as informed consent. The opaque nature of “black-box” algorithms complicates transparency, making it essential to clearly communicate the role of AI technologies in patient care. For AI-driven apps and tools, clear and comprehensible user agreements are vital. Additionally, protecting patient data, ensuring algorithmic safety, and maintaining fairness in AI systems are critical ethical concerns. These can be addressed through regular audits and diverse teams in AI development. However, there is a need for strict regulatory frameworks to ensure transparency, privacy, and reduced bias in AI applications.[36]

In the context of telemedicine, AI has improved diagnostic and therapeutic processes by minimizing medical errors, but it also brings challenges in terms of safety, regulation, and cost. AI tools must comply with regulatory requirements to ensure patient safety, data privacy, and algorithm accuracy.[37] Financial constraints, particularly for smaller healthcare providers, limit access to AI technologies. Telemedicine, despite its benefits, cannot fully replace in-person care. Concerns include potential data breaches, reliance on patient self-reports, and delays in procedures due to the lack of physical examinations. Additionally, state regulations and the high costs of telemedicine infrastructure further complicate its widespread adoption.[38]

AI technology has the potential to bridge healthcare gaps in underserved areas where traditional healthcare infrastructure has struggled to reach. Through telemedicine and mobile health applications, AI can facilitate remote consultations, diagnostics, and health education without the need for extensive physical infrastructure. AI-powered tools, such as smartphone-compatible diagnostics and decision-support systems for community health workers, enable cost-effective screening and improved care delivery in remote settings. Additionally, AI’s ability to analyze population data can optimize resource allocation, ensuring targeted interventions in high-need areas. Collaborative efforts among governments, NGOs, and tech companies, combined with investments in digital infrastructure, further enhance the scalability and accessibility of AI-driven healthcare solutions, making it possible to reach even the most resource-limited communities.

Scalability and readiness

• Infrastructure Challenges: The scale of use of AI within Indian healthcare is restricted primarily by the absence of requisite infrastructure, particularly in rural pockets of the country. There is a need for systematic infrastructural advancement in terms of digital technology, supplementary electricity, and internet connectivity.[39]

• Workforce Capacity: India’s doctor-patient ratio is 1:834, which is below the WHO recommendation,[40] with rural areas especially underserved. AI training programs and efforts toward digital health literacy among healthcare workers can help ameliorate this situation.[41]

• Financial Sustainability: Healthcare expenditure continues to remain low at 3.27% of GDP. Sustainable AI integration requires creative finance approaches, such as outcome-based financing and private investment.

• Policy and Regulation: Effective AI policy requires collaboration between government, regulators, and stakeholders. Clear guidelines on data privacy and ethical use are essential for compliance.

• Capacity Building: Continuous AI and digital literacy training for healthcare workers is vital for effective implementation.

• Public-Private Partnerships: PPPs are crucial to scaling AI interventions, as demonstrated by the success of Ayushman Bharat and other models.

• Community Engagement: Participation from the local community guarantees the inclusion, acceptability, and cultural sensitivity of AI-driven solutions.

• Bridging Healthcare Gaps with AI: AI has the potential to address healthcare disparities in underserved areas through telemedicine, mobile health applications, and cost-effective diagnostic tools. By empowering community health workers with decision-support systems and optimizing resource allocation through data analysis, AI enables scalable and accessible healthcare delivery. Collaborative efforts and investments in digital infrastructure further enhance its reach, making quality healthcare feasible in even the most remote regions.

• Economic Considerations of AI in Healthcare: While AI implementation involves initial costs for trials, infrastructure, and training, it offers significant long-term savings by enabling early diagnosis, optimizing treatments, and improving resource efficiency. AI-driven automation can reduce healthcare system burdens, especially in resource-limited settings. Collaborative efforts between governments, private sectors, and non-profits can further subsidize costs, ensuring equitable and sustainable integration of AI into public health systems [Figure 3].

Figure 3.

Limited AI integration in Indian healthcare

Future Directions, Research Needs, and Strategic Recommendations

There is a pressing need to focus on the overall evaluation of the long-term efficiency and effectiveness of AI technologies for health systems, which will help the government optimize resource allocation. The application of diverse AI algorithms for varied ethnic and cultural segments of the target population will give optimal benefits from the intervention. For a more seamless adoption, it is also essential to guarantee data interoperability, integrate with the current healthcare infrastructure seamlessly, and prepare the staff with focused training. Comprehensive, policy-based approaches dealing with ethical, legal, and social issues should adequately protect patients’ confidentiality and guarantee equal access. Capacity-building activities are crucial in enhancing the digital capacities of health professionals due to the existing gaps. Engaging communities encourages cultural acceptability and uptake, while investing in digital infrastructure and encouraging stakeholder partnerships will assist in establishing robust networks supporting AI-driven healthcare solutions.

Strengths and weaknesses

This review highlights the transformative potential of AI in enhancing public health outcomes, particularly in the context of India’s diverse and resource-constrained healthcare system. By systematically examining a wide range of studies, it provides a comprehensive overview of AI’s applications in diagnostics, disease prediction, and treatment management, demonstrating its ability to improve healthcare delivery, especially in underserved areas. The addition of different AI models from different areas improves the findings’ generalizability. However, there are some limitations too. There is heterogeneity in the nature of the studies, in terms of AI algorithm, study designs, and characteristics of the population, which affects the applicability of the results. In addition, it is difficult to guarantee that the results will hold true when more advanced and sophisticated, newer models are created, owing to the rapid development of AI technology. We acknowledge the potential for publication bias, as studies with lower diagnostic accuracy may be underrepresented in the literature. To minimize this, we included articles from diverse databases to ensure a comprehensive review. Additionally, we recognize the limitation of incorporating heterogeneous studies spanning multiple diseases and technologies. This approach was intentional, as it allowed us to provide a broad understanding of AI’s applications in community-based health amidst the paucity of focused studies. By synthesizing diverse evidence, we aimed to capture the current state of AI implementation and identify overarching trends and challenges in this evolving field.

Conclusion

In the context of addressing the prevailing problems within India’s healthcare system, the use of AI systems poses no great harm; instead, it actively promotes the accuracy of diagnosis, prediction, and treatment of various diseases. It is also evident that AI has the potential to increase responsiveness and accessibility of health care, especially in resource-constrained areas, where timely, accurate management is necessary. However, the deployment of AI faces challenges such as algorithm bias, data privacy issues, and the need for improved infrastructure and trained professionals. A country like India, which aspires to make the most out of AI, should build the required digital infrastructure, have adequate laws/regulatory framework, and conduct sufficient research so that AI in healthcare can have universal benefit to society without any inequality.

Conflicts of interest

There are no conflicts of interest.

Supplementary File Table S1

Summary of all the studies included in the review

Funding Statement

Nil.