Antimicrobial resistance (AMR) poses a formidable threat to public health in India, requiring a multidimensional response that bridges policy, innovation, and intersectoral collaborations. The rapid emergence and spread of multidrug-resistant pathogens driven by the misuse of antimicrobials in human health, animal health, agriculture, and environmental settings has rendered many existing therapies obsolete, leaving the population vulnerable to untreatable infections. To examine the multifactorial drivers of AMR in India, review current national and state-level policies, and explore the potential role of Artificial Intelligence (AI) in AMR surveillance, prevention, and control. This review synthesizes evidence from published literature, reports, and policy documents. It analyses AMR determinants across human, animal, and environmental sectors, evaluates policy frameworks such as India's National Action Plan, and State Action Plans to combat AMR. Key AMR drivers include antibiotic misuse, inadequate regulation, over-the-counter availability, pharmaceutical and hospital wastewater contamination, and gaps in infection prevention. India has come up with national and six state level action plans for AMR containment. The measures include stewardship programs, laboratory network strengthening, spreading awareness and intersectoral coordination. The role of AI in strengthening AMR surveillance and clinical decision-making by integrating complex, high-dimensional data for predictive modeling has been explored. While India has made significant policy and surveillance advances, enforcement gaps, limited awareness, and fragmented data hinder progress. Strengthening governance, expanding One Health surveillance, integrating AI, and embedding AMR strategies into universal health coverage are critical to mitigating AMR's health and economic burden.

Background Noncommunicable diseases (NCDs) are one of the leading causes of morbidity and mortality in India. This study aimed to ascertain the individual, household, community, and health systems level factors influencing the demand of public healthcare network for primary management of diabetes and hypertension in a city in South India. Methods A mixed-methods cross-sectional study was conducted in Mysuru City, Karnataka. For quantitative inquiry, three-stage sampling strategies were followed and interviewed 6007 households comprising 2078/11978 (17.3%) individuals aged 30 and above with known diabetes or hypertension using structured questionnaires and different hierarchical levels of the workforce at 23 urban public health facilities. Andersen’s Behavioural Model of Healthcare Utilization, Multivariate logistic regression and Erreyger’s concentration indices were employed to unravel the socio-economic-demographic and contextual determinants driving the utilization of either public or private healthcare facilities for primary care of Diabetes and Hypertension. For Qualitative inquiry, we used multifarious techniques, such as a) four Key Informant Interviews (KII) conducted with policymakers at the state and city level with the help of semi-structured interview guides, b) 12 focus group discussions (FGDs) conducted with a homogenous group of respondents (men and women with diabetes and hypertension, frontline health workers and c) 36 In-depth Interviews (IDIs) d) 04 Public engagement workshops. Results The prevalence of diabetes and hypertension was greater in females (52.9% and 57.4%, respectively). More than two-thirds of surveyed population (68.4% and 64.6%) sought care for diabetes and hypertension from heterogeneous private providers. Socio-structural factors such as the level of education, occupation status, religion and caste, and individual characteristics such as living standards and insurance coverage (inverse and direct relationship, respectively) and facilities having high-technical and process quality scores and absence of multimorbidity impacted the likelihood of utilization of the public health system for management of diabetes and hypertension care. Erreyger's Indices established the inequality in access to public health facilities for treatments for both diabetes and hypertension among the poor. Conclusion The findings revealed a pervasive gap in the utilization of public health facilities for management of NCDs, by the urban poor despite the financial burden to seek care from private providers. Various demand and supply side recommendations can be propounded to augment the coverage and quality of public facilities for NCD management.

Antimicrobial resistance (AMR) poses one of the significant challenges for the progress of modern medicine not only by its myriad manifestations on a microbiological plane but also due to its demand for a complex, multisectoral approach for its annulment. While some nations have been actively involved in the deployment of counter measures against AMR, the same have yet to be implemented in India. This can be attributed due to a combination of its epitomizing multivariate nature and the challenges encountered on a ground-level in policy implementation. This review surveys mechanisms and factors promoting incidence of AMR, potential Artificial Intelligence (AI) implementation alongside evaluating the efficacy of the One Health approach on national and state levels in curbing the rise of AMR.