Search

The medical industry in India has undergone substantial evolution driven by progress in technology and infrastructure. Yet, prominent hurdles remain when addressing critical problems like inadequate medical infrastructure for the nation's massive population, restricted accessibility, a deficit of qualified practitioners, and high-quality services. Artificial intelligence (AI)-based interventions offer the possibility to ease the burden on the Indian medical network; nevertheless, incorporating reliable AI into this domain is still obstructed by moral and legal barriers. The objective of this research is to critically analyze the contemporary state of AI system engineering in Indian medical care and its alignment with the moral and legal dimensions of AI, while also pinpointing the obstacles and prospects for implementing reliable AI within the Indian medical domain. This investigation assessed 15 papers chosen from an initial pool of 1136 papers collected across two digital databases, PubMed and Google Scholar, alongside project sites. This inquiry utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). The findings indicate that current publications primarily deployed traditional machine learning (ML) models and artificial neural networks (ANNs) for diverse operations, including drug discovery, disease monitoring networks, early sickness identification and diagnostic precision, and medical resource administration in India. This analysis uncovers a deficiency in the integration of reliable AI within Indian medical care and various obstructions linked to it. It examines pathways for engineering reliable AI within Indian medical environments, prioritizing patient protection, information confidentiality, and adherence to moral and statutory criteria.

Artificial intelligence increasingly informs consequential decisions, yet high predictive performance alone does not establish that an intelligent system is worthy of trust. This systematic review examines trustworthy AI as a multidimensional, socio-technical, and lifecycle-dependent research domain. It synthesises selected peer-reviewed journal evidence addressing transparency, interpretability, explainability, robustness, safety, privacy, security, fairness, accountability, governance, and meaningful human oversight. The review applies predefined questions, eligibility principles, trustworthiness-dimension mapping, claim-level evidence extraction, and cross-dimensional synthesis without undertaking meta-analysis or inventing quantitative screening results. The evidence indicates that transparency and explanation must be assessed in relation to fidelity, stability, completeness, usefulness, stakeholder needs, contestability, recourse, and the practical authority of human overseers. Robustness and safety require evaluation under adversarial conditions, uncertainty, context shift, system failure, and residual risk, while privacy-preserving approaches must still be examined for leakage, cybersecurity vulnerabilities, and governance limitations. Fairness depends on context-sensitive definitions, bias identification, distributional consequences, documentation, responsibility allocation, auditability, and institutional mechanisms for correction. Cross-dimensional analysis reveals persistent tensions between transparency and privacy, explainability and security, automation and human control, and performance and fairness. Important gaps remain in multidimensional evaluation, realistic human-oversight studies, explanation-usefulness validation, lifecycle monitoring, accountability operationalisation, stakeholder participation, and foundation-model assurance. The review contributes an integrated evidence synthesis showing that AI worthiness of trust should be supported through coordinated technical validation, human-centred assessment, institutional governance, and continuing monitoring rather than inferred from any isolated property.

Intelligent systems increasingly influence decisions, allocate attention, generate recommendations, and mediate interactions across technical, organizational, and social environments. Yet trustworthiness is often approached through isolated interventions, such as improving model accuracy, adding post-hoc explanations, documenting datasets, or assigning nominal human oversight. This article proposes a trustworthy-by-design conceptual framework that treats trustworthiness as a system-level property requiring coordinated design and evaluation across the full intelligent-system lifecycle. The framework begins with system-purpose and risk-context definition, followed by stakeholder and responsibility mapping, data-integrity assessment, provenance and lineage review, data documentation and governance, model-reliability evaluation, robustness and uncertainty assessment, and bias and fairness review where relevant. It then connects explanation quality with human interpretability and decision usefulness, rather than assuming that technically generated explanations will necessarily support understanding. Human oversight, meaningful control, contestability, recourse, institutional accountability, traceability, auditability, monitoring, and feedback updating form additional interdependent layers. The framework’s principal contribution is to clarify how technical evidence must be connected to human decision conditions and institutional responsibility without treating any individual safeguard as sufficient. Data provenance does not establish data appropriateness, robustness does not prove safety, explanations do not guarantee interpretability, human presence does not ensure meaningful control, and governance arrangements do not independently establish accountability. The framework may support conceptual analysis, assurance-oriented evaluation, and lifecycle governance planning, but requires empirical validation, human-centered assessment, domain-specific interpretation, and continuing institutional review.