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The integration of deep learning (DL) algorithms offers significant potential to enhance healthcare applications. Specifically, DL has advanced medical imaging diagnostics, therapeutic interventions, and disease management. However, deploying deep learning algorithms on sensitive medical imagery introduces substantial challenges regarding data security and patient privacy. Optimizing medical imaging performance while simultaneously ensuring patient anonymity remains a complex task. Consequently, privacy-preserving techniques designed for deep learning model training and inference are increasingly gaining traction. Currently, these medical image sequences are interpreted using state-of-the-art computer-aided detection/diagnosis (CAD) methodologies. Yet, frameworks that require uploading medical photographs to external servers inherently create privacy vulnerabilities.To address this, this article introduces a convolutional Bi-LSTM network designed to evaluate fully homomorphic-encrypted (HE) time-series medical images. Within this framework, convolutional blocks extract selective spatial features from the secure image sequences, while analytical sequence layers based on Bi-LSTM encode the temporal data. To optimize efficiency and minimize misdiagnoses, a weighted unit and sequence voting layer incorporates spatial data using varying weights. The effectiveness of this framework is demonstrated using two rigorous public benchmarks: the CheXpert and BreaKHis datasets. The proposed technique surpasses several competing methods, achieving an accuracy exceeding 0.99 across both datasets. These findings indicate that the introduced methodology can successfully extract visual representations and sequential dynamics from encrypted medical image series, thereby safeguarding patient privacy while maintaining high performance in medical image analysis.

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.

Federated learning enables multiple participants to develop shared models without routinely transferring their raw data to a central repository, yet data localization alone does not resolve the trustworthiness challenges of distributed artificial intelligence. Explainability can support understanding, oversight, auditability, and accountability, but explanations may also expose sensitive attributes, local data characteristics, model information, organizational knowledge, provenance traces, or security-relevant evidence. This article proposes a conceptual federated explainable AI framework for integrating privacy-preserving learning, explanation sharing, confidentiality protection, leakage-risk assessment, and cross-organizational accountability. The framework begins by defining the federation context, participants, responsibilities, data boundaries, and sovereignty conditions. It then structures privacy–explainability tension assessment, local and global explanation requirements, explanation granularity and abstraction, access control, redaction, filtering, provenance, model-update review, threat modelling, conditional privacy safeguards, auditability, incident response, and governance updating. The analysis distinguishes local, global, personalized, organization-specific, and cross-organizational explanations while emphasizing that each may produce different disclosure and accountability risks. It also identifies confidentiality threats arising from model updates, gradients, feature attributions, explanation aggregation, membership inference, reconstruction, and proprietary-information leakage. The central contribution is an explanation-boundary approach that asks what may be explained, to whom, for what purpose, at what level of detail, under which safeguards, and with what residual-risk communication. The framework may support responsible design and evaluation, but it does not independently establish privacy, security, confidentiality, legal compliance, ethical sufficiency, accountability, certification, or deployment readiness.