Bromocriptine (BCR) presents poor bioavailability when administered orally because of its low solubility and prolonged first-pass metabolism. This poses a significant challenge in its utilization as an effective treatment for managing Parkinson’s disease (PD). The utilization of lipid nanoparticles can be a promising approach to overcome the limitations of BCR bioavailability. The aim of the research work was to develop and evaluate bromocriptine-loaded solid lipid nanoparticles (BCR-SLN) and bromocriptine-loaded nanostructured lipid carriers (BCR-NLC) employing the Box-Behnken design (BBD). BCR-SLNs and BCR-NLCs were developed using the high-pressure homogenization method. The prepared nanoparticles were characterized for particle size (PS), polydispersity index (PDI), and entrapment efficiency (EE). In vitro drug release, cytotoxicity studies, in vivo plasma pharmacokinetic, and brain distribution studies evaluated the optimized lipid nanoparticles. The optimized BCR-SLN had a PS of 219.21 ± 1.3 nm, PDI of 0.22 ± 0.02, and EE of 72.2 ± 0.5. The PS, PDI, and EE of optimized BCR-NLC formulation were found to be 182.87 ± 2.2, 0.16 ± 0.004, and 83.57 ± 1.8, respectively. The in vitro release profile of BCR-SLN and BCR-NLC showed a biphasic pattern, immediate release, and then trailed due to the sustained release. Furthermore, a pharmacokinetic study indicated that both the optimized BCR-SLN and BCR-NLC formulations improve the plasma and brain bioavailability of the drug compared to the BCR solution. Based on the research findings, it can be concluded that the BCR-loaded lipid nanoparticles could be a promising carrier by enhancing the BBB penetration of the drug and helping in the improvement of the bioavailability and therapeutic efficacy of BCR in the management of PD.

Background: Artificial Intelligence as a Medical Device (AIaMD) introduces regulatory, methodological, ethical, and clinical challenges that are not fully addressed by traditional device trial frameworks. Given rapidly evolving and jurisdiction-specific guidance, a consolidated mapping of trial design expectations and regulatory requirements is needed. Objective: To map regulatory requirements and clinical trial design approaches for AIaMD across major jurisdictions and to identify key methodological and implementation gaps relevant to adaptive/continuously learning systems. Methods: A scoping review was conducted in accordance with the PRISMA-ScR reporting guideline. Peer-reviewed literature (2017–2025) was searched in PubMed, Embase, Web of Science, and the Cochrane Library. Gray literature was identified from major regulators and policy bodies (FDA, EMA, MHRA, PMDA, WHO, CDSCO). Eligible records addressed AIaMD clinical evaluation, trial design, regulatory pathways, post-market surveillance, or reporting standards. Data were charted using a predefined extraction framework and synthesized descriptively with thematic analysis across regulatory, methodological, ethical, and clinical implementation domains. Results: Included sources demonstrate substantial heterogeneity in evidence expectations and AI-specific pathways across jurisdictions. Recurrent themes include the need for predefined change management, performance monitoring and drift controls, dataset representativeness and bias evaluation, transparency and versioning, cybersecurity, and real-world evidence integration. Reporting frameworks (SPIRIT-AI, CONSORT-AI, MI-CLAIM) are frequently cited as mechanisms to improve reproducibility and regulatory readiness. Conclusions: Evidence and regulatory expectations for AIaMD remain fragmented. Harmonization of terminology, trial design principles, and post-market governance—supported by standardized reporting—would improve clinical validity, safety assurance, and scalability across regions. This review has several limitations. As a scoping synthesis, it prioritizes breadth of coverage rather than quantitative meta-analysis. Included sources vary in methodological rigor and reporting detail, and evolving regulatory guidance may change rapidly over time. Nevertheless, integrating peer-reviewed and regulatory evidence provides a comprehensive overview of current expectations and emerging gaps. In conclusion, effective evaluation of AIaMD requires a shift from static, one-time validation toward continuous lifecycle oversight that integrates adaptive trial designs, transparent reporting standards, bias surveillance, and structured post-market monitoring. Regulatory heterogeneity currently poses significant barriers to multinational development; however, coordinated adoption of standardized evidence frameworks and collaborative governance mechanisms may reduce duplication while preserving patient safety. By translating methodological principles into operational guidance, this review aims to support regulators, sponsors, and clinical investigators in designing trials that are both scientifically rigorous and practically implementable for continuously learning systems.