☸️SAIMSARA Journal
Digital Health: Articles
Issue 3, Volume 1, 2026 in progress

This review shows where AI is already delivering real value in drug discovery: not just faster predictions, but experimentally validated hits, better ADMET screening, large-scale virtual screening, and even early human translation. It maps which AI advances are truly credible, which claims remain fragile, and where the field is genuinely moving from hype to practical therapeutic impact.

Medical machine translation may look fluent, but this review shows where it still breaks: semantic precision, cultural nuance, audience adaptation, and high-stakes clinical reliability. The full read is worth it because it separates the real strengths of MT in constrained tasks from the specific failure modes that still make expert human oversight essential.

This paper shows that the future of scientific publishing will not be defined by AI alone, but by whether publishing can become more transparent, machine-readable, open, and resistant to predatory and low-signal science at the same time. The full read is worth it because it maps where this transition is already happening, where it is failing, and which concrete changes in publishing, peer review, and research evaluation are most likely to matter next.

This paper shows where systematic reviews and scoping reviews truly diverge: one is built to answer narrow, bias-sensitive questions, the other to map broad, uncertain evidence landscapes. Read the full text to see how reporting quality, automation, rapid reviews, overviews, and emerging synthesis methods fit into one practical framework for choosing the right review design.