AI-Native Scoping Reviews & Evidence Mapping

AI cybercrime is no longer just a technical threat but a dual-use arms race: the same AI systems that scale phishing, deepfakes, fraud, and offender automation are also powering detection, forensic triage, and evidence preservation. This full evidence map shows where the strongest signals actually lie — from high-accuracy phishing and fraud detection to blockchain-backed forensics, XAI, governance gaps, and the unresolved question of whether AI-generated evidence can be trusted in real legal workflows.

AI chatbots are becoming more than tools: this evidence map shows how loneliness, perceived empathy, parasocial bonding, and satisfaction can turn conversational support into emotional dependency, over-reliance, and withdrawal-like patterns. The full review separates original evidence from opinion literature and maps where the strongest signals emerge across attachment, social disconnection, Replika-style companion use, student dependency, and design strategies to reduce harm.

EMA approval is not the end of evidence generation, but the start of a lifecycle in which uncertainty must be resolved through post-authorization studies, pharmacovigilance, RWE, and reimbursement reassessment. This evidence map shows where regulatory access, conditional authorization, surrogate endpoints, safety monitoring, and real-world confirmation succeed — and where they still leave clinicians, payers, and patients with critical unanswered questions.

FDA approval is not always the end of uncertainty — this evidence map shows how surrogate endpoints, accelerated pathways, thin pivotal trials, underrepresented populations, and delayed confirmatory evidence can leave major clinical questions unresolved after approval. The full evidence map helps clinicians, researchers, and policy-minded readers distinguish regulatory authorization from proven real-world benefit, safety, equity, and patient-centered value.

AI can already imitate parts of executive work — drafting CEO-style messages, supporting decisions, and simulating crisis responses — but the evidence shows that authority, trust, ethics, and accountability still remain deeply human problems. The full SAIMSARA evidence map reveals where AI may genuinely augment leadership, where it becomes symbolic hype, and why the future CEO is more likely to be human-with-AI than AI-alone.

Roblox, Minecraft, and Fortnite are no longer just games — this evidence map shows how they function as learning spaces, social-development environments, advertising channels, and child-safety risk zones at the same time. Built from 46 references, 32 original studies, and 13,812 participants/sample observations, the full map helps readers separate measurable educational and social benefits from monetization, harmful-content, harassment, and gaming-disorder concerns.

LLM documentation is not simply “safe” or “unsafe”: this evidence map shows where hallucination risk becomes measurable, where it falls below human benchmarks, and which safeguards actually matter—RAG, verification loops, fine-tuning, and human review. Built from 46 references and 32 original studies, it turns scattered clinical AI documentation evidence into a practical safety map for deciding whether, where, and how LLMs should enter real clinical workflows.

ChatGPT vs Claude is not a contest with one winner: the evidence map shows that model performance flips by task, version, modality, and risk level across medicine, education, coding, safety, and research workflows. The full evidence map turns scattered benchmark studies into a practical guide for choosing the right model, identifying where human oversight is essential, and understanding where each system actually performs best.

LinkedIn and Twitter/X are not interchangeable platforms: this evidence map shows where LinkedIn better supports professional identity, recruitment, institutional signaling, and provider-targeted outreach, while Twitter/X more often drives rapid dissemination, public conversation, and broader engagement. The full read turns scattered studies across healthcare, academia, business, government, and computational research into a practical platform-by-platform guide for choosing the right channel, audience, and outcome.