AI transformed healthcare communication. Conversational AI utilizes ChatGPT and other large language models more frequently. This technology finds personalized health data, expanding options. Intelligent technologies help people understand their health by teaching medical ideas and giving interactive support. AI improves patient care and workflow. AI improves patient-provider communication, raising legal, ethical, and regulatory issues. The research addresses these issues. This investigation identified studies that used PRISMA-compliant systematic reviews. A search of Google Scholar, Taylor & Francis, Elsevier, PubMed, and others yielded 50 hits. Native studies enable academics to evaluate AI in health communication as a whole. The studies identified issues related to ethical concerns, data privacy, and the preparation of healthcare professionals. The paper provides a health communication AI conceptual framework for planned, moral progress. AI must be invented,used, and applied equitably, responsibly, transparently, and ethically to maximize its revolutionary promise while respecting patient rights and promoting equitable public health.

This study explores public trust in Grok, an AI fact-checking tool on X (Twitter), during the 2025 Pakistan-India war. Using Media Dependency Theory, it examines how Grok shaped trust, fact-checking behavior, and perceptions of journalistic authority. A mixed-methods survey of 380 respondents measured trust, dependency, and views on AI’s role in crisis reporting. Findings show moderately high trust in Grok, moderate trust in journalists, and a perception that Grok was more reliable during the conflict. However, most participants favored human-AI collaboration over replacing reporters. Dependency on Grok was moderate and positively linked to trust. While respondents feared misinformation, they still valued AI for verification. Overall, the study suggests AI will not replace journalism but is becoming a parallel authority, influencing how audiences navigate uncertainty in conflict situations.

Regulators and public interest activists increasingly advocate for full transparency in customer-facing AI. However, an empirical meta-synthesis of 17 studies (N=14,820) reveals a critical transparency dilemma: uncontextualized raw AI disclosures trigger negative persuasion knowledge, reducing consumer trust by 1.12 to 1.48 points on a 7-point scale. This trust penalty erodes brand trust, perceived competence, and institutional legitimacy. Critically, this decline stems from unframed transparency rather than algorithm aversion itself. Combining AI disclosures with explicit Human-in-the-Loop (HITL) cues protects consumer trust and restores brand authenticity (M=5.56 and M=5.68). A cross-sectoral analysis across banking, healthcare, retail, aviation, and technology highlights widespread governance gaps, with 82% of top-performing firms asserting AI use without addressing AI governance. Based on a structural equation modeling path model (R2=0.58), the study provides a business communication framework with actionable guidance for practitioners and regulators.