The novel coronavirus outbreak began in late December 2019 and rapidly spread worldwide, critically impacting public health systems. A number of already approved and marketed drugs are being tested for repurposing, including Favipiravir. We aim to investigate the efficacy and safety of Favipiravir in treatment of COVID-19 patients through a systematic review and meta-analysis. This systematic review and meta-analysis were reported in accordance with the PRISMA statement. We registered the protocol in the PROSPERO (CRD42020180032). All clinical trials which addressed the safety and efficacy of Favipiravir in comparison to other control groups for treatment of patients with confirmed infection with SARS-CoV2 were included. We searched electronic databases including LitCovid/PubMed, Scopus, Web of Sciences, Cochrane, and Scientific Information Database up to 31 December 2020. We assessed the risk of bias of the included studies using Cochrane Collaboration criteria. All analyses were performed using the Comprehensive Meta-Analysis software version 2, and the risk ratio index was calculated. Egger and Begg test was used for assessing publication bias. Nine studies were included in our meta-analysis. The results of the meta-analysis revealed a significant clinical improvement in the Favipiravir group versus the control group during seven days after hospitalization (RR = 1.24, 95% CI: 1.09–1.41; P = 0.001). Viral clearance was more in 14 days after hospitalization in Favipiravir group than control group, but this finding marginally not significant (RR = 1.11, 95% CI: 0.98–1.25; P = 0.094). Requiring supplemental oxygen therapy in the Favipiravir group was 7% less than the control group, (RR = 0.93, 95% CI: 0.67–1.28; P = 0.664). Transferred to ICU and adverse events were not statistically different between two groups. The mortality rate in the Favipiravir group was approximately 30% less than the control group, but this finding not statistically significant. Favipiravir possibly exerted no significant beneficial effect in the term of mortality in the general group of patients with mild to moderate COVID-19. We should consider that perhaps the use of antiviral once the patient has symptoms is too late and this would explain their low efficacy in the clinical setting.

The novel coronavirus outbreak began in late December 2019 and rapidly spread worldwide, critically impacting public health systems. A number of already approved and marketed drugs are being tested for repurposing, including Favipiravir. We aim to investigate the efficacy and safety of Favipiravir in treatment of COVID-19 patients through a systematic review and meta-analysis. This systematic review and meta-analysis were reported in accordance with the PRISMA statement. We registered the protocol in the PROSPERO (CRD42020180032). All clinical trials which addressed the safety and efficacy of Favipiravir in comparison to other control groups for treatment of patients with confirmed infection with SARS-CoV2 were included. We searched electronic databases including LitCovid hub/PubMed, Scopus, ISI web of Sciences, Cochrane, and Scientific Information Database up to 31 December 2020. We assessed the risk of bias of the included studies using Cochrane Collaboration criteria. All analyses were performed using the Comprehensive Meta-Analysis software version 2, and the risk ratio index was calculated. Egger and Begg test was used for assessing publication bias. Nine studies were included in our meta-analysis. The results of the meta-analysis revealed a significant clinical improvement in the Favipiravir group versus the control group during seven days after hospitalization (RR=1.24, 95% CI: 1.09-1.41; P=0.001). Viral clearance was more in 14 days after hospitalization in Favipiravir group than control group, but this finding marginally not significant (RR=1.11, 95% CI: 0.98-1.25; P=0.094). Requiring supplemental oxygen therapy in the Favipiravir group was 7% less than the control group, (RR=0.93, 95% CI: 0.67-1.28; P=0.664). Transferred to ICU and adverse events were not statistically different between two groups. The mortality rate in the Favipiravir group was approximately 30% less than the control group, but this finding not statistically significant. Favipiravir possibly exerted no significant beneficial effect in the term of mortality in the general group of patients with mild to moderate COVID-19. We should consider that perhaps the use of antiviral once the patient has symptoms is too late and this would explain their low efficacy in the clinical setting.

Background: Artificial intelligence is increasingly influencing health care and policy, yet the global research landscape linking artificial intelligence and health care policy remains underexplored. This study aimed to map publication trends, major contributors, collaboration networks, citation structures, and emerging themes in this field. Methods: A bibliometric analysis was conducted using the Web of Science Core Collection. The search was performed on 3 May 2026, and covered publications from 2000 to 3 May 2026. The final dataset included 347 peer-reviewed English-language original research and review articles. Biblioshiny, VOSviewer, and CiteSpace were used to analyze publication trends, country and institutional contributions, author and journal productivity, collaboration networks, citation and co-citation structures, keyword patterns, and thematic evolution. Results: Publications increased markedly after 2020 and reached their highest annual output in 2025. The 2026 publication count was lower because data for that year were partial at the time of database retrieval. Researchers from 82 countries and 900 institutions contributed to the field, with the United States leading in output, followed by China, England, Canada, and India. Harvard Medical School was the most productive institution, whereas Harvard University had the highest institutional centrality. Frontiers in Public Health published the most articles, and PLOS ONE was the most frequently co-cited journal. The most cited article was “Artificial intelligence and the future of global health.” Key research themes included machine learning, COVID-19, health policy, risk, large language models, interpretable machine learning, neural network-assisted screening, socioeconomic perspectives, and public health applications. Conclusions: Research on artificial intelligence and health care policy has expanded rapidly, particularly in recent years, and is increasingly centered on predictive modeling, public health decision-making, and emerging artificial intelligence technologies. These findings highlight influential contributors, evolving themes, and future directions for researchers, policymakers, and health care leaders.