The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI).

The application of machine learning and artificial intelligence to clinical settings for prevention, diagnosis, treatment, and the improvement of clinical care have been demonstrably cost-effective. However, current clinical AI (cAI) support tools are predominantly created by non-domain experts and algorithms available in the market have been criticized for the lack of transparency behind their creation. To combat these challenges, the Massachusetts Institute of Technology Critical Data (MIT-CD) consortium, an affiliation of research labs, organizations, and individuals that contribute to research in and around data that has a critical impact on human health, has iteratively developed the "Ecosystem as a Service (EaaS)" approach, providing a transparent education and accountability platform for clinical and technical experts to collaborate and advance cAI. The EaaS approach provides a range of resources, from open-source databases and specialized human resources to networking and collaborative opportunities. While mass deployment of the ecosystem still faces several hurdles, here we discuss our initial implementation efforts. We hope this will promote further exploration and expansion of the EaaS approach, while also informing or realizing policies that will accelerate multinational, multidisciplinary, and multisectoral collaborations in cAI research and development, and provide localized clinical best practices for equitable healthcare access

The advent of medical artificial intelligence: lessons from the Japanese approach

Artificial intelligence or AI has been heralded as the most transformative technology in healthcare, including critical care medicine. Globally, healthcare specialists and health ministries are being pressured to create and implement a roadmap to incorporate applications of AI into care delivery. To date, the majority of Japan’s approach to AI has been anchored in industry, and the challenges that have occurred therein offer important lessons for nations developing new AI strategies. Notably, the demand for an AI-literate workforce has outpaced training programs and knowledge. This is particularly observable within medicine, where clinicians may be unfamiliar with the technology. National policy and private sector involvement have shown promise in developing both workforce and AI applications in healthcare. In combination with Japan’s unique national healthcare system and aggregable healthcare and socioeconomic data, Japan has a rich opportunity to lead in the field of medical AI

The impact of COVID-19 on flight networks

As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections. Such restrictions have a direct effect on the volume of international flights among these countries, resulting in extensive social and economic costs. To better understand the situation in a quantitative manner, we analyzed the OpenSky Network data to clarify flight patterns and flight densities around the world. Then we observed relationships between flight numbers with new infection cases and the economy (the unemployment rate) in Barcelona. We found that the number of daily flights gradually decreased and then suddenly dropped 64% during the second half of March in 2020 after the United States and Europe enacted travel restrictions. We also observed a 51% decrease in the global flight network density decreased during this period. Regarding new COVID-19 cases, the United States had an unexpected surge regardless of travel restrictions. Finally, the layoffs for temporary workers in the tourism and airplane business increased by 4.3 fold in the weeks following Spain’s decision to close its borders.Peer ReviewedPostprint (published version

Proportion of HBV-infected pregnant women eligible for antiviral prophylaxis to prevent vertical transmission: a systematic review and meta-analysis

International audienceBackground & AimsIn 2020, WHO recommended peripartum antiviral prophylaxis (PAP) for HBV-infected pregnant women with high viremia (≥200,000 IU/mL). Hepatitis B e antigen (HBeAg) was also recommended as an alternative where HBV DNA is unavailable. To inform policy-making and implementation of prevention of mother-to-child transmission, we conducted a systematic review and meta-analysis to estimate proportion of HBV-infected pregnant women eligible for PAP at global and regional levels.MethodsWe searched PubMed/EMBASE/Scopus/CENTRAL for studies of HBV-infected pregnant women. We extracted proportions of women with high viremia (≥200,000 IU/mL), positive HBeAg, cross-stratified proportions based on HBV DNA and HBeAg, and the child infection risk. Proportions were pooled using random-effects meta-analysis.ResultsOf 6,999 articles, 131 studies involving 71,712 HBV-infected pregnant women were included. The number of studies per WHO region was 66 (Western Pacific), 21 (Europe), 17 (Africa), 11 (Americas), 9 (Eastern Mediterranean), and 7 (South-East Asia). The overall pooled proportion of high viremia was 21.27% (95% CI 17.77-25.23), with significant regional variation: Western Pacific (31.56%), Americas (23.06%), South-East Asia (15.62%), Africa (12.45%), Europe (9.98%) and Eastern Mediterranean (7.81%). HBeAg positivity showed similar regional variation. After cross-stratification, the proportions of high viremia and positive HBeAg, high viremia and negative HBeAg, low viremia and positive HBeAg, and low viremia and negative HBeAg were 14.80% (10.75-20.05), 2.62% (1.81-3.78), 3.66% (2.83-4.73), and 76.18% (69.79-81.58%), respectively. The corresponding risks of child infection following birth dose vaccination without immune globulin and PAP were 14.86% (8.43-24.88), 6.94% (2.92-15.62), 7.14% (1.00-37.03), and 0.14% (0.02-1.00).ConclusionsApproximately 20% of HBV-infected pregnant are eligible for PAP. Given significant regional variations, each country should tailor strategies for HBsAg screening, risk stratification, and PAP in routine antenatal care