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Human albumin solution utilization patterns prior and during COVID-19 pandemic in United Arab Emirates: Time to develop and implement national guideline on prescribing and utilization Sallam, Mohammed; Snygg, Johan
Narra J Vol. 2 No. 2 (2022): August 2022
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v2i2.82

Abstract

The human albumin solution (HAS) has limited but important indications in clinical practice. However, the inappropriate use of HAS can be costly. Thus, it is imperative to establish a practical protocol to use albumin products and rationalize its usage. The aim of this study was to identify albumin utilization patterns in a multi-specialty private hospital in Dubai, United Arab Emirates (UAE), before and during the COVID-19 epidemic in the country. In addition, the objectives were to demonstrate the importance of reconsidering the prescribing strategies for albumin administration. All data on 20% HAS administration in Mediclinic Welcare Hospital were retrieved during January 2019–May 2021, including the total quantities administered and data on primary diagnosis. A total of 579 patient admissions with several diagnoses were included in the study. The percentage of clinically indicated 20% HAS administration decreased from 13.0% in the pre-COVID-19 phase to 1.5% in the COVID-19 phase (P<0.001). An increase in the administration of 20% HAS not backed by agreed clinical evidence followed the increase in new number of COVID-19 cases in the UAE. The current study showed a large proportion of administered HAS, that drastically increased during COVID-19 with lack of clear evidence of its benefit. This pilot study should be followed by refining of the institutional guidelines of HAS use, frequent audits and interactive educational interventions. In turn, the refinement of HAS administration guidelines can help to reduce the unjustified cost of inappropriate HAS use.
Chinese generative AI models (DeepSeek and Qwen) rival ChatGPT-4 in ophthalmology queries with excellent performance in Arabic and English Sallam, Malik; Alasfoor, Israa M.; W. Khalid, Shahad; Al-Mulla, Rand I.; Al-Farajat, Amwaj; M. Mijwil, Maad; Zahrawi, Reem; Sallam, Mohammed; Egger, Jan; Al-Adwan, Ahmad S.
Narra J Vol. 5 No. 1 (2025): April 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i1.2371

Abstract

The rapid evolution of generative artificial intelligence (genAI) has ushered in a new era of digital medical consultations, with patients turning to AI-driven tools for guidance. The emergence of Chinese-developed genAI models such as DeepSeek-R1 and Qwen-2.5 presented a challenge to the dominance of OpenAI’s ChatGPT. The aim of this study was to benchmark the performance of Chinese genAI models against ChatGPT-4o and to assess disparities in performance across English and Arabic. Following the METRICS checklist for genAI evaluation, Qwen-2.5, DeepSeek-R1, and ChatGPT-4o were assessed for completeness, accuracy, and relevance using the CLEAR tool in common patient ophthalmology queries. In English, Qwen-2.5 demonstrated the highest overall performance (CLEAR score: 4.43±0.28), outperforming both DeepSeek-R1 (4.31±0.43) and ChatGPT-4o (4.14±0.41), with p=0.002. A similar hierarchy emerged in Arabic, with Qwen-2.5 again leading (4.40±0.29), followed by DeepSeek-R1 (4.20±0.49) and ChatGPT-4o (4.14±0.41), with p=0.007. Each tested genAI model exhibited near-identical performance across the two languages, with ChatGPT-4o demonstrating the most balanced linguistic capabilities (p=0.957), while Qwen-2.5 and DeepSeek-R1 showed a marginal superiority for English. An in-depth examination of genAI performance across key CLEAR components revealed that Qwen-2.5 consistently excelled in content completeness, factual accuracy, and relevance in both English and Arabic, setting a new benchmark for genAI in medical inquiries. Despite minor linguistic disparities, all three models exhibited robust multilingual capabilities, challenging the long-held assumption that genAI is inherently biased toward English. These findings highlight the evolving nature of AI-driven medical assistance, with Chinese genAI models being able to rival or even surpass ChatGPT-4o in ophthalmology-related queries.