Recommendation systems are ubiquitous, from Spotify playlist suggestions to Amazon product suggestions. Nevertheless, depending on the methodology or the dataset, these systems typically fail to capture user preferences and generate general recommendations. Recent advancements in Large Language Models (LLM) offer promising results for analyzing user queries. However, employing these models to capture user preferences and efficiency remains an open question. In this paper, we propose LLMRS, an LLM-based zero-shot recommender system where we employ pre-trained LLM to encode user reviews into a review score and generate user-tailored recommendations. We experimented with LLMRS on a real-world dataset, the Amazon product reviews, for software purchase use cases. The results show that LLMRS outperforms the ranking-based baseline model while successfully capturing meaningful information from product reviews, thereby providing more reliable recommendations.

Scopio Labs, a leading provider of Full Field Morphology (FFM), announced that it was granted FDA clearance to market and sell its X100 with Full Field Peripheral Blood Smear (Full Field PBS) Application, unlocking the potential of in vitro hematology diagnosis. Full Field PBS is also available in Europe with CE mark certification granted earlier this year. Blood is one of the most foundational gateways to health information. Even with the adoption of digital tools, today's solutions do not showcase all required regions of interest in a PBS slide, only capturing snapshots of cells. To help improve diagnostic accuracy leveraging novel computer vision tools, Full Field PBS gives clinical laboratories an unprecedented ability to capture digital scans using advanced computational photography imaging and tailored AI tools.

Artificial Intelligence has proved its usefulness in this time of crisis. The technology is one of the greatest soldiers the world could ever get in the fight against coronavirus. AI along with its subsets is leveraging significant innovation across the healthcare sector and others as well to win against the pandemic. Where on the one hand computer vision is helping officials and government authorities to identify people who are not wearing a mask which is extremely critical in today's time, Natural Language Processing (NLP) powers chat programs to help detect the outbreak in the initial stages. NLP, moreover, is driving monitoring efforts in order to minimize the virus spread.