Databases are essential for storing and retrieving structured data supporting business intelligence, research, and enterprise applications. Querying databases typically requires SQL, which varies across systems and can be complex. While LLMs offer the potential for automating queries, most approaches rely on translating natural language to SQL, often leading to errors due to syntax differences. A… →
CONCLUSION: CMRL is beneficial to improving residents’ ability of comprehensive analysis and prescribing medical orders as well as residents’ ability of communication skills and patient care in SRT. CMRL may be beneficial to improving the ability of clinical practice. →
CONCLUSION: MHI was comparable to 99mTc in safety and SLN detection rates but differed in sensitivity on positive SLNs. →
BACKGROUND: In the ARIEL4 trial of rucaparib versus standard-of-care chemotherapy in patients with relapsed BRCA-mutated ovarian carcinoma, the primary endpoint was met, showing improved investigator-assessed progression-free survival with rucaparib. Here, we present the final overall survival analysis of the trial and other post-progression outcomes. →
Large language models (LLMs) have revolutionized artificial intelligence by demonstrating remarkable capabilities in text generation and problem-solving. However, a critical limitation persists in their default “fast thinking” approach—generating outputs based on a single query without iterative refinement. While recent “slow thinking” methods like chain-of-thought prompting break problems into smaller steps, they remain constrained by static… →
CONCLUSION: In summary, using data from a recent RCT, TM was found to both improve health outcomes and reduce total costs in this analysis. Based on these results, further effectiveness trials and wider adoption of TM should be considered. →
CONCLUSIONS: Durable guselkumab efficacy was sustained through 5 years of treatment among patient subpopulations irrespective of baseline disease severity or prior treatment history. J Drugs Dermatol. 2025;24(2):196-202. doi:10.36849/JDD.8344. →
In artificial intelligence and machine learning, high-quality datasets play a crucial role in developing accurate and reliable models. However, collecting extensive, verified data—particularly in specialized domains like mathematics, coding, and science—remains a challenge. Traditional data-gathering methods often fail to produce datasets that effectively train models for complex reasoning tasks. This gap highlights the need for… →
Robots are usually unsuitable for altering different tasks and environments. General-purpose models of robots are devised to circumvent this problem. They allow fine-tuning these general-purpose models for a wide scope of robotic tasks. However, it is challenging to maintain the consistency of shared open resources across various platforms. Success in real-world environments is far from… →