Automated news summarization using large language models (LLMs) offers great potential to enhance information accessibility. However, critical challenges, such as hallucinations, bias, and toxicity, threaten their reliability and societal acceptance. In this paper, we present NewsLensAI, a novel summarization framework explicitly designed to address these trustworthiness concerns through Named Entity Recognition (NER)-guided prompting. By anchoring summaries in key factual entities extracted from source articles, our method significantly reduces factual inaccuracies without altering model weights or architectures. We evaluated NewsLensAI on a dataset of 1,500 real-world news articles using open-source (LLaMA 3) and proprietary (Gemini 1.5) LLMs. Our analysis encompasses factual consistency, political bias shifts, sentiment preservation, and moderation of toxicity. Our results indicate substantial improvements in factual alignment, demonstrated by an average increase in the BERTScore from 0.80 (baseline) to 0.88 (NER-enhanced), and an approximately 60% reduction in hallucinated entities. To capture contextual terms that are relevant beyond the core entities, we use TF-IDF salience scoring to supplement standard NER categories, particularly for legislative terms and event identifiers. Furthermore, we identify and characterize a notable “centrist drift,” wherein summaries tend to moderate extreme biases present in source articles, along with a measurable reduction in toxic or emotionally charged language. Complementing our empirical findings, we introduce a real-time NewsLensAI demo that summarizes live news feeds from the Guardian API, providing dynamic bias and sentiment analysis. This practical implementation underscores the real-world applicability and potential societal benefit of our approach. Finally, we discuss critical ethical implications, including potential impacts on media literacy and information diversity. Our interdisciplinary approach, linking NLP, journalism, and ethical analysis, positions NewsLensAI as a meaningful step towards safer, fairer, and more trustworthy AI-generated news consumption.