Browsing by Author "Payal Gupta"

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AI-Enabled Nano Biosensors for Estimating Heavy Metal Contamination in Crops
(Springer, 2025) Nishant Singhal; Harsh Vardhan; Rajul Jain; Payal Gupta; Ashish Gaur; Suresh Kushinath Ghotekar; Deepak Kumar
Heavy metal pollution poses a serious global threat to food safety and sustainable agriculture. Routine evaluation of harmful substances such as cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) in crops is essential to prevent their accumulation in the food supply. Conventional detection techniques, including atomic absorption spectrometry and ICP-MS, are often expensive, labor-intensive, and unsuitable for field applications. In this context, merging artificial intelligence (AI) with nano biosensors introduces an innovative approach for rapid and precise detection. Nano biosensors, which utilize nanomaterials alongside biorecognition elements, provide remarkable sensitivity and specificity for identifying various heavy metals, even at minimal concentrations. When combined with AI and machine learning, these sensors allow for instant data processing, predictive analysis, and spatial mapping of contaminated sites. These real-time observations empower farmers and environmental organizations to make time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Role of artificial intelligence in automating diagnostic procedures in clinical microbiology laboratories
(Elsevier B.V., 2025) Nishant Singhal; Harsh Vardhan; Rajul Jain; Payal Gupta; Aaysha Pandey; Naresh Kumar Wagri; Ashish Gaur
With infectious diseases continuing to pose a significant challenge to global health, clinical laboratories are pursuing faster, more accurate, and more scalable diagnostic options. This article highlights how advancements in robotics, machine learning, deep learning, and natural language processing are revolutionizing traditional laboratory practices. From automated Gram-staining and slide analysis to AI-enabled bacterial identification and antibiotic resistance testing, every technological development enhances diagnostic precision, reduces human error, and speeds up turnaround times. The assessment also deals with the real-world challenges of integrating these technologies, which include ethical issues, data privacy, system compatibility, and user acceptance. Additionally, it examines possible future developments, such as rapid diagnostics, smart laboratory infrastructure, and AI’s capability to create a seamless, interconnected network of diagnostic tools. As laboratories move towards completely automated and intelligent systems, combining human expertise with machine intelligence may enhance microbiological diagnostics’ quality, efficiency, and responsiveness in clinical settings. © 2025 The Author(s).