Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities Artificial intelligence (AI) is increasingly integrated into point-of-care (POC) biosensing, offering rapid, accurate, and accessible health assessments directly at the patient level. This review explores the transformative impact of AI on POC biosensing, highlighting recent computational advancements, ongoing challenges, and future prospects. It provides an overview of core biosensing technologies and their use in POC settings, emphasizing ongoing issues that AI can address. The review discusses AI methodologies, including machine learning algorithms, neural networks, and data processing frameworks, which facilitate real-time analytical decision-making. It explores AI applications at each stage of biosensor development, highlighting opportunities beyond simple data analysis. The review also analyzes outstanding challenges in AI-assisted biosensing, focusing on technical and ethical issues such as data security, algorithmic bias, and regulatory compliance. The integration of AI into biosensing can significantly improve the performance and reliability of POC diagnostics, bringing them closer to laboratory standards. AI has the potential to overcome challenges in POC biosensing by accelerating biomarker discovery, enhancing data processing, improving device accuracy, and simplifying operational complexities. In biosensing, AI can be applied at each stage of the biosensor development process, assisting in analyte selection, recognition element development, signal transduction enhancement, and data analysis. AI can enhance the selection of relevant biomarkers by leveraging omics data, identifying novel biomarkers, and enabling combinatorial multi-analyte analysis. AI also plays a crucial role in the discovery of recognition elements, including the top-down and bottom-up approaches for identifying and designing biorecognition elements. AI can improve the design of new materials with enhanced biosensing properties, miniaturize existing sensing instrumentation, and develop biosensing methods that transcend traditional biomolecular interactions. At the data-handling level, AI can generate clean, detailed data for robust biomolecular analysis, improve data acquisition, analysis, and interpretation. AI-driven adaptive sampling techniques can determine optimal sampling times and conditions to maximize information content and minimize resource use. Machine learning algorithms can process and analyze vast amounts of data generated by biosensors, identify patterns, and classify data that might be missed by traditional methods. AI technologies can significantly enhance data interpretation by analyzing complex time-series data to predict future measurements or detect anomalous events based on historical data. This review aims to highlight the many avenues through which AI can benefit the biosensing community at every stage of the biosensor development process.Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities Artificial intelligence (AI) is increasingly integrated into point-of-care (POC) biosensing, offering rapid, accurate, and accessible health assessments directly at the patient level. This review explores the transformative impact of AI on POC biosensing, highlighting recent computational advancements, ongoing challenges, and future prospects. It provides an overview of core biosensing technologies and their use in POC settings, emphasizing ongoing issues that AI can address. The review discusses AI methodologies, including machine learning algorithms, neural networks, and data processing frameworks, which facilitate real-time analytical decision-making. It explores AI applications at each stage of biosensor development, highlighting opportunities beyond simple data analysis. The review also analyzes outstanding challenges in AI-assisted biosensing, focusing on technical and ethical issues such as data security, algorithmic bias, and regulatory compliance. The integration of AI into biosensing can significantly improve the performance and reliability of POC diagnostics, bringing them closer to laboratory standards. AI has the potential to overcome challenges in POC biosensing by accelerating biomarker discovery, enhancing data processing, improving device accuracy, and simplifying operational complexities. In biosensing, AI can be applied at each stage of the biosensor development process, assisting in analyte selection, recognition element development, signal transduction enhancement, and data analysis. AI can enhance the selection of relevant biomarkers by leveraging omics data, identifying novel biomarkers, and enabling combinatorial multi-analyte analysis. AI also plays a crucial role in the discovery of recognition elements, including the top-down and bottom-up approaches for identifying and designing biorecognition elements. AI can improve the design of new materials with enhanced biosensing properties, miniaturize existing sensing instrumentation, and develop biosensing methods that transcend traditional biomolecular interactions. At the data-handling level, AI can generate clean, detailed data for robust biomolecular analysis, improve data acquisition, analysis, and interpretation. AI-driven adaptive sampling techniques can determine optimal sampling times and conditions to maximize information content and minimize resource use. Machine learning algorithms can process and analyze vast amounts of data generated by biosensors, identify patterns, and classify data that might be missed by traditional methods. AI technologies can significantly enhance data interpretation by analyzing complex time-series data to predict future measurements or detect anomalous events based on historical data. This review aims to highlight the many avenues through which AI can benefit the biosensing community at every stage of the biosensor development process.