Integrating artificial intelligence (AI) into biosensor technology enables data processing, quantitative analysis, real‐time decision‐making, and adaptive sensing capabilities through advanced pattern recognition and predictive modeling. In addition, AI has the potential to drive innovation in the design of advanced materials for biosensing applications by reducing the reliance on trial‐and‐error methods. This review explores the transformative impact of AI on biosensor technology in the context of historical development, current status, and future prospects. It begins with an overview of the evolution of AI, biosensor technology, and their integration. Comparative analysis of AI‐driven innovations in optical, fluorometric, and electrochemical biosensors is presented, highlighting how AI can improve sensor performance. The role of advanced materials on the development of AI‐assisted biosensors is also discussed as the choice of material has a profound effect on biosensor capabilities. Applications of AI‐assisted biosensors are comprehensively explored across healthcare, environmental monitoring, food safety, and agriculture. This study concludes by addressing challenges, opportunities, ethical concerns, and future research directions, providing a comprehensive and up‐to‐date resource for researchers.

We present a chip device with an array of 900 gourd-shaped microwells designed to pair single cells of different types. The device consists of interdigitated array (IDA) electrodes and uses positive dielectrophoresis to trap cells within the microwells. Each side of a microwell is on a different comb of the IDA, so that cells of different types are trapped on opposite sides of the microwells, leading to close cell pairing. Using this device, a large number of cell pairs can be formed easily and rapidly, making it a highly attractive tool for controllable cell pairing in a range of biological applications.

Optimization of nanofiber (NF) surface properties is critical to achieve an adequate cellular response. Here, the impact of conjugation of biomimetic aspartic acid (ASP) and glutamic acid (GLU) templated peptides with poly(lactic-co-glycolic acid) (PLGA) electrospun NF on osteogenic differentiation of human bone marrow-derived mesenchymal stem cells (hMSCs) was evaluated. Cold atmospheric plasma (CAP) was used to functionalize the NF surface and thus to mediate the conjugation. The influence of the CAP treatment following with peptide conjugation to the NF surface was assessed using water contact angle measurements, Fourier-Transform Infrared Spectroscopy (FTIR) and X-ray Photoelectron Spectroscopy (XPS). The effect of CAP treatment on morphology of NF was also checked using Scanning Electron Microscopy (SEM). Both the hydrophilicity of NF and the number of the carboxyl (-COOH) groups on the surface increased with respect to CAP treatment. Results demonstrated that CAP treatment significantly enhanced peptide conjugation on the surface of NF. Osteogenic differentiation results indicated that conjugating of biomimetic ASP templated peptides sharply increased alkaline phosphatase (ALP) activity, calcium content, and expression of key osteogenic markers of collagen type I (Col-I), osteocalcin (OC), and osteopontin (OP) compared to GLU conjugated (GLU-pNF) and CAP treated NF (pNF). It was further depicted that ASP sequences are the major fragments that influence the mineralization and osteogenic differentiation in non-collagenous proteins of bone extracellular matrix.