The fight against infection begins with a signal that may be invisible to the naked eye: a subtle colour change, a weak electrical response, or a molecular interaction revealing the presence of a pathogen in a sample. The value of modern healthcare technologies lies in transforming this signal into reliable diagnostic information. In this context, integrating artificial intelligence with biosensors has emerged as a research field that brings together microbiology, engineering, and computer science to accelerate pathogen detection and improve the interpretation of associated signals. Biosensors rely on a biological recognition element that interacts with a specific target and a transducer that converts this interaction into a measurable signal. The target may be a bacterial cell, a distinctive protein, or a sequence of a pathogen’s genetic material. Scientific precision requires distinguishing between biosensors designed to detect microorganisms and microbial biosensors that use microorganisms themselves as sensing elements. Although these technologies overlap in certain applications, they differ in their operating mechanisms and development requirements. Artificial intelligence plays an important role after signal generation. A sensor may produce images, electrical measurements, or time-series data in which the target response overlaps with noise and effects arising from the sample and surrounding environment. Machine Learning and Deep Learning algorithms can learn patterns associated with positive and negative samples and extract features that are difficult to distinguish through direct observation. However, a model’s classification capability remains dependent on the quality of its training data and how well those data represent actual operating conditions. Experimental research has provided practical examples of this integration. The QolorEX platform combines microfluidics, molecular amplification, and colorimetric detection, using a Machine Learning algorithm to analyse the readout and determine the result. Testing on a limited set of human saliva samples demonstrated the possibility of obtaining results within minutes. This experiment illustrates how biological interactions and computational analysis can be connected within an integrated system, although broader validation remains necessary to establish the generalisability of its performance. The significance of this integration extends beyond shortening testing time. It may contribute to developing portable devices that support point-of-care diagnostics and reduce the need to transport certain samples to distant laboratories. Connecting these devices to health information systems could enable networks that aggregate test results and monitor their spatial and temporal variation. Such networks could support early warnings of unusual increases in infection, provided that measurement quality is verified, duplicate results are excluded, and differences in the number of people tested across locations are considered. The field is also relevant to antimicrobial resistance research. However, detecting a pathogen does not, by itself, establish its resistance. Determining resistance requires an appropriate measurement, such as monitoring a microorganism’s response to an antimicrobial agent or detecting specific genetic markers. Accordingly, the system should be designed around the intended diagnostic question, with a clear distinction between identifying a pathogen and determining its treatment-relevant characteristics. The gap between laboratory success and clinical adoption remains a fundamental challenge. An algorithm may perform well on a limited dataset but show reduced performance when applied to different samples or devices. Evaluation therefore requires independent data, comparison with established reference methods, and assessment of sensitivity, specificity, false positives, and false negatives. Using living microorganisms within a biosensor also introduces requirements concerning response stability, biological containment, and the safe disposal of materials. Connecting these systems to networks introduces further responsibilities regarding data protection and the integrity of results. Diagnostic information requires access controls, encryption, and audit trails, because system reliability depends as much on the integrity of data transmission as on measurement accuracy. The scientific value of integrating artificial intelligence with biosensors lies in building a reliable chain that begins with biological recognition and ends with clinically actionable information. Realising this value requires collaboration among specialists in microbiology, intelligent medical systems, data science, and cybersecurity. When grounded in rigorous experimental and clinical validation, this integration can contribute to faster diagnosis, more accurate infection surveillance, and a more evidence-based public health response. Al-Mustaqbal University | Iraq’s Number One University