Wound healing is a complex tissue regeneration process involving many changes in multiple physiological parameters. The pH and temperature of a wound site have long been recognized as important biomarkers for assessing wound healing status. For effective wound management, wound dressings integrated with wearable sensors and systems used for continuous monitoring of pH and temperature have received much attention in recent years. Herein, recent advances in the development of wearable pH and temperature sensors and systems based on different sensing mechanisms for wound status monitoring and treatment are comprehensively summarized. Challenges in the areas of sensing performance, infection identification threshold, large-area 3-dimensional detection, and long-term reliable monitoring in current wearable sensors/systems and emerging solutions are emphasized, providing critical insights into the development of wearable sensors and systems for wound healing monitoring and management.

The aging population and the prevalence of infectious diseases have impacted the traditional medical order, significantly increasing the burden on healthcare and adversely affecting the socio‐economic system. Medical sensors based on artificial intelligence (AI) provide new ideas for modern medical data collection to monitor the health status of individuals and environmental changes. Meanwhile, with the aid of AI algorithms, the big data processing capabilities of sensor systems have been greatly improved, further realizing early predictions and timely diagnoses. In this paper, a brief overview is offered on the development status of AI‐enabled medical sensors for off‐body detection, near‐body monitoring, disease prediction, and clinical decision support system, and the ongoing challenges and future prospects to move from concept to implementation are discussed. In the foreseeable future, breakthroughs in the combination of medical sensors and AI algorithms are expected to pave the way for early detection and clinical decision support and improve the accuracy and efficiency for disease diagnosis.

Volatile organic compounds (VOCs) sensor arrays have garnered considerable attention due to their potential to provide real‐time information for monitoring pollution levels and personal health associated concerning VOCs in the ambient environment. Here, an AI‐driven wearable mask‐inspired self‐healing sensor array (MISSA), created using a simplified single‐step stacking technique for detecting and identifying VOCs is presented. This wearable MISSA comprises three vertically placed breathable self‐healing gas sensors (BSGS) with linear response behavior, consistent repeatability, and reliable self‐healing abilities. For wearable and portable monitoring, the MISSA is combined with a flexible printed circuit board (FPCB) to produce a mobile‐compatible wireless system. Due to the distinct layers of MISSA, it creates exclusive code bars for four distinct VOCs over three concentration levels. This grants precise gas identification and concentration prognoses with excellent accuracy of 99.77% and 98.3%, respectively. The combination of MISSA with artificial intelligence (AI) suggests its potential as a successful wearable device for long‐term daily VOC monitoring and assessment for personal health monitoring scenarios.