Wireless capsule endoscopy (WCE) is a non-invasive method for evaluating the digestive system, offering advantages over traditional endoscopic procedures by eliminating the need for sedation and reducing associated risks. However, existing commercial WCE devices have limitations, such as the absence of autonomous lesion detection and treatment capabilities. Recent advancements in micro-electromechanical systems (MEMS) and artificial intelligence (AI) have led to the integration of sophisticated technologies into commercial capsule endoscopes, aiming to surpass wired endoscopes. This review discusses the future requirements for intelligent capsule robots, comparing various methods and highlighting recent developments in six key technologies: near-field wireless power transmission, magnetic field active drive, ultra-wideband/intrabody communication, hybrid localization, AI-based autonomous lesion detection, and magnetic-controlled diagnosis and treatment. The review explores the challenges and solutions in each technology, emphasizing the potential of "capsule surgeons" that can perform surgeries within the human body without direct medical intervention. It also discusses the limitations of current WCE devices, such as their inability to conduct surgical interventions during diagnostic procedures and their limited endurance. The article provides a comprehensive comparison of traditional and advanced intelligent technologies, highlighting the benefits and drawbacks of each method. Additionally, it covers the development of WCE devices, including improvements in image quality, battery life, and active movement, as well as the integration of intelligent lesion detection capabilities. The review concludes by discussing the future directions for capsule endoscopy research, emphasizing the potential of battery-free wireless power transmission, active locomotion, communication, localization, visual detection of lesion tissue, and diagnostic and therapeutic functions. It also explores the integration of AI algorithms to enhance the accuracy and efficiency of WCE, making it a promising tool for gastrointestinal diagnostics and treatment.Wireless capsule endoscopy (WCE) is a non-invasive method for evaluating the digestive system, offering advantages over traditional endoscopic procedures by eliminating the need for sedation and reducing associated risks. However, existing commercial WCE devices have limitations, such as the absence of autonomous lesion detection and treatment capabilities. Recent advancements in micro-electromechanical systems (MEMS) and artificial intelligence (AI) have led to the integration of sophisticated technologies into commercial capsule endoscopes, aiming to surpass wired endoscopes. This review discusses the future requirements for intelligent capsule robots, comparing various methods and highlighting recent developments in six key technologies: near-field wireless power transmission, magnetic field active drive, ultra-wideband/intrabody communication, hybrid localization, AI-based autonomous lesion detection, and magnetic-controlled diagnosis and treatment. The review explores the challenges and solutions in each technology, emphasizing the potential of "capsule surgeons" that can perform surgeries within the human body without direct medical intervention. It also discusses the limitations of current WCE devices, such as their inability to conduct surgical interventions during diagnostic procedures and their limited endurance. The article provides a comprehensive comparison of traditional and advanced intelligent technologies, highlighting the benefits and drawbacks of each method. Additionally, it covers the development of WCE devices, including improvements in image quality, battery life, and active movement, as well as the integration of intelligent lesion detection capabilities. The review concludes by discussing the future directions for capsule endoscopy research, emphasizing the potential of battery-free wireless power transmission, active locomotion, communication, localization, visual detection of lesion tissue, and diagnostic and therapeutic functions. It also explores the integration of AI algorithms to enhance the accuracy and efficiency of WCE, making it a promising tool for gastrointestinal diagnostics and treatment.