The article "AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring" by Tomasz Wasilewski, Wojciech Kamysz, and Jacek Gębicki explores the integration of artificial intelligence (AI) and machine learning (ML) with portable, user-friendly devices for the detection of biomarkers. The authors highlight the advancements in consumer electronics, microflow techniques, nanotechnology, and data processing, which have led to the development of cost-effective, portable devices that can serve as both gadgets and diagnostic tools. These devices can monitor patients' health in real time and feed data to AI models, improving decision-making in diagnosis and treatment. The article compares traditional clinical practices with modern diagnostic techniques based on AI and ML, emphasizing the potential for these technologies to revolutionize medical diagnostics. It discusses the challenges in translational medicine, such as the gap between laboratory results and clinical practice, and the need for regulatory approval and clinical validation. The authors also review the development and application of biosensors, including contact lenses, mouthguards, diapers, wearable devices, face masks, and smartphones, for detecting both traditional and digital biomarkers. The article further delves into the detection of cancer biomarkers using biosensors, highlighting the role of microfluidic technology and AI in enhancing the accuracy and speed of cancer diagnosis. It emphasizes the importance of understanding the composition of body fluids and their relationship to specific medical conditions for the clinical adoption of wearable technology. The preprocessing of sensor data and the use of AI-based algorithms to enhance sensor performance are also discussed, along with the potential for integrating physical knowledge to improve algorithm performance. Overall, the article underscores the potential of AI and biosensors to transform medical diagnostics by enabling early disease detection, personalized healthcare, and continuous monitoring, while addressing the challenges and limitations of current diagnostic practices.The article "AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring" by Tomasz Wasilewski, Wojciech Kamysz, and Jacek Gębicki explores the integration of artificial intelligence (AI) and machine learning (ML) with portable, user-friendly devices for the detection of biomarkers. The authors highlight the advancements in consumer electronics, microflow techniques, nanotechnology, and data processing, which have led to the development of cost-effective, portable devices that can serve as both gadgets and diagnostic tools. These devices can monitor patients' health in real time and feed data to AI models, improving decision-making in diagnosis and treatment. The article compares traditional clinical practices with modern diagnostic techniques based on AI and ML, emphasizing the potential for these technologies to revolutionize medical diagnostics. It discusses the challenges in translational medicine, such as the gap between laboratory results and clinical practice, and the need for regulatory approval and clinical validation. The authors also review the development and application of biosensors, including contact lenses, mouthguards, diapers, wearable devices, face masks, and smartphones, for detecting both traditional and digital biomarkers. The article further delves into the detection of cancer biomarkers using biosensors, highlighting the role of microfluidic technology and AI in enhancing the accuracy and speed of cancer diagnosis. It emphasizes the importance of understanding the composition of body fluids and their relationship to specific medical conditions for the clinical adoption of wearable technology. The preprocessing of sensor data and the use of AI-based algorithms to enhance sensor performance are also discussed, along with the potential for integrating physical knowledge to improve algorithm performance. Overall, the article underscores the potential of AI and biosensors to transform medical diagnostics by enabling early disease detection, personalized healthcare, and continuous monitoring, while addressing the challenges and limitations of current diagnostic practices.