Quantum biosensors (QB) are emerging as a transformative technology in medical diagnostics and personalized medicine by leveraging quantum phenomena to enhance sensitivity, specificity, and detection speed compared to traditional biosensors. QBs integrate biological entities like DNA, proteins, or enzymes with quantum sensors, which exhibit discernible alterations in light emissions when interacting with sample molecules. This allows for early and accurate diagnoses of diseases such as Alzheimer's and cancer, as well as real-time monitoring of treatment responses. However, challenges such as stability, reproducibility, and complex quantum interactions remain. The field of QB is rapidly evolving, with advancements in fabrication techniques, interdisciplinary collaborations, and standardization protocols. AI techniques, including machine learning and deep learning, are being integrated to analyze the complex data generated by QBs, enhancing their diagnostic capabilities. Despite these advancements, QBs are still in the early stages of clinical trials and face significant hurdles in widespread adoption. The potential of QBs to redefine medical diagnostics and advance precision medicine is promising, but further research and development are needed to overcome current limitations.Quantum biosensors (QB) are emerging as a transformative technology in medical diagnostics and personalized medicine by leveraging quantum phenomena to enhance sensitivity, specificity, and detection speed compared to traditional biosensors. QBs integrate biological entities like DNA, proteins, or enzymes with quantum sensors, which exhibit discernible alterations in light emissions when interacting with sample molecules. This allows for early and accurate diagnoses of diseases such as Alzheimer's and cancer, as well as real-time monitoring of treatment responses. However, challenges such as stability, reproducibility, and complex quantum interactions remain. The field of QB is rapidly evolving, with advancements in fabrication techniques, interdisciplinary collaborations, and standardization protocols. AI techniques, including machine learning and deep learning, are being integrated to analyze the complex data generated by QBs, enhancing their diagnostic capabilities. Despite these advancements, QBs are still in the early stages of clinical trials and face significant hurdles in widespread adoption. The potential of QBs to redefine medical diagnostics and advance precision medicine is promising, but further research and development are needed to overcome current limitations.