Parkway Radiology has implemented the Annalise.ai Enterprise CXR solution across all its radiology clinics and hospital departments in Singapore. This represents the first network-wide deployment of the chest X-ray AI technology in the country.
The solution is capable of identifying up to 124 clinical findings on chest X-rays within seconds. It is intended to support radiologists in enhancing diagnostic accuracy and consistency, particularly in the detection of subtle or critical findings.
The AI system is now fully operational at Parkway Radiology’s clinics in Bedok, Paragon, Jurong East, and Republic Plaza, as well as in the radiology departments of Gleneagles Hospital, Mount Elizabeth Hospital, Mount Elizabeth Novena Hospital, and Parkway East Hospital.
Chest X-rays represent a substantial portion of Parkway Radiology’s services, with more than 85,000 examinations performed in 2024. This modality is commonly used in the diagnosis and management of cardiopulmonary conditions, acute and emergency care, follow-up imaging, and statutory screenings such as those for employment.
Ms Tan Yujuan, CEO of Parkway Radiology, said, “We are excited to include the Annalise.ai solution to our chest X-ray services. By leveraging AI, it augments the team’s diagnostic skills and expertise while ensuring that we remain at the forefront of healthcare and innovation. This will translate to better care for the thousands of patients whose lives we touch every day.”
Dr Tham Seng Choe, Clinical Director of Parkway Radiology’s Radiologic Clinic at Mount Elizabeth Novena Hospital and clinical lead for the implementation, added, “Parkway Radiology has consistently led the way in adopting new technologies to improve patient care, and AI represents the next step in this evolution. Since integrating Annalise.ai into our reporting workflow, it has improved the team’s diagnostic confidence and overall reporting performance.”
This implementation reflects Parkway Radiology’s ongoing efforts to incorporate AI technologies that support clinical decision-making and improve patient outcomes within diagnostic imaging.
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