Artificial intelligence (AI) is becoming more prevalent across the world in all sectors. Robots and cutting-edge AIs process information much faster than humans, increasing efficiency. It can even help with misdiagnosis and medical errors to some extent. Furthermore, scientists and medical practitioners are continuously using technology to treat previously incurable diseases such as AIDS, lupus, polio and cancer.
In 2018, the Asia Pacific AI healthcare market was valued at US$149 million, with rapid growth driven by the accelerating adoption of AI across a growing number of applications. Overall, AI promises to improve diagnostic and treatment accuracy, increase operational efficiency, and provide better patient care. However, a report predicts that the global AI market will grow by 40% a year through 2021.
Southeast Asia is at the forefront of AI healthcare innovation, given the rising demand for medical services and a surge in health-tech start-ups and investment. The Philippines has also been working to adopt technology across the healthcare sector.
The Department of Science and Technology-Philippine Council for Health Research and Development (DOST-PCHRD) has described its latest effective implementation of a validated artificial intelligence (AI) algorithm for diabetic retinopathy screening last week as a “significant milestone,” highlighting the country’s first use of AI in ophthalmology. The Philippine Eye Research Institute (PERI) collaborated with the Queen’s University of Belfast in the United Kingdom to develop an AI-based algorithm for diabetic retinopathy screening on September 21.
This is a significant milestone in Philippine ophthalmology and is a step towards establishing an inclusive program for diabetic retinopathy screening that has the potential to eliminate diabetes-related blindness.
– Philippine Council for Health Research and Development
The Newton-Agham programme REACH-DR (UK-Philippines Remote Retinal Evaluation Collaboration in Health: Diabetic Retinopathy) seeks to set a diabetic retinopathy screening programme (DRSP) in the country. The initiative aims to create a local DRSP to aid in the timely identification of eyes at risk of diabetes-related blindness and visual loss.
To accomplish this, REACH-DR plans to analyse and validate existing telemedicine technology to develop the necessary infrastructure for a local DRSP. The programme also aimed to adapt the selected technologies to the Philippine context and complete the technology transfer to the Philippine stakeholders
“Evaluating retinal images is a highly skilled process, which requires training, continuous quality control, and maintenance of a specialised skill set. As trained retinal image readers are costly and difficult to train with limited numbers worldwide, it has become a necessity to seek automation processes in ocular telemedicine to increase throughput while maintaining cost-effectiveness and accuracy,” said a Harvard Medical School Assistant Professor of Ophthalmology.
According to the DOST-PCHRD, retinal screening is being done in underprivileged areas in Metro Manila and Central Luzon. The screening is expected to be completed by June 2022 by the REACH-DR team.
In addition, the Philippines have established an eHealth Strategic Framework Plan for 2014 to 2020, which is being implemented by the DOH and DOST with WHO guidance, to support the delivery of health services and the efficient management of health systems. As a result of these efforts, the DOH has issued administrative issuances that establish mandatory health data standards to ensure interoperability and to identify the respective roles and responsibilities of the relevant government agencies.
OpenGov Asia reported that there is an increasing global demand for smart healthcare, which includes telehealth and telemedicine. Telehealth is one of the newest industries to make extensive use of AI, from the distribution of electronic medical cards to personal consultations. The role of AI in telemedicine will grow significantly as the field of telemedicine and telehealth evolves with increased adoption.
Consider all the vast amounts of data that AI has the potential to harness – from genomic, biomarker, and phenotype data to health records and delivery systems. Decisions in data-intensive specialities such as radiology, pathology, and ophthalmology are already being supported by the technology. AI technology is increasingly going to perform a plethora of tasks autonomously in the future, allowing medical practitioners to concentrate on better patients outcomes.
