Could robot doctors be on the horizon? A study published February in Nature Medicine revealed that AI could be used to detect and diagnose childhood diseases as accurately as physicians.

The research highlights just one aspect of a growing trend. Politicians and policymakers in the United Kingdom, Japan, and China have all committed to funding research and programs that integrate AI for greater efficiency in underfunded hospitals.

In some countries, long wait times for medical services and doctor shortages have made it difficult for people to get the care they desperately need. In an effort to solve problems plaguing hospitals, governments around the globe are investing in technologically driven approaches to healthcare.

A valuable tool

In the Nature study, researchers in the United States and China used 18 months of data compiled on 6,000 patients in China that included their symptoms, history, and lab results.

They then used computers to analyze large quantities of documents to find patterns between symptoms, medical history, and academic research to automatically identify common childhood illnesses. The technology could diagnose infections from meningitis to asthma with over 90 per cent accuracy in some categories.

The researchers concluded that AI is a “powerful tool” to mine data that will aid in disease diagnosis and management that mirrors decisions made by human physicians.

Catalina Vallejos, Chancellor Fellow at the MRC Genetics Unit at the University of Edinburgh and a Turing Fellow said the results of AI research is encouraging and that there are “huge opportunities” in using AI within healthcare.

She added that the big boom in big data, data science, and AI in healthcare aims to improve patient benefits and outcomes, but policies and AI programs should be bespoke to ensure the best chances of securing those outcomes.

Tackling shortages

In China’s bid to become the AI leader of the world by 2030, the government has pledged to make huge investments in tech startups and AI research to solve common problems. China has made the health sector one of it’s biggest priorities to be at the forefront of its AI strategy in an effort to reduce hospital backlog.

Capitalising on government investments, hospitals have been partnering with tech companies to help assist with understaffing.

According to 2017 OECD rankings, China is the third worst country for the number of doctors per inhabitants with 1.8 doctors per 1,000 people, Austria was the highest at 5.1.

A collaboration between China’s search engine Baidu and Sun Yat-Sen University has created new technology using artificial intelligence and cameras that are capable of diagnosing three types of eye disorders with 94 per cent accuracy.

The technology, piloted in several hospitals in China including Deqing county hospital in the Guangdong Province, was designed to help deal with the shortage of ophthalmologists, particularly in rural areas.

A second project was launched to help with the shortage of trained dermatologists able to detect skin diseases.

A research chief at the China-Japan Friendship Hospital, Cui Young, was approached by the government to research new methods for internet-based consultations.

Due to the lack of trained dermatologists missing the skills to evaluate various skin diseases, he then started an application called Quality Skin. The experimental app produces probability-weighted results of over 2,000 skin diseases.

While China is making strides in developing new specialised technology to fix understaffing, Japan has committed to completely revolutionizing hospitals with built-in AI capabilities to deal with its doctor shortage.

In 2017, the Japanese government pledged to invest $100 million to create ten hospitals by 2022 that will use AI from everything to administrative duties to assisting with surgery.

Although the number of doctors in Japan climbed 2.7 per cent in 2018, physicians remain highly concentrated in urban areas. In some rural cities in Japan, such as Fukui and Nara will each have five or fewer surgeons and in remote cities of Kochi and Gunma will only have one each, according to JMSB.

In the framework for the AI hospitals, the education, industry, and health ministries will work with academia and businesses to develop time-saving AI solutions that allow doctors to have more time to focus on patients.

Participants are in the process of making computerized assistants that can automate medical records and enter medical information based on patient conversations with nurses and doctors. AI will also be used to analyze resonance scans, endoscopic images, and blood tests to assist with diagnosis. New technologies for gathering data from blood pressure meters, electrocardiographs, and other devices are also currently being developed.

The Japanese government is expecting AI-powered tools to not only save billions of yen a year in medical expenses but allow doctors to see more patients and expand services particularly in rural areas.

Cutting wait times

Meanwhile, one English hospital will integrate AI technology to, not only deal with doctor shortages but to reduce wait times for patients seeking medical treatments or emergency services.

Wait time for medical services is at a record high in England. The amount of people waiting longer than two months for specialist treatment on the National Health Service (NHS), for example, has increased by 48 per cent from 2017 to 2018, according to NHS statistics.

Royal College Hospital London (RCHL) is collaborating with a government-funded data science research institution, the Alan Turing Institute, to tackle wait times by implementing AI for speedier decision-making.

One area of focus will be creating algorithms to analyze the NHS’s collection of health data to diagnose common illnesses such as chest pain to find the best treatment option. AI would be used to determine how critical each patient’s case was, so the more severe could be fast-tracked for treatment.

For less serious cases, the algorithms would help doctors decide the best course of action, such as administering blood tests or ultimately discharging the patient. This solution aims to help the hospital hit the NHS four hour target wait time for emergency services.

Another part of the collaboration will centre on reducing congestion in the hospital for more efficient operations. The researchers will use machine learning to analyze data collected on the flow of people moving through the hospital.

AI-dependent healthcare is on course to be transformative in delivering medical care. AI’s capabilities to assist doctors and medical professionals will result in more time saved and ultimately, lives.

But as more governments are signing on to use AI, Vallejos warned that the ethical implications of using AI need to be considered. She said that AI should not be used independently from humans and that prioritizing patients based on technology could cause people to fall behind. 

This piece originally appeared on Apolitical, the global network for public servants. You can find the original here.