Artificial intelligence helping healthcare experts find more TB and other diseases in Thai hospitals

Shobha Shukla *

With only 56 months left to end TB globally by 2030, the progress is way off the mark. To end TB, we have to protect people from getting infected with TB bacteria in the first place - and - we have to find all those with TB disease (correct and timely diagnosis), link them to effective and right treatment, care and support.

The Land of Smiles - Thailand - has done commendably well in responding to TB over the years. For example, World Health Organization (WHO) no longer lists it among high-burden countries for drug-resistant forms of TB, but it continues to be in the list for high burden TB and TB-HIV nations.

Thailand misses 1 in every 5 people with TB disease

Out of the estimated 104,000 persons with TB disease in Thailand in 2024, the country could diagnose and treat 81,700 of them. It missed reaching out to over 22,000 people with TB – deadliest of all infectious disease today. Annual TB decline (2023-2024) in Thailand as per the latest WHO Global TB Report 2025 is 2% which is good but not good enough to end TB by 2030.

Ray of hope to find more TB in Thai hospitals

Health systems miss TB due to at least 2 major reasons: 1) access barriers faced by those most in need, 2) bad diagnostic tools like microscopy that grossly underperforms in finding TB (misses half or more of those with TB among those who take a TB test). Diagnostic (and hence treatment) delays and catastrophic costs go in tandem.

That is why all the UN countries, including Thailand, agreed at the 2023 United Nations General Assembly High Level Meeting on TB that they would completely replace microscopy with WHO recommended molecular tests for upfront TB testing by 2027.

As per latest WHO report, upfront molecular testing in Thailand shot up to 69% in 2024 whereas globally it was 54% (and even lower in South-East Asian region at 41%). The world has 20 more months to completely replace poor performing TB microscopy test with upfront molecular testing (by 2027).

AI means Artificial intelligence as well as “All Inclusive” approach

Evidence shows that not just diagnosing TB correctly is enough but early and timely diagnosis is critical too. AI helps us find people with TB even when they have no symptoms.

Thailand is deploying many more proven strategies to improve infection prevention and control, find more TB early enough and link those diagnosed with TB to right treatment, care and support. One such proven tool is artificial intelligence (AI) which is helping Thai healthcare professionals to not-miss those with TB (and few other diseases which are screened by AI).

In 2022, Thailand FDA had approved Genki AI, which is an AI powered lung health screening software (developed by DeepTek) to automate the interpretation of chest X-rays for 27 different pathologies including TB. Genki is also approved by US FDA and by regulators of several other countries/ regions, such as European Union, Singapore, India, Malaysia, Kenya, Indonesia, among others.

Thailand’s FDA approved Genki AI for screening for a range of pathologies including TB, general opacity, pneumonia, nodules, atelectasis, fibrosis, lung mass, opaque hemithorax, oedema, calcification, pleural effusion, pleural thickening, pneumothorax, cardiomegaly among others.

AI turning point of 2021

In July 2021, WHO had integrated AI powered computer-aided detection software into its official guidelines for TB screening and diagnosis to help bridge the "missing millions" gap in TB detection. AI powered software can be used to interpret digital chest X-rays for TB screening.

This was historically the first time ever when AI powered computer-aided detection software was recommended for use in interpreting chest X-Rays for TB. Several studies have shown that AI-enabled computer-aided detection software can achieve highly sensitive TB detection in population-based screening and its accuracy is at-par with human readers. Moreover, AI enabled TB screening tools - like Genki - are highly cost effective in resource limited high burden settings.

CNS Managing Editor Shobha Shukla visited one of Thailand’s hospitals which is almost half a century old in Chonburi province - Aikchol Hospital where noted radiologist Dr Grisit Prueksaritanond has been using Genki AI for over a year now. Chonburi province is among those Thai provinces like Bangkok notable for higher TB rates.

Dr Grisit shared insights on how Genki AI is helping him not-miss TB and other lung abnormalities. Aikchol Hospital has X-Rays including mobile X-Ray (of Shimadzu, Japan) powered with Genki AI.

Among over 1000 chest X-Rays scanned in a year with Genki AI (as well as by Dr Grisit), it helps Dr Grisit reconfirm his X-Ray interpretation and diagnosis, and has helped him stop missing 3 cases with lesions - which otherwise (without Genki AI) would have been missed.

“Genki AI is crucial. I think it is very helpful,” said Dr Grisit.

Multi-disease AI screening is a boon too

Dr Grisit points out that when Genki AI helps detect an abnormality in the lung, “It is already very helpful.” This needs to be followed up with medical expert’s further investigations (like confirmatory tests and expert medical assessment and advise), be it general opacity, TB, nodule, fibrosis, or lung mass, among others.

Dr Grisit reflected that “as long as it (Genki AI) can detect something in the lung, I can evaluate further. Sometimes, I just might have missed it wholly if I was not using any programme (AI).” Dr Grisit underpinned importance of Genki AI screening of chest X-rays in finding not just more TB, but also those with fibrosis, pneumonia, pneumothorax, or nodules.

It is noteworthy that WHO is also shifting towards multi-disease elimination approach in recent years.

Do not misdiagnose but diagnose early, correctly

Dr Grisit highlighted the importance of not missing any patient with lung abnormality. In the last one year, AI helped him diagnose at least 3 cases correctly (which otherwise would have been missed). “So, I think that is worth more. It is very sensitive – it is more sensitive than my eyes. So that's better!”

Dr Grisit says that in settings where availability of radiologists is scarce, AI can be a bigger boon.

Thailand is a higher middle-income country. But availability of human experts is often scarce in low- and middle-income countries. So it saves the time of experts where AI can be of help. And who gets benefitted the most? The underserved people.

Generally speaking, AI became a substitute for a human expert reader in places where experts (like a radiologist or trained medical officer) were not available to detect abnormalities consistent with TB and avoid delays in the care pathway – especially in low- and middle-income countries. For example, Indian government has deployed AI-enabled handheld X-Rays for screening high risk populations for TB.

While referring to AI computer aided detection of TB, Dr Grisit said that "I think it is quite useful for the country that has few radiologists. And it is also quite helpful if even where you have a radiologist because AI can double check that he/she/they are not missing any finding in the chest X-Ray."

Triaging those who do not have a disease

Especially in high burden and low resource settings, it is important to triage those who are likely to not have the disease. AI is a great help in this context, said Dr Grisit. “Ruling out people who do not have any problem is important – and it is much quicker this way (so that those with some health problems can access care earlier). Otherwise, it would be a very tedious process for those people who do not have any disease or any lesions to get ruled out.” Dr Grisit underpins the importance of medical experts (which is often a legal mandate too) while we expand the use of AI in health systems.

With 56 months to end TB, Thailand - and the world - has to keep the #endTB and #SDGs promise. We have to prevent people from getting infected with TB disease as a human rights imperative - and those with TB bacteria must access standard care in person-centred, rights-based and gender transformative manner, where no one is left behind.