The Global TB Crisis: A Race Against Time
The world has 56 months left to end tuberculosis (TB) globally by 2030. Yet progress remains dangerously off track. Thailand alone estimated 104,000 people with TB disease in 2024. Of those, only 81,700 received a diagnosis and treatment — meaning the country misses roughly 1 in every 5 people with TB. This gap is not unique to Thailand. Across high-burden nations, millions go undetected every year — the so-called “missing millions.”
Globally, the challenge runs deeper. Nearly half of all TB cases occur in people with no visible symptoms. Traditional screening methods like sputum microscopy frequently miss these silent infections. Thus, finding TB early and accurately has remained a formidable public health problem — until artificial intelligence (AI) entered the picture.
How AI Is Changing TB Diagnosis
From Scarce Radiologists to Smart Algorithms
In most low- and middle-income countries, radiologists are either unavailable or overwhelmed. AI-powered computer-aided detection (CAD) software fills this critical gap. These tools interpret digital chest X-rays automatically. They flag abnormalities consistent with TB and alert clinicians for a follow-up review. Consequently, diagnoses no longer depend solely on the physical presence of a trained specialist.
Moreover, AI screening works fast. A chest X-ray takes about one minute. The AI delivers its report almost instantly. For patients with suspected TB, a confirmatory molecular test then follows within hours. This speed reduces delays and cuts the costs patients face when seeking care far from hospitals.
Genki AI in Action: Thailand’s Aikchol Hospital
A Radiologist’s First-Hand Account
CNS Managing Editor Shobha Shukla visited Aikchol Hospital in Chonburi province — a nearly 50-year-old institution in one of Thailand’s higher TB-burden regions. There, radiologist Dr. Grisit Prueksaritanond has used Genki AI for over a year alongside conventional X-ray equipment, including a Shimadzu mobile X-ray unit from Japan.
Over that period, Dr. Grisit scanned more than 1,000 chest X-rays using Genki AI. The tool helped him reconfirm his own interpretations. Crucially, it helped him catch three cases with lung lesions that he would otherwise have missed. Those patients received timely care as a direct result.
Dr. Grisit also highlighted a key efficiency gain. Ruling out people without any abnormality is just as important as identifying those who are sick. Genki AI makes that process quicker, freeing up clinical time for patients who genuinely need attention. Without AI, clearing hundreds of patients manually would be far more tedious and time-consuming.
What Genki AI Screens For
27 Pathologies Beyond Tuberculosis
Thailand’s FDA approved Genki AI in 2022. Developed by DeepTek, it automates chest X-ray interpretation across 27 different pathologies. These include TB, pneumonia, nodules, atelectasis, fibrosis, lung mass, opaque hemithorax, oedema, calcification, pleural effusion, pleural thickening, pneumothorax, and cardiomegaly, among others.
Genki AI holds regulatory approval in multiple countries and regions. The US FDA, the European Union, Singapore, India, Malaysia, Kenya, and Indonesia have all validated the technology at varying levels of rollout. Thailand’s approval specifically covers a comprehensive range of lung conditions — making it a versatile tool for general respiratory health screening, not just TB.
The WHO Endorsement That Changed Everything
A Historic Shift in TB Guidelines
In July 2021, the World Health Organization made history. For the first time, WHO integrated AI-powered computer-aided detection software into its official TB screening and diagnosis guidelines. The move directly addressed the “missing millions” problem in global TB detection.
Studies had already demonstrated that AI-enabled CAD software delivers highly sensitive TB detection in population-based screening. Furthermore, its accuracy matches that of trained human readers. This evidence gave WHO the confidence to endorse AI as a legitimate diagnostic tool — not just a supplementary one.
Why Early and Accurate Diagnosis Matters
The Case for Speed in TB Care
Early diagnosis saves lives. It also stops transmission. Once a person with active TB starts effective treatment, they quickly become non-infectious. Therefore, finding TB sooner prevents further spread within communities. Delay, on the other hand, allows infection to multiply.
AI helps find TB even among asymptomatic patients — a group that microscopy-based tests consistently miss. Government-led surveys confirm that nearly half of all TB cases fall into this silent category. AI-enabled X-rays reach these patients by bringing screening directly to communities, including remote and underserved areas.
AI as “All Inclusive” Healthcare
Bridging the Access Gap for the Underserved
The acronym AI means artificial intelligence. In this context, it also stands for “All Inclusive.” AI-enabled TB screening tools are highly cost-effective, particularly in resource-limited, high-burden settings. They do not replace medical experts — Dr. Grisit himself underlines that expert oversight remains a legal and ethical requirement. Instead, AI supports clinicians, reduces their burden, and ensures fewer cases fall through the cracks.
Additionally, the technology democratises diagnostic access. Communities that previously lacked any radiological infrastructure can now receive screening through portable, battery-operated AI-enabled X-ray units taken directly to their doorsteps.
The Road to Ending TB by 2030
Keeping the Global Promise
World leaders at the 2023 UN General Assembly High Level Meeting on TB committed to diagnosing at least 90% of all TB patients by 2027. Meeting that target demands tools that work at scale, in diverse settings, and at low cost. AI delivers on all three fronts.
Thailand continues to deploy proven strategies — better infection prevention, earlier detection, and stronger linkage to treatment. AI is central to all three. The country’s efforts, supported by tools like Genki AI and endorsements from WHO, signal a path forward for the entire global TB response. With commitment, collaboration, and the right technology, ending TB by 2030 remains an achievable goal.
