Tuberculosis (TB) is still a major health problem in Malaysia. Every year, more than 20,000 people get TB, and drug-resistant TB is becoming more common. Even with global targets to reduce TB by 2030, Malaysia is falling behind.
While digital tools like MySejahtera exist, Malaysia is not using artificial intelligence (AI) to help detect and prevent TB. AI could help the country find cases earlier, predict where TB is likely to spread, and make treatment more effective.
Right now, Malaysia mostly reacts to TB cases after people get sick. Screening in high-risk groups, such as urban poor communities, indigenous populations, and prisoners, is limited. TB programs are not well integrated with other health services, like diabetes care, even though diabetes increases TB risk.
AI could change this by doing the following:
- Predict outbreaks before they happen. AI can analyse past TB cases, social factors, and movement patterns to identify hotspots.
- Help doctors detect TB faster. AI-assisted chest X-rays could find TB in people before symptoms get worse, especially in rural areas.
- Focus on high-risk people. AI can identify who should get screened first, making sure vulnerable groups aren’t missed.
- Support patients to finish treatment. AI can flag people at risk of stopping treatment and suggest reminders or follow-ups.
Despite this potential, Malaysia faces several challenges: fragmented health data, no clear rules for using AI safely, limited budgets, and not enough trained staff to use AI tools effectively.
The following needs to be done:
- Create a national plan to use AI for TB prevention, detection, and treatment.
- Connect health data across hospitals, clinics, and migration screening safely.
- Test AI programs in high-TB states like Sabah and Selangor before scaling nationwide.
- Train health workers to use AI tools correctly.
- Protect patient privacy and ensure AI decisions are transparent.
TB is not just a medical problem, it is also a policy and system problem. By using AI strategically, Malaysia can find TB earlier, prevent outbreaks, and make treatment more efficient. This approach could help the country reach global TB targets and protect vulnerable communities.
Mohamad Arif Awang Nawi is a senior lecturer in statistics and data science at the Health Campus, Universiti Sains Malaysia (USM), Kota Bharu.
- This is the personal opinion of the writer or publication and does not necessarily represent the views of CodeBlue.
