AI Tools in Tuberculosis Detection and Management

Recent advancements in technology have implications for tuberculosis (TB) detection and management in India. The country’s Health Ministry aims to eliminate TB by 2025. However, progress has been slow. The Health Technology Assessment of India (HTAIn) committee reported on AI-assisted solutions for TB screening in early 2024. Yet, the implementation of these technologies remains stagnant.

AI Tools for TB Screening

• Two notable AI tools are qXR and Genki.
• Developed by Qure.ai and DeepTek, respectively, these tools use chest X-ray interpretation to identify presumptive TB cases.
• Both solutions have undergone HTA assessments, confirming their cost-effectiveness and accuracy.
• qXR has been implemented in over 3,100 sites globally, while Genki is operational in over 80 sites in India.
• Their pooled sensitivity and specificity meet the World Health Organization’s (WHO) standards.

Regulatory Framework and Delays

The Central TB Division (CTD) oversees the introduction of new technologies. It waits for HTA assessments and approvals from the Medical Technology Assessment Board (MTAB) before implementation. Despite the HTA’s positive evaluations of qXR and Genki, the CTD has not integrated these tools into the national TB programme. In contrast, the CTD has recommended another AI tool, DeepCXR, despite its lack of HTA assessment.

Challenges in Implementation

The CTD’s communication with state authorities has been inadequate. States were advised to “consider utilising” DeepCXR only when they inquired about AI solutions. This lack of proactive communication hinders effective implementation. Moreover, there is limited published data on the performance of DeepCXR, raising concerns about its reliability compared to qXR and Genki.

Cost-Effectiveness and Accuracy

Cost analysis reveals that both qXR and Genki are economically viable. The cost per case screened is ₹30 for qXR and ₹22 for Genki. These tools not only save costs but also provide quick and accurate results. AI-assisted interpretation can be completed in under a minute, making it suitable for resource-limited settings.

Significance of Chest X-Ray Screening

Chest X-rays play important role in TB detection. They were instrumental in identifying percentage of TB cases in recent surveys. The integration of AI technologies enhances the efficiency of screening processes, ensuring timely diagnosis and treatment.

Future Prospects

To meet the 2025 elimination target, the Government of India must expedite the integration of effective AI tools into the TB programme. Addressing bureaucratic delays and enhancing communication with state authorities will be essential for improving TB management and outcomes.

Questions for UPSC:

1. Examine the impact of artificial intelligence on public health management in India.
2. Discuss the role of chest X-ray screening in the early detection of tuberculosis.
3. Critically discuss the challenges faced in the implementation of health technology assessments in India.
4. With suitable examples, discuss the importance of cost-effectiveness in healthcare technology adoption.

Answer Hints:

1. Examine the impact of artificial intelligence on public health management in India.

1. AI tools like qXR and Genki enhance TB screening accuracy and speed, aiding early detection.
2. AI-assisted interpretation reduces the workload on healthcare professionals, allowing for more efficient use of resources.
3. Integration of AI can lead to cost savings in healthcare, making treatments more accessible in resource-limited settings.
4. AI technologies can facilitate data analysis and improve decision-making in public health policies.
5. Successful implementation of AI tools has the potential to transform TB management and contribute to the elimination goal by 2025.

2. Discuss the role of chest X-ray screening in the early detection of tuberculosis.

1. Chest X-rays are crucial for identifying presumptive and subclinical TB cases, as evidenced by recent surveys.
2. AI-assisted X-ray interpretation provides rapid results, reducing the time for diagnosis.
3. High accuracy rates of tools like qXR and Genki enhance the reliability of chest X-rays in TB detection.
4. Screening via chest X-rays can uncover cases that may not be identified through traditional methods.
5. Integration of X-ray screening into national programs can lead to timely treatment and better health outcomes.

3. Critically discuss the challenges faced in the implementation of health technology assessments in India.

1. There are bureaucratic delays in the approval and integration of new technologies into health programs.
2. Lack of proactive communication from the Central TB Division hinders timely implementation of AI tools.
3. Dependence on HTA assessments before implementation can slow down the adoption of effective technologies.
4. Limited published data on certain tools, like DeepCXR, raises concerns about their reliability and efficacy.
5. Stakeholder engagement in the HTA process is often inadequate, leading to missed opportunities for innovation.

4. With suitable examples, discuss the importance of cost-effectiveness in healthcare technology adoption.

1. Cost-effectiveness of qXR and Genki is evident with screening costs of ₹30 and ₹22 respectively, promoting wider use.
2. Both AI tools demonstrate cost savings per case, making them attractive options for healthcare providers.
3. Cost-effective technologies improve access to TB screening, especially in low-resource settings.
4. Successful examples include the integration of qXR in over 3,100 sites globally, showcasing its economic viability.
5. Health technology assessments (HTA) must prioritize cost-effectiveness to ensure sustainable healthcare solutions.