The state of TB in India

We believe Artificial Intelligence (AI) can potentially play a role through the cascade of care.
The government of India has announced an ambitious target to end TB by 2025.

As of publishing this post, over 130,000 people have died because of Covid19. It is a disturbing number. The virus has overwhelmed health services and dominated headlines. But tuberculosis (TB), according to news reports, claimed 80,000 lives in 2019 and continues to be one of India’s biggest public health challenges. 

Earlier, the government of India announced an ambitious target to end TB by 2025. There was a reason for singling out this infection. About a third of global deaths due to TB are in India. According to reports, about 2.4 million people with TB were reported in India in 2019 and 2.64 million were estimated to have the disease in 2019. TB is a challenging disease to fight against. Physicians say that the bacterium that causes TB can stay dormant in a person for an indefinite period and can activate at any time, which can cause the patient to manifest symptoms. 

But despite this, most strains of TB are curable. A rigorous six-month regimen can treat the infection. But patients either aren’t diagnosed or don’t follow through on treatment. This makes the fight even harder. 

How India handles TB

India has a multipronged strategy when it comes to handling TB. Health workers canvas at-risk areas and speak to people in those locations. Because TB has non-specific indolent early symptoms, there are no obvious tell-tale signs. Patients are encouraged to visit health facilities if they show symptoms. There, healthcare workers or doctors direct them to get lab tests. This is where leakage first begins, some patients never visit the centre or those who visit are not directed to get tests. 

Those who do, and are diagnosed by subsequent lab tests, are registered on a national database, where their treatment is tracked. This is where the second challenge emerges. Some patients are misdiagnosed and miss out on much-needed care. Those who are accurately diagnosed, are assigned a doctor.

As of 2018, in India, over 50% of the estimated 2.64 million people who have TB and walk into clinics seeking care do not go through the public healthcare system. This means the accuracy of diagnosis, estimation of caseload or adherence to treatment is unknown. This is another point where patients can’t be tracked for care. According to research by public health professionals, TB treatment in the private sector is fraught with challenges. In private care, the physicians may not follow standard protocol and a lack of structure can cause an absence of supervision, which leads to patients stopping treatment prematurely. 

Those patients who take the public healthcare system are prescribed medication. The government has enabled a call centre service that patients can use to discuss treatment and ask questions to healthcare professionals. During treatment, India loses another clutch of patients. In public health, this is called, “loss to follow up” or LFU. This increases the risk of the patient developing drug resistance and spreading the infection to others. 

The role AI can play

Artificial Intelligence (AI) can potentially play a role through the cascade of care. One of the primary challenges in the existing system is the identification of hotspots. Most healthcare workers use obvious income and population density markers to find probable patients. Researchers could also create an AI model, which will be able to identify potential patient locations depending on markers beyond just the two currently used, hence making it easy to find patients in non-obvious locations as well. 

Wadhwani AI has also developed a model, where a patient can be triaged for being at risk of having the coronavirus. The argument, hence, can be made that a similar tool can be used to triage TB patients without the need for issuing X-ray vouchers.

Once identified, the patient can be tracked and according to certain behavioural patterns and contextual data, researchers believe that an AI model can predict who is likely to be lost to follow up.

  • Wadhwani AI

    We are an independent and nonprofit institute developing multiple AI-based solutions in healthcare and agriculture, to bring about sustainable social impact at scale through the use of artificial intelligence.

ML Engineer


An ML Engineer at Wadhwani AI will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:

Early stage of proof of concept (PoC)

  • Setup and structure code bases that support an interactive ML experimentation process, as well as quick initial deployments
  • Develop and maintain toolsets and processes for ensuring the reproducibility of results
  • Code reviews with other technical team members at various stages of the PoC
  • Develop, extend, adopt a reliable, colab-like environment for ML

Late PoC

This is early to mid-stage of AI product development

  • Develop ETL pipelines. These can also be shared and/or owned by data engineers
  • Setup and maintain feature stores, databases, and data catalogs. Ensuring data veracity and lineage of on-demand pulls
  • Develop and support model health metrics

Post PoC

Responsibilities during production deployment

  • Develop and support A/B testing. Setup continuous integration and development (CI/CD) processes and pipelines for models
  • Develop and support continuous model monitoring
  • Define and publish service-level agreements (SLAs) for model serving. Such agreements include model latency, throughput, and reliability
  • L1/L2/L3 support for model debugging
  • Develop and support model serving environments
  • Model compression and distillation

We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.


Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.


  • Expert level Python programmer
  • Hands-on experience with Python libraries
    • Popular neural network libraries
    • Popular data science libraries (Pandas, numpy)
  • Knowledge of systems-level programming. Under the hood knowledge of C or C++
  • Experience and knowledge of various tools that fit into the model building pipeline. There are several – you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
  • Database concepts; SQL
  • Experience with cloud platforms is a plus

ML Scientist


As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.

In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.  


Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments. 

Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good. 


  • B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field. Work experience beyond the terminal degree will determine the appropriate seniority level.
  • Solid software engineering skills across one or multiple languages including Python, C++, Java.
  • Interest in applying software engineering practices to ML projects.
  • Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
  • Strong verbal and written communication skills in English.