In collaboration with the Ministry of Health and Family Welfare and other partners, we are currently working on a range of AI solutions to address the acute shortage of trained medical personnel and resources, and to help augment existing public health frameworks
Wadhwani AI is developing a suite of AI-powered tools that can assist doctors and other healthcare staff during patient consultations and provide AI-powered differential diagnosis recommendations for more efficient delivery of services in the public healthcare sector.
One such tool is a Patient Assistance plugin, in the form of a smart interactive form. Patients are asked to answer a series of questions in the line of diagnosis as they wait at the health facility to consult a physician.
Another tool in development is a Diagnostic Assistance solution to assist physicians during patient consultations. Based on the signs and symptoms collected via the Patience Assistance form, the Diagnostic Assistance tool suggests a list of probable differential diagnoses, along with suggestions for diagnostic tests for confirmation.
These AI tools can be incorporated with existing online OPD setups, telemedicine systems, and hospital management information systems (HMIS) that connect patients and doctors via video consultations, where doctors capture the patients’ chief complaints and prescribe medicines.
Wadhwani AI’s AI-powered media scanning tool automates event-based disease surveillance systems. This system aims to detect and respond to disease outbreaks by generating early warning signals and ensuring that an effective and timely response to contain outbreaks can be initiated. Until recently, media scanning for this purpose has been a largely manual process involving hundreds of newspapers and digital media outlets.
The AI model scans large volumes of digital media in multiple languages to track events that signal an impending disease outbreak and maintain a database for them. This database is then leveraged to generate alerts. The AI model automatically and continuously scrapes the web to collect data via news reports and digital media on the internet identifies news articles that contain specific events of interest and then extracts these selected articles to store in a database. This database can be integrated into health information portals, and healthcare personnel can then use the database to keep track of potential outbreaks and create alerts for public health officials when necessary.
Our AI-powered solution for detecting radiological features from chest X-rays is an image-based diagnostic solution that can detect 17 features, including fibrosis, effusion, edema, lung opacity, hernia, cardiomegaly, and emphysema, among others. The solution aims to reduce the burden on radiologists, general practitioners, and lab technicians by providing provisional patient diagnoses. This will enable radiologists to focus on high-priority patients and improve last-mile healthcare delivery at places where radiologists are scarce, and consultations can be conducted through a teleradiology platform.
We are working on a solution to support medical practitioners in diagnosing skin conditions through images captured using smartphone cameras. Our AI model has been trained on an open-source dataset and can diagnose up to 10 skin conditions, including monkeypox, chickenpox, scabies, eczema, psoriasis, and tinea, among others. As data for other diseases becomes available for model training, the number of diseases that can be diagnosed with the solution will increase. The solution can come in handy at healthcare centers where dermatologists may not be available and support is required to diagnose and treat patients with skin conditions.
We are working on a solution to support ophthalmologists, optometrists, and healthcare workers in diagnosing diabetic retinopathy in patients at the last mile. This classification model can grade the severity of diabetic retinopathy through images of the eye’s posterior chamber captured through fundoscopy, enabling healthcare workers to take effective measures for patient treatment. The model has been trained on 35,000 eye images from an open-source dataset and can potentially diagnose other features in posterior eye chambers after the model is trained on annotated image datasets.
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ROLES AND RESPONSIBILITIES
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)
Late PoC
This is early to mid-stage of AI product development
Post PoC
Responsibilities during production deployment
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.
DESIRED QUALIFICATIONS
Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.
Programming
ROLES AND RESPONSIBILITIES
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.
REQUIREMENTS
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.
DESIRED QUALIFICATIONS
