The Terms may be amended from time to time, with prior notice to App users, in which case, in order to continue using the App, you will be required to accept the revised Terms.
The Cough for TB – HCW app is an AI-powered cough-sounds-based screening solution to assess the likelihood of pulmonary tuberculosis in individuals, for the early detection and treatment of tuberculosis (TB). This App is intended to be used by healthcare workers (HCW).
HCWs designated by the National TB Elimination Program (NTEP). The subjects can be the general population of India from 18–100 years of age, who are not on a TB treatment regimen. Informed consent from parents/ or guardians is required for the screening of subjects below 18 years of age.
The HCW agrees that they will only use the App in good faith and will not provide false or misleading information about themselves, the subject, or their symptomatic and clinical status. The HCW agrees that they will not indulge in any act that may impair the performance or functionality of the App.
The app can be installed on any smartphone with the following minimum specifications:
The App authenticates the HCW using an OTP delivered on the registered mobile number via SMS.
Personal and symptomatic data of the subject needs to be entered, as per the instructions provided in the form. HCWs should refer to Annexure 1 and Annexure 2 for safety measures.
Before recording the cough sounds, please ensure the following for each subject:
The App processes the symptomatic data along with the cough audio and provides the results as follows (please refer to the Disclaimer section for more information about the results):
Likely to have pulmonary tuberculosis
Based on the automated inference, the individual’s chances of having pulmonary tuberculosis are high and they should consult a doctor and get tested to confirm the diagnosis.
Not likely to have pulmonary tuberculosis
Based on the automated inference, the individual’s chances of having pulmonary tuberculosis are low and they should consult a physician if symptoms continue.
Though efforts are made to ensure that the information and content provided as part of the App are correct at the time of inclusion on the App, there is no guarantee to the accuracy of the Information. We do not make representations or warranties as to the fairness, completeness or accuracy of the Information on the App. There is no commitment to update or correct any information that appears on the Internet or on the Website/ Application. Information is supplied upon the condition that the persons receiving the same will make their own determination as to its suitability for their purposes prior to use or in connection with the making of any decision.
Wadhwani AI shall own all rights, titles and interests, including all related intellectual property rights, in and pertaining to (i) the App and any suggestions, ideas, enhancement requests, feedback, recommendations or any other offerings; (ii) the text, graphics, user interfaces, visual interfaces, photographs, trademarks, logos, sounds, music, artwork and computer code; or (iii) other information provided by You, however, in accordance with the applicable laws.
Third-party trademarks, if any, may appear on this App and all rights therein are reserved to the registered owners of those trademarks.
You, the user of the App, shall be solely responsible for any violations of any laws and for any infringements of any intellectual property rights caused by your use of the App.
You can report any defects or bugs in the App or the Services to firstname.lastname@example.org. We will make every endeavor to address all reported bugs and defects.
These Terms shall be governed by the laws of India.
For all devices used for data collection, we shall follow the below recommendations, as issued by the Ministry of Health and Family Welfare (MoHFW).
The following measures will be undertaken to prevent infection during data collection.
IPC is a critical and integral part of data collection from patients, as per the guidelines of the Ministry of Health and Family Welfare (MoHFW).
Standard precautions will always be routinely applied, including hand hygiene, avoiding direct contact with patients’ respiratory secretions, standard precautions for safe waste management and cleaning and disinfection of equipment (including data collection devices).
Implementation of infection prevention and control measures for patients with suspected or confirmed nCoV infection as per the guidelines of the MoHFW.
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)
This is early to mid-stage of AI product development
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.
Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.
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.
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.