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Use Cases in Health

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Health Use Cases
Voice-Enabled Health Summaries
Brief description
The use case is addressing the problem of converting digital records of patient health summaries into speech. The AI-based solution is a digital health summary. The technology or approach used is still in the MVP stage. The domain/track is Health.
Development stage
Target industry
Conversational Health Data Gathering
Brief description
This use case addresses the problem of collecting patient health information. The AI-based solution, Conversational Health Data Gathering, employs ASR technology to facilitate the collection of patient health information through natural dialogue. The project is currently in the pilot stage and falls under the Health domain.
Development stage
Target industry
On-Device Multimodal LLM for Diagnostic Assistance
Brief description
This use case explores the viability of using an On-Device Multimodal LLM for assisting doctors in their diagnosis using their own database of previous users and the patient history. The AI-based solution involves adding tools to perform longitudinal analysis with the imaging data and identify novel biomarkers. This secure, local approach leverages the data of a hospital/clinic to provide new and more accurate insights. An end-to-end solution like this could help in diagnostic accuracy and help make better decisions.
Development stage
Target industry
AI-Optimized Hospital Resource Allocation
Brief description
This use case addresses the problem of improving healthcare services by optimizing hospital resource allocation and scheduling. The AI-based solution uses predictive analytics and machine learning algorithms to forecast patient admissions, allocate resources efficiently, and optimize scheduling to reduce wait times. The next steps include implementing the AI solution in more healthcare facilities, gathering feedback, and making necessary adjustments for full-scale deployment.
Target industry
Who is most impacted
hospitals, healthcare providers, patients
Links resources
AI-Optimized Healthcare Operations
Brief description
This use case addresses the problem of enhancing healthcare services by optimizing hospital operations and improving patient care. The solution involves deploying a predictive analytics system that uses Machine Learning algorithms to forecast patient admissions, optimize staff schedules, and allocate resources efficiently. The system can also analyze patient data to identify high-risk individuals and provide personalized treatment plans.
Target industry
Who is most impacted
healthcare providers, patients, hospital administrators
Optimized Healthcare through Predictive Analytics
Brief description
Enhancing healthcare services by optimizing hospital operations and improving patient care through predictive analytics. Utilizing machine learning algorithms to analyze historical data, predict patient admissions, optimize staff allocation, and improve overall operational efficiency
Target industry
Who is most impacted
Patients, hospital staff, healthcare administrators
AI for Epilepsy Awareness and Treatment
Brief description
Epilepsy is a common neurological disorder in India, with an increasing number of cases each year. Despite its widespread prevalence, there is a significant gap in awareness, diagnosis, and treatment, particularly in rural and semi-rural areas. This use case aims to address these challenges by creating awareness campaigns tailored to specific demographics, improving access to treatment, and using AI to analyze large datasets of EEGs, MRI scans, and other diagnostic tests to detect epilepsy earlier and more accurately.
Development stage
Target industry
Who is most impacted
Epilepsy Patients
AI Consultation Assistant
Brief description
This use case addresses the problem of patients or caregivers struggling to ask relevant questions during medical consultations. The solution is a conversational AI assistant that guides patients or caregivers in formulating questions during these consultations.
Target industry
Who is most impacted
patients or caregivers
RetinaScanAI
Brief description
Develop an AI-based system to detect early signs of diabetic retinopathy in patients. Utilizing computer vision technology to analyze retinal images and identify signs of diabetic retinopathy.
Target industry
Who is most impacted
healthcare providers and diabetic patients
AI-enabled gynecologist consultations for women in underresourced geographies
Brief description
PinkyPromise is an AI-driven clinical decision support tool for women’s health in India. It has been used by ~150,000 women and has completed 10,600+ AI-enabled gynecologist consultations since its launch in March 2024. The app is used across India, with the highest volumes coming from areas around New Delhi, Punjab, Haryana, and Telangana. 54% of their customers have never consulted a gynecologist before consulting one on Pinky Promise and >70% of their customers are from tier 2, tier 3 and smaller areas of India.
Development stage
Target industry
Who is most impacted
Women
Links resources
AI-Powered Scribe for Clinical Trials
Brief description
This use case addresses the problem of time-consuming patient information collection and form filling for clinical trials. The solution is an AI-powered scribe that allows healthcare providers to document patient interactions within seconds using voice commands. It also streamlines clinical trial form completion, saving time and reducing costs. The AI’s performance is being validated through a clinical trial at Tata Memorial Hospital, involving 300+ patient encounters.
Development stage
Target industry
Who is most impacted
Patients
AI-Driven Pacemaker
Brief description
This use case addresses the limitations of traditional pacemakers. We are integrating AI, embedded systems, and real-time signal processing into a next-generation AI-driven pacemaker. The technology used includes Deep Learning (TensorFlow/Keras), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) to detect and classify cardiac arrhythmias in real-time. The AI continuously analyzes ECG signals, detecting abnormalities early.
Development stage
Target industry
Who is most impacted
Heart attack Patients
AI Sleep Assistant
Brief description
The use case addresses the problem of lack of sleep in individuals, particularly the elderly. The proposed solution is an AI Chatbot that gathers information about the individual's physical and mental state, food habits, daily routines, and stress symptoms. Based on this data, the AI Chatbot can suggest various methods to improve sleep quality, such as singing a song, telling a story in a soothing voice, or providing reassurance.
Development stage
Target industry
Who is most impacted
People who are deprived of sleep
Links resources
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