MLworks
ML Works is an enterprise-grade machine learning monitoring platform with automated workflows, pre-built solutions to track model degradation, manage code workflow, and fast track model management.
Use Case Code : AI_POST_SURG
Use Case Code : AI_Clm
Use Case Code : ONCO_DRG
Chemotherepeutic agents act against the rapidly dividing cancer cells. As collateral damage happens, the patients develop several side effects. Such side effects may vary from nausea, vomiting to a major cardiac event. Based upon the exisitng knowledge from published scientific literature, we try to pre-empt such toxicities. This may mean not giving a particular drug, or decreasing dosage or starting other medicines emperically to counter the effects. There is merit in being able to evaluate these effects, not only rare catastrophic ones but also routine ones with higher grades. We want to evaluate if AI can predict such toxicities to various chemotherepeutic drugs.
Prediction of grade 3/4 toxicities related to commonly used drugs to treat various cancers. Input variables will include clinical and demographic data, co-morbidites, various labarotary parameters
Before proceeding we can discuss about the drugs we would like to cover.
Use Case Code : DR_ArMD
The solution needs to clinically validate diabetic retinopathy and age related macular degeneration based on AI-driven software algorithm on fundus images for analysis for the detection and diagnosis . Special emphasis on fundus images from android phone with attachment
Diabetic retinopathy and Age related macular degeneration Grading as referable /non-referable.
Use Case Code : LLM_PC
In rural areas, conducting eye-checkup camps poses challenges due to the high costs associated with hiring patient counselors. Typically, only one human counselor is available, leading to inefficiencies when dealing with a large number of people. To address this issue, a patient counseling chatbot based on Large Language Models (LLMs), based on English and specifically crafted for the Tamil language and tailored to the domain of Ophthalmology is needed. It should capture spoken language inputs, comprehend user queries, retrieve relevant medical information, and synthesize natural-sounding speech for responses. The key innovation lies in the adaptability and accessibility of the chatbot. It must be able to get deployed on any device.
The solution must not only enhance the efficiency of counseling sessions but also aim to revolutionize patient counseling practices, ultimately improving healthcare accessibility and outcomes in rural communities.
Use Case Code : EMS_Disp
EMS / Ambulance service dispatch automation, a solution through which the customer can have interaction based on given input data and the AI triages the patient requirments to decide on the the type of ambulance and resources required, provides information interms of cost and TAT, pushes structured information interms of demgraphic details, address, clinical issue to the allow end user to take further action
Automated, fast and accurate discharge summary generation in a format as advised by the organisation
The solution needs to integrate with the exisiting EHR
Use Case Code : DSA
A robust and accurate automated solution which enables hospitals to prepare the final discharge summary of the patient within a matter of minutes once the discharge intimation is given. The solution must be capable of assimilating, analyzing and putting it in desirable final format consisiting of the clinical data, ward notes, cross consultation, investigation and so on for the enitre duration of patientb stay in the hospital.
Automated, fast and accurate discharge summary generation in a format as advised by the organisation
The solution needs to integrate with the exisiting EHR
Use Case Code : EMS_CDSS
Solution which can provide support to the paramedics with clinical decisions in out of hospital scenarios
Paramedics should be able to take timely actions in prehospital setup with help of CDS.
Use Case Code : AI_CC
Use Case Code : AI_USG
Use Case Code : ONCO_THYR
Thyroid nodules may be present in 30-40% of the general population. Majority of these nodules are benign, but in 5-10% cases these may be malignant. Generally, benign nodules don’t need any intervention. We generally judge whether a nodule is benign or malignant based upon USG and FNAC findings. Molecular tests have also come up in this domain. Still, we end up operating upon a large number of cases who eventually turn out to be benign. These are the patients who could have potentially avoided surgery. There is a need to be able to find out which nodules are at higher risk of malignancy. We want an AI based tool to predict risk of malignancy in thyroid nodules using clinical details (age, gender, etc), fine needle aspiration cytology (FNAC) and ultrasounf (USG) features
Better risk prediction of malignancy in thyroid nodules. This will help avoid surgery in many benign cases.
FNAC slides will need digitalization
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