Healthcare - Medical Diagnosis

Alleviate the uncertainties in medical diagnosis using AI/ML

Diagnosis is a complex, patient-centered collaborative activity that involves information gathering, integration, and interpretation with clinical reasoning in determining patient problems. The information-gathering happens in multiple ways through clinical history and patient interview, physical examination, analysis of medical images, and consultation with specialists. The illness is identified with information from current symptoms, past medical history, family history, current medications, diet, lab reports, and medical imaging. The process does not always focus on achieving diagnostic certainty but tries to reduce the uncertainty involved. Here, the integration of AI into health infrastructure accelerates the identification of relevant medical data from multiple sources which are tailored to the needs of the patient and provide holistic diagnoses. Furthermore, AI generates results based on a larger population data rather than on subjective and personal experiences.

Computer Vision can analyze CT Scans, ECG, MRI images, and eye images using deep learning and neural networks to segment anatomical features and predict a larger number of patient conditions. Machine learning algorithms find correlations between patient data and disease occurrences, determining the most likely diseases in the population the patient belongs to. AI systems autonomously triage patients based on the symptoms for review by physicians.

Quantrium has developed a bionomics framework and has the expertise to build models using broad datasets, high-quality labeling, and cutting-edge deep learning algorithms to help medical experts diagnose illness. We're delighted to push this study to new horizons and show how AI might provide revolutionary and breakthrough diagnoses.

Use Cases

Diagnostic Insights

NLP with AI/ML models can be used to process the patient's past medical records and the patient’s preliminary conversations to detect and rank based on the severity of the disease.

Advanced Medical Search

NLP can be used to process large medical reports, drug information, and treatment plans through which healthcare providers can access critical insights that help in clinical decision-making.

Rare Disease Diagnosis

Patients facial descriptors are compared with specific syndrome descriptors to quantity similarity resulting in a list of potential rare diseases using deep learning.


ML algorithms to analyze imaging data acquired during routine cancer care including cancer cell classification, detection, segmentation, characterization, and monitoring.


ML-assisted automated screening, early diagnosis of the eye infections such as glaucoma, diabetic retinopathy, refraction error.


Deep learning AI can be automated to pinpoint accurately the areas of interest such as tumors, fractures, and neurological abnormalities by analyzing the scanned images.

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