top of page

"Machine Learning in a Box"

SquareML Generative AI and machine learning streamline health record summarization for actionable insights, improved patient care and empowering providers with concise summaries for informed decisions. 

Visualization of a healthcare data aggregator network, illustrating data sources interconnected with arrows pointing towards

Machine Learning for Medical Summarization

Summarize Medical Records, with Generative AI and Machine Learning

Designed by Freepik

Analyzing Scans

Our Medical record summarization solutions involves Generative AI based condensing of the detailed medical history and information of a patient into a concise, clear, and easy-to-understand format. The goal is to present the most critical information efficiently for medical professionals to quickly grasp the patient's medical background and current condition. Here are some key aspects involved in medical record summarization:

  1. Patient Information: Start with the patient's demographic information such as name, age, gender, date of birth, and contact details.

  2. Medical History: Include past medical conditions, surgeries, hospitalizations, and major illnesses. Note any chronic diseases and relevant family medical history.

  3. Medications: List current medications, including dosage and frequency, as well as any allergies or adverse reactions to medications.

  4. Immunizations: Include information about the patient's vaccination history.

  5. Clinical Findings: Highlight the most recent clinical findings, such as physical examination results, vital signs, and test results.

  6. Diagnoses: Provide a summary of current diagnoses and any differential diagnoses being considered.

  7. Treatment Plan: Outline the current treatment plan, including any ongoing therapies, procedures, or future treatment options.

  8. Social History: Summarize relevant aspects of the patient's lifestyle, such as smoking, alcohol use, and exercise habits.

  9. Lab and Imaging Results: Include a brief summary of recent lab and imaging results, especially if they impact the patient's diagnosis or treatment.

  10. Progress Notes: Briefly mention any significant progress notes from recent medical visits, including any changes in the patient's condition, responses to treatments, or other observations made by healthcare professionals.

Health Data Aggregation
Dashboard interface showing various graphs, charts, and metrics related to healthcare data aggregation and analysis

Electronic Health Record

Group of professionals engaged in a discussion around a conference table, focusing on data aggregation strategies

Modality Data

step-by-step process of healthcare data aggregation, including data collection, processing, analysis, and reporting.


Data Aggregation

Healthcare Formats

Data Aggregation

Remote Monitoring Data

Data Aggregation

Vitals/ Bio metrics

Data Aggregation


Data Aggregation

Unstructured Data

Data Aggregation

Claims Data

Device Data

Health Data Aggregation


Health Data Aggregation

ADT Records

Health Data Aggregation
Health Data Aggregation
Health Data Aggregation
Health Data Aggregation
Health Data Aggregation
Health Data Aggregation
Health Data Aggregation



Medical Record Summarization Made Easy

Use cases

Actionable Insights

SquareML leverages cutting-edge AI technology to offer personalized health assessments.

Personalized AI Healthcare

Our platform simplifies the healthcare journey.

Streamlined Experience

We believe in the power of data. SquareML provides data-driven insights that help you.


SquareML’s subscription-based pricing models align with the budget constraints of healthcare companies.


Users can integrate with existing data giving users one workspace across the AI life-cycle.​​​

Rapid Deployment​​

Realize value quickly with a platform designed to deliver accelerated deployment.

Multi-Source Integrations

Offers integration with various healthcare sources, facilitating seamless data ingestion and analysis

Compliance and Security

SquareML adheres to strict regulatory requirements such as HIPAA to ensure data protection.


SquareML’s ability to handle increasing computational demands without sacrificing performance.

Why Choose SquareML?
bottom of page