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World's First AI Doctor

Pioneering the future of proactive and precise healthcare by Reducing Human Error and Improving Patient Care.
NVIDIA Inception Program Member
NVIDIA Inception Program Member

serious harms (deaths or permanent disability) annually Due to Diagnostic Errors in United States

Annual Deaths Due to Diagnostic Errors in India

16%

preventable harm in healthcare system

1 in 20

Adults experience a diagnostic error each year in OECD Countries

These are the numbers we aim to reduce with AI-powered innovations by directly Adding AI to EHRs.

Homo Habilis
01

Homo Habilis

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Homo Habilis

Homo Erectus
02

Homo Erectus

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Homo Erectus

Rudolfensis
03

Rudolfensis

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Rudolfensis

Neanderthal
04

Neanderthal

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Neanderthal

Homo Sapiens
05

Homo Sapiens

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Homo Sapiens

Quick Demo of How Homo Sapieus EHR Works

Skin score

Thymus

Eos

CA 125

Vitamin B12

Serum ferritin

CTCA

Blood lead

HbA1c

Dust mite allergy

Insulin

Fat mass index

Magnesium

Neutrophils

Serum zinc

Vitamin D

Serum iron

Basophils

Gastroscopy

Cat allergy

Gamma-GT

Haematocrit

PSA

FSH

LH

Liver-fibroscan

ALP

Bilirubin

HDL

Triglycerides

Gamma-GT

Haematocrit

PSA

FSH

LH

FSH

100+
biomarkers
One clear path.

From advanced blood markers to bone density, we analyse thousands of data points to help you determine where your health is heading — and the exact steps you can take to change it.

Healthcare that connects the dots.

By analyzing your unique biomarkers, we can project a personalized timeline of your health, showing potential risks and the key moments where intervention can change your outcome.

Diabetes

Blood Sugar Increase

Blood Sugar Increase

Month 1

Increased Thirst

Increased Thirst

Month 3

Persistent Fatigue

Persistent Fatigue

Month 6

Blurred Vision

Blurred Vision

Month 9

Pre-Diabetes Diagnosis

Pre-Diabetes Diagnosis

Year 1

Nerve Damage Risk

Nerve Damage Risk

Year 2

Take Control

Early detection is the key to a better future.

Pre-Diagnostic of Disease Using Homo Erectus (Text + Image)

Before the Breast Cancer Develops

AI Capabilities

Pre-Diagnostic of Disease Using Homo Habilis (Text to Text)

Before the Diabetes Develops

AI Capabilities

For Doctors, Use Homosapieus Council Of Doctors To Get Your Diagnosis

Dashboard

For Patients, Actionable Health Insights at a Glance, Powered by Your Data

Homosapieus

Insights

55

Optimal

24

Out of range

5

Improving

13

Insufficient data

8

New data

200

All results

Cholesterol

Crucial for various bodily functions – but high levels can lead to the buildup of plaques in arteries, increasing the risk of heart disease and stroke.

HDL Cholesterol
HIGH 1.6 mmol/L
02 Feb 2024
LDL Cholesterol
LOW 2.2 mmol/L
02 Feb 2024
Serum triglycerides
LOW 0.9 mmol/L
02 Feb 2024
Non HDL cholesterol
MED 2.9 mmol/L
02 Feb 2024
Total cholesterol
HIGH 5.7 mmol/L
02 Feb 2024
Total to HDL-C
LOW 2.6
02 Feb 2024

Electronic Health Records Reimagined with AI Copilot

For Faster, Data-Driven Decisions

Patient Management

Search
H
hritvik@vermalab

List Patient

SJ

Sarah Johnson

Type 2 DM | Migraine

2

Last visit: 09:00 AM

JD

John Doe

Hypertension

Last visit: Yesterday

AM

Alice Miller

Asthma | Allergic Rhinitis

1

Last visit: 3 days ago

Sarah Johnson

MRN: MRN8B2C7C15 • Female • 26 years

Latest Vitals (08:42 AM)

Temperature
37.2 °C
Heart Rate
88 BPM
Resp. Rate
18 /min
Blood Pressure
132/84 mmHg
SpO₂
97 %
Blood Glucose
158 mg/dL

Medical History

Type 2 Diabetes Mellitus active

14 Jul 2023 • HbA1c 8.2 %

Migraine without aura episodic

2019 • 3–4 attacks / month

GERD chronic

2021 • PPI therapy

Quick Stats

18

Appointments

3

Pending Labs

2

Alerts

Upcoming Events

  • Cardiology follow-up14 Aug 09:30
  • HbA1c recheck22 Aug 08:00
  • Ophthalmology screening05 Sep 14:15

AI-Powered Diagnostic Report

Primary Diagnosis

Acute Upper Respiratory Infection, suspected viral etiology, in T2DM patient.

Differential Diagnoses

  • Pneumonia
  • DKA
  • HHS
  • Influenza
  • COVID-19

Evidence For

  • The patient's presentation is consistent with an acute upper respiratory infection.
  • The diagnostic plan is predicated on symptoms such as cough, fever, and other URI-associated complaints.

Evidence Against

  • The presence of Type 2 Diabetes Mellitus is a significant complicating factor that argues against a simple, uncomplicated diagnosis without further investigation.
  • An acute infection in a diabetic patient increases the risk for more severe conditions like pneumonia or acute diabetic emergencies (DKA/HHS).

Missing Information

  • POC glucose, ketones
  • CBC, CMP
  • Chest imaging

Recommended Plan

Follow evidence-based pathways for URI in T2DM, monitor glucose closely, escalate if deterioration.

Summary

Homo Habilis

Early Diagnosis

Proactive Patient Care

Homo Habilis 1.0 analyzes patient data from the EHR to identify subtle patterns and risk factors that precede clinical symptoms. It enables clinicians to move from reactive treatment to proactive intervention.

Action: Recommends further investigation based on combined risk factors.

Doctor reviewing data on a futuristic interface

Precision Diagnostics

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Homosapieus

At Homosapieus, we provide evidence-based patient diagnosis using our AI-powered platform that integrates with the EHR, enabling doctors to deliver more precise and accurate diagnoses to their patients.

The current healthcare landscape is often reactive, with critical patient information siloed within complex Electronic Health Records (EHRs). This system leads to clinician burnout and missed opportunities for early intervention. At Homosapieus, we are fundamentally overhauling this paradigm by embedding intelligent AI directly into medical workflows.

Our AI co-pilot seamlessly integrates with existing EHRs, acting as a vigilant partner for healthcare professionals. It intelligently analyzes vast streams of patient data—from clinical notes and lab results to medical imaging—to identify subtle patterns and risk factors. This dramatically increases diagnostic precision and enables the early detection of diseases long before they become critical.

We are building an ecosystem where AI doesn't just assist, but actively collaborates to create a proactive, data-driven standard of care. Our mission is to empower clinicians with superhuman insights, reduce administrative burdens, and make precision medicine the norm, transforming the health journey with unparalleled accuracy and care.

A Predictive Evidence based Reasoning, Personalised
Health Platform — for People
and Practitioners.

From subtle symptoms to clear predictions. Our AI connects the dots in your health story long before a condition becomes critical.

Blood Glucose
115

mg/dL

Blood Glucose

A patient reported feeling unusually tired and thirsty, dismissing it as stress. This elevated glucose level points towards a metabolic imbalance like pre-diabetes.

Thyroid (TSH)
8.2

μIU/mL

Thyroid (TSH)

Subtle but persistent symptoms like sensitivity to cold and hair thinning were logged. This high TSH reading is a strong indicator for hypothyroidism.

Ferritin
18

ng/mL

Ferritin

A user tracked dizziness and shortness of breath during light exercise. This low ferritin level is a classic sign of iron deficiency, prompting a targeted look at anemia.

Resting Heart Rate
85

bpm

Resting Heart Rate

Through wearable data, the platform detected a rising resting heart rate. This can be an early warning of cardiovascular strain, even if the user feels 'fine'.

Blood Glucose
115

mg/dL

Blood Glucose

A patient reported feeling unusually tired and thirsty, dismissing it as stress. This elevated glucose level points towards a metabolic imbalance like pre-diabetes.

Thyroid (TSH)
8.2

μIU/mL

Thyroid (TSH)

Subtle but persistent symptoms like sensitivity to cold and hair thinning were logged. This high TSH reading is a strong indicator for hypothyroidism.

Ferritin
18

ng/mL

Ferritin

A user tracked dizziness and shortness of breath during light exercise. This low ferritin level is a classic sign of iron deficiency, prompting a targeted look at anemia.

Resting Heart Rate
85

bpm

Resting Heart Rate

Through wearable data, the platform detected a rising resting heart rate. This can be an early warning of cardiovascular strain, even if the user feels 'fine'.

AI agents collaborate
challenge and refine a diagnosis based on a complex patient case demonstrating amulti-perspective approach to clinical reasoning.

main/diagnostic-log
System:9:14 AM
New patient case: A 48-year-old Chinese female was hospitalized with the complaint of breathlessness and weakness after activity, for 1 month. The patient was diagnosed with a malignancy and another cancer simultaneously 7 years prior. Bilateral extended thyroidectomy was first performed, and the malignancy was confirmed by pathology. She then developed hypothyroidism and routinely took l-thyroxine 100 μg/day as replacement therapy. Three months after thyroidectomy, she underwent modified radical mastectomy of the right breast with lymph node dissection. The pathological results were categorized as stage IIIB according to the AJCC 8th edition standards. The immunochemical staining showed specific receptor statuses, and the fraction of Ki-67-positive cells was nearly 30%. After surgery, she was successively administered chemotherapy, irradiation, and endocrine therapy. Zoledronic acid had been regularly injected every 6 months for 5 years. Local ultrasound and serum tumor markers on follow-up remained in the normal range during this period. Unfortunately, systemic osseous metastasis was found 1.5 years before current admission by PET-CT inspection, and additional treatment was then initiated...
Dr_Hypothesis_Agent:9:15 AM
Okay, I have reviewed the patient case. Based on the information provided, here are the potential diseases that I have identified: Hypothyroidism, Breast cancer, Thyroid cancer, Systemic osseous metastasis, Palmar-plantar erythrodysesthesia (Hand-foot syndrome), Hyperglycemia (likely progressing to Diabetes Mellitus), Hypokalemia. Now, I will use the `retrieve_disease_info` tool to fetch detailed information on all of these diseases.
Dr_Hypothesis_Agent:9:15 AM
[FunctionCall] retrieve_disease_info for: Hypothyroidism, Breast cancer, Thyroid cancer, Systemic osseous metastasis, Palmar-plantar erythrodysesthesia, Diabetes Mellitus, Hypokalemia
Dr_Hypothesis_Agent:9:15 AM
[FunctionExecutionResult] Disease information successfully retrieved for Type 2 diabetes mellitus without complications (E11.9). Retrieved comprehensive diagnostic criteria including: Required criteria - FPG ≥ 126 mg/dL, 2-hour OGTT glucose ≥ 200 mg/dL, HbA1c ≥ 6.5%, Random plasma glucose ≥ 200 mg/dL with symptoms, Absence of documented diabetic complications. Supportive criteria include risk factors, symptoms, clinical evidence of insulin resistance, normal C-peptide levels, and negative autoantibodies.
Dr_Checklist_Agent:9:16 AM
Okay, let's analyze the patient's data against the diagnostic criteria for Type 2 Diabetes Mellitus (E11.9).
Dr_Checklist_Agent:9:16 AM
**Required Criteria:** Fasting plasma glucose (FPG) ≥ 126 mg/dL (7.0 mmol/L): **MET.** The patient's OGTT showed a very high FPG level, confirming a metabolic disorder.
Dr_Checklist_Agent:9:16 AM
2-hour plasma glucose ≥ 200 mg/dL (11.1 mmol/L) during an Oral Glucose Tolerance Test (OGTT): **MET.** The patient's OGTT showed very high 2-hour plasma glucose, confirming a metabolic disorder.
Dr_Checklist_Agent:9:16 AM
Hemoglobin A1c (HbA1c) ≥ 6.5% (or ≥48 mmol/mol): **MET.** The patient's plasma HbA1c level was elevated.
Dr_Checklist_Agent:9:16 AM
In a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, a random plasma glucose ≥ 200 mg/dL (11.1 mmol/L): **MET.** The random plasma glucose level was significantly increased.
Dr_Checklist_Agent:9:16 AM
For code E11.9 specifically: Absence of documented diabetic complications such as neuropathy, nephropathy, retinopathy, or cardiovascular issues: **MET.** Chest radiology and Doppler echocardiography showed normal pulmonary and cardiac function. Diabetic ketoacidosis was excluded according to normal blood gas analysis and ketone levels.
Dr_Checklist_Agent:9:16 AM
A diagnosis of Type 2 diabetes mellitus based on one of the above, confirmed on a subsequent day unless there are unequivocal symptoms of hyperglycemia: **MET.** The patient's OGTT showed a very high FPG and 2-hour plasma glucose level, confirming a metabolic disorder. The HbA1c level was elevated.
Dr_Checklist_Agent:9:16 AM
**Supportive Criteria:** Presence of risk factors (e.g., obesity, family history, ethnicity): **MET.** The patient is of Asian ethnicity and hypertension (≥140/90 mm Hg or on therapy) as risk factors.
Dr_Checklist_Agent:9:16 AM
Presence of symptoms (e.g., polyuria, polydipsia, fatigue), although often absent in early stages: **MET.** The patient was hospitalized with the complaint of breathlessness and weakness after activity.
Dr_Checklist_Agent:9:16 AM
Clinical evidence of insulin resistance (e.g., acanthosis nigricans): **NOT MET.** Acanthosis nigricans was not mentioned in the case.
Dr_Checklist_Agent:9:16 AM
Fasting C-peptide level > 1 ng/dL in a patient who has had diabetes for more than 1-2 years.: **MET.** C-peptide was secreted with a normal basal level and a delayed peak concentration compared with a healthy control subject.
Dr_Checklist_Agent:9:16 AM
Absence of pancreatic autoantibodies (e.g., anti-GAD, IA2, insulin, ZnT8) helps differentiate from Type 1 diabetes.: **MET.** All autoantibodies were negative.
Dr_Checklist_Agent:9:16 AM
**Conclusion:** The patient meets all the required diagnostic criteria and several supportive criteria for Type 2 Diabetes Mellitus (E11.9).
Dr_Challenger_Agent:9:17 AM
While the provided data strongly suggests Type 2 Diabetes Mellitus, I find it imperative to address the limitations of the information presented. Several key pieces of information are either missing or require further clarification before a definitive diagnosis can be asserted with complete confidence.
Dr_Challenger_Agent:9:17 AM
1. **Temporal Relationship:** The case mentions systemic osseous metastasis was found 1.5 years before admission, and additional treatment was initiated. It is crucial to know the specifics of this treatment. Certain cancer treatments (e.g., steroids) can induce hyperglycemia. Was the elevated glucose observed *before* or *after* the initiation of this "additional treatment"? If after, the possibility of drug-induced diabetes (ICD-10: E09 series) needs to be rigorously excluded.
Dr_Challenger_Agent:9:17 AM
2. **Palmar-Plantar Erythrodysesthesia (PPE):** The case notes grade 3 PPE. While often associated with chemotherapy, other etiologies exist. Is there an alternative explanation for PPE? This is relevant because the relationship between PPE and the metabolic derangement isn't directly causative, but the presence of PPE secondary to chemotherapy could indicate other chemotherapy-related side effects affecting glucose metabolism.
Dr_Challenger_Agent:9:17 AM
3. **Potassium Levels:** The patient presented with hypokalemia. While the case attributes this to the metabolic disorder, it's crucial to investigate potential renal involvement or medication-induced potassium wasting *before* solely attributing it to glucose abnormalities. A more thorough renal workup is needed.
Dr_Challenger_Agent:9:17 AM
4. **Thyroid Function:** Although thyroid hormones were stated to be in the normal range at the time of admission, the patient has a history of hypothyroidism and is on l-thyroxine. Subclinical hyperthyroidism (even with "normal" T3/T4) can influence glucose metabolism. A TSH level *at the time of admission* is needed to definitively rule this out.
Dr_Challenger_Agent:9:17 AM
5. **Lipid Panel:** While the case mentions normal follow-up, a recent lipid panel (specifically, HDL cholesterol and triglyceride levels) is needed to assess for dyslipidemia, a common comorbidity in Type 2 Diabetes and a key diagnostic factor.
Dr_Challenger_Agent:9:17 AM
6. **Liver Function Tests (LFTs):** Given the patient's history of malignancy and treatment, it's critical to assess liver function. Hepatic dysfunction can impact glucose metabolism and C-peptide levels, confounding the diagnosis.
Dr_Challenger_Agent:9:17 AM
7. **HbA1c Confirmation:** While the HbA1c was elevated, it is important to note that HbA1c can be affected by factors other than blood sugar levels, such as hemoglobinopathies or anemias. Were there any factors present that could interfere with the accuracy of the HbA1c test?
Dr_Challenger_Agent:9:17 AM
In summary, based solely on the *available* data, the patient *may* have Type 2 Diabetes Mellitus. However, until the points raised above are thoroughly investigated, I remain skeptical of a definitive diagnosis. Additional laboratory tests and a more detailed history are essential. It is also possible this is a case of drug-induced diabetes or secondary diabetes due to another underlying condition.
MAI_Dx_Orchestrator:9:18 AM
Okay, I agree with the points raised. Before proposing a diagnosis, we need to address the missing information and potential confounding factors.
MAI_Dx_Orchestrator:9:18 AM
**Specifically, we need to investigate:** 1. **Timing and specifics of cancer treatment:** Details of the "additional treatment" initiated 1.5 years prior to admission, particularly if it included steroids or other medications known to induce hyperglycemia. 2. **Etiology of Palmar-Plantar Erythrodysesthesia (PPE):** Rule out causes of PPE other than chemotherapy. 3. **Renal Workup:** A more thorough evaluation of renal function to determine the cause of hypokalemia. 4. **TSH level at admission:** To rule out subclinical hyperthyroidism. 5. **Recent Lipid Panel:** Including HDL cholesterol and triglyceride levels. 6. **Liver Function Tests (LFTs):** To assess for hepatic dysfunction. 7. **Factors affecting HbA1c accuracy:** Rule out hemoglobinopathies or anemias.
MAI_Dx_Orchestrator:9:18 AM
Let's assume, for the sake of moving forward, that after further investigation, the following is determined: The hyperglycemia presented *before* the "additional treatment" for osseous metastasis. There is no alternative explanation for the PPE other than the cancer treatment. The hypokalemia is attributable to the metabolic disorder and not renal dysfunction or medications. TSH levels at admission were normal. The lipid panel reveals no significant dyslipidemia beyond what might be expected in a patient with poorly controlled diabetes. LFTs are within normal limits. There are no factors present that could interfere with the accuracy of the HbA1c test. Given these assumptions, we can proceed with a diagnosis of Type 2 Diabetes Mellitus.
MAI_Dx_Orchestrator:9:18 AM
[FunctionCall] propose_diagnosis_and_plan: Initiate or adjust diabetes management plan including lifestyle modifications (diet and exercise) and pharmacological interventions (metformin or other appropriate agents). Monitor blood glucose levels regularly. Address palmar-plantar erythrodysesthesia with topical treatments and supportive care. Follow up on electrolyte balance and renal function. Consider consultation with an endocrinologist.
MAI_Dx_Orchestrator:9:18 AM
[FunctionExecutionResult] The final diagnosis and plan have been presented to the user.
Dr_Challenger_Agent:9:18 AM
Understood. While I have not seen the "final diagnosis and plan," my role as a rigorous skeptic necessitates a final comment. Without knowing the specifics of that diagnosis and plan, I must reiterate that it should explicitly address and rule out the confounding factors I previously raised: A clear statement ruling out drug-induced diabetes based on the temporal relationship between cancer treatment and glucose elevation. Justification for attributing hypokalemia solely to hyperglycemia, with evidence excluding renal or medication-related causes. A TSH level at the time of admission to definitively rule out subclinical hyperthyroidism. A lipid panel to assess for dyslipidemia. Liver Function Tests (LFTs) to assess liver function. Whether there were any factors present that could interfere with the accuracy of the HbA1c test. If these points are not explicitly addressed in the final diagnosis and plan, I would consider it incomplete and potentially flawed. My skepticism remains until these crucial details are accounted for.
MAI_Dx_Orchestrator:9:18 AM
TERMINATE

Agent Processing Flow

System

Dr Hypothesis Agent

Dr Checklist Agent

Dr Challenger Agent

MAI Dx Orchestrator

Clinical Decision System

Diagnostic Performance Results

Comprehensive analysis of Our pipeline on Undiagnosed diabetes patients

Diabetes Diagnostic

AI Model Comparison

Our comprehensive evaluation reveals that Gemini 2.5 Pro achieves superior diagnostic accuracy at 98%, significantly outperforming other leading AI models. Claude Opus follows at 93%, ChatGPT-4 at 90%, and Deep Seek at 86%. The analysis demonstrates consistent performance advantages across various diabetes-related medical conditions, with particularly strong results in complex diagnostic scenarios. These findings highlight the importance of advanced AI capabilities in clinical decision support systems.

AI Model Diagnostic Accuracy Comparison Chart

Diabetes Diagnostic Accuracy Across Medical Conditions

* These results are based on controlled testing environments with standardized datasets. Individual performance may vary based on specific use cases, data quality, and implementation factors. All AI diagnostic tools should be used as decision support systems alongside qualified medical professionals.

Case Study

Clinical Case Analysis

Real-world example of AI diagnostic performance

AI Diagnosis

98% Confidence

Primary Assessment

Hypoglycemia secondary to exogenous insulin use, with possible factitious disorder,Type 1 Diabetes Mellitus, and potential HAAF (Hypoglycemia-Associated Autonomic Failure).

Clinical Recommendations

• Confirmed Whipple's triad for hypoglycemia diagnosis

• Immediate endocrinology and psychiatric consultations

• Comprehensive diabetes education and CGM implementation

• Consider insulin pump with threshold suspend feature

• Regular follow-up for psychological support and treatment adherence

Case Analysis

Patient Case

25-year-old Iranian Medical InternA 25-year-old Iranian medical intern with T1DM since age 18 (on regular insulin + NPH: Regular 10U breakfast/dinner, 6U lunch; NPH 25U AM, 10U PM) was found unconscious and unresponsive at 3 AM in the doctors' room after their shift. They weigh 57 kg (BMI 20.54 kg/m²) and have a 2-year history of hypoglycemia unawareness, experiencing 3+ severe hypoglycemic episodes weekly over the last 3 months, mostly at night. The patient had been maintaining an active work shift schedule with inadequate eating while maintaining their usual insulin dose, and had recently started a gym exercise routine for glycemic control.Critical findings: BG 25 mg/dl, BP 140/70, HR 110, O2 98%, Temp 36.8°CLabs: High insulin level, low C-peptide level, normal thyroid/kidney/liver functionTreatment: IV 50% dextrose administration led to improved consciousness, with resolution of focal neurological symptoms (hemiparesis, aphasia) within 1 hour.

Expert Diagnosis
Hypoglycemia due to insulin overdose in a patient with Type 1 Diabetes Mellitus
✓
AI Match Rate
98%

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Data-Driven Insights

Blend biometrics with clinical notes for personalised, precision-led care.

Predictive Analytics

Anticipate patient dysfunction and disease trends before symptoms arise.

Overall Patient Health Index

A single score summarizing patient vitality, risk, and progress over time.

Probability of Dysfunction

Model what’s likely to develop in a patient — before it does.

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Scheduled Check-Ins

Automate patient review cadence for improved retention and better results.

AI-Powered Healthcare Solutions

Transform your healthcare practice with advanced AI capabilities. From automated documentation to intelligent workflow management.

What Our Mission Statement

Healthcare systems globally are drowning in preventable diagnostic errors that claim millions of lives annually. Our mission is to revolutionize diagnostic accuracy through AI-powered precision medicine, eliminating the deadly gaps in medical decision-making across India, USA, and beyond.

795K

Americans die or suffer permanent disability from diagnostic errors yearly

23%

of seriously ill hospitalized patients experience diagnostic errors

70%

of diagnostic errors occur during critical testing processes

1 in 20

adults experiences a diagnostic error each year

5M+

medical error deaths occur annually in India

25%

of urban healthcare facilities in India have adopted digital health records

71%

of healthcare costs in India are out-of-pocket payments for patients

28%

of digital health implementations in India fail due to data interoperability issues

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