Machine Learning Overview

Diagnostic Analysis

Our diagnostic models analyze patient symptoms, medical history, demographics, and risk factors to generate:

• Primary diagnosis recommendation with confidence score
• Differential diagnoses ranked by probability
• Explanation of reasoning for each diagnosis
• Suggested follow-up questions to increase diagnostic accuracy
• Recommended diagnostic tests with clinical rationale

Medical Imaging Analysis

Our imaging models can analyze various medical images to detect:

• Abnormalities and pathological findings
• Anatomical segmentation and measurements
• Disease progression compared to prior studies
• Incidental findings requiring attention

Currently supporting: chest X-rays, abdominal CT, brain MRI, dermatological images, and retinal imaging.

Treatment Recommendations

Based on diagnosis and patient context, our models provide:

• Evidence-based treatment options ranked by efficacy
• Medication suggestions with dosage considerations
• Potential drug interactions and contraindications
• Non-pharmacological interventions
• Follow-up and monitoring recommendations

Risk Stratification

Our predictive models assess patient risk for:

• Disease progression trajectories
• Hospitalization or readmission likelihood
• Complications from specific conditions
• Response to different treatment options

Evidence Tracing

All model outputs include references to the specific evidence considered:

{
  "primary_diagnosis": {
    "condition": "Community-Acquired Pneumonia",
    "confidence_score": 0.87,
    "icd10_code": "J18.9",
    "evidence": [
      {
        "factor": "Fever (38.5°C)",
        "contribution": "positive",
        "weight": 0.65
      },
      {
        "factor": "Cough with sputum production",
        "contribution": "positive",
        "weight": 0.72
      },
      {
        "factor": "Crackles on auscultation",
        "contribution": "positive",
        "weight": 0.83
      },
      {
        "factor": "Recent travel history",
        "contribution": "negative",
        "weight": -0.15
      }
    ],
    "references": [
      {
        "title": "Community-Acquired Pneumonia in Adults: Diagnosis and Management",
        "source": "American Family Physician",
        "year": 2022,
        "url": "https://www.aafp.org/pubs/afp/issues/2022/..."
      }
    ]
  }
}

Feature Attribution

For imaging analysis, we provide visual overlays highlighting the regions contributing to the model's conclusion:

Confidence Reporting

All predictions include calibrated confidence scores and uncertainty estimates:

Model Cards

For each model, we provide detailed documentation about:

• Training methodology and datasets
• Performance characteristics across different patient demographics
• Known limitations and edge cases
• Validation procedures and results
• Safety guardrails and monitoring

Bias Mitigation

We employ multiple strategies to detect and mitigate biases in our models:

• Diverse and representative training data across demographics
• Regular fairness audits across different patient populations
• Bias detection algorithms integrated into the model pipeline
• Continuous monitoring of performance across demographic groups
• Adjustment of model weights to ensure equitable performance

Privacy Protection

Our models are designed with privacy at their core:

• Trained on anonymized and de-identified data
• Differential privacy techniques applied during training
• No patient-specific data stored within model parameters
• HIPAA-compliant data handling and processing

Human Oversight

We maintain rigorous human oversight of our AI systems:

• Clinical advisory board reviews all model updates
• Regular clinician review of model outputs
• Anomaly detection systems flag unusual predictions for human review
• Clear escalation pathways for concerning model behaviors