AI-Assisted Telemedicine and Virtual Health

Module: Healthcare AI | Difficulty: Advanced

Triage Algorithm

Clinical Decision Tree

Conversation Quality Metrics

import torch
import torch.nn as nn

class MedicalChatbot(nn.Module):
    def __init__(self, vocab_size=30000, hidden_dim=256, num_intents=20):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, hidden_dim)
        self.lstm = nn.LSTM(hidden_dim, hidden_dim, batch_first=True, bidirectional=True)
        self.intent_head = nn.Linear(hidden_dim * 2, num_intents)
        self.entity_head = nn.Linear(hidden_dim * 2, 10)
        self.response_head = nn.Linear(hidden_dim * 2, vocab_size)

    def forward(self, input_ids):
        embedded = self.embedding(input_ids)
        lstm_out, _ = self.lstm(embedded)
        context = lstm_out[:, -1, :]
        intents = self.intent_head(context)
        entities = self.entity_head(lstm_out)
        response = self.response_head(context)
        return intents, entities, response

class TriageBot(nn.Module):
    def __init__(self, input_dim=100, hidden_dim=64):
        super().__init__()
        self.symptom_encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim), nn.ReLU(),
            nn.Dropout(0.3), nn.Linear(hidden_dim, hidden_dim))
        self.urgency_head = nn.Linear(hidden_dim, 5)
        self.specialty_head = nn.Linear(hidden_dim, 20)

    def forward(self, symptoms):
        encoded = self.symptom_encoder(symptoms)
        urgency = self.urgency_head(encoded)
        specialty = self.specialty_head(encoded)
        return urgency, specialty

chatbot = MedicalChatbot()
input_ids = torch.randint(0, 30000, (1, 100))
intents, entities, response = chatbot(input_ids)
print(f'Intents: {intents.shape}, Entities: {entities.shape}')

triage_bot = TriageBot()
symptoms = torch.randn(1, 100)
urgency, specialty = triage_bot(symptoms)
print(f'Urgency: {urgency.shape}, Specialty: {specialty.shape}')

Research Insight: Telemedicine AI must handle the full spectrum of patient interactions while maintaining safety. The most successful approaches use a hybrid architecture: a fast triage model for initial assessment, followed by a more detailed diagnostic model for complex cases. Human-in-the-loop design ensures that AI recommendations are reviewed by clinicians before final decisions.