| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Accepts medical records in DICOM, PDF, image, and printed document formats via web upload or phone camera, automatically extracting structured health data (test results, prescriptions, diagnoses) using a combination of proprietary image neural networks for visual content and OCR-based text extraction. The system normalizes heterogeneous input formats into a unified internal representation for downstream AI analysis, handling variable image quality from phone photos to professional medical prints.
Unique: Combines proprietary image neural networks with OCR and DICOM parsing to handle heterogeneous medical record formats (professional imaging, PDFs, phone photos, prints) in a single unified pipeline, normalizing outputs for AI analysis — most competitors require standardized digital formats or manual data entry
vs alternatives: Broader input format support than most health AI tools (accepts phone photos and prints, not just digital records), reducing friction for users in regions with limited digital healthcare infrastructure
Provides conversational Q&A interface over uploaded medical records using GPT-3.5, GPT-4, and Google Gemini as interchangeable backend models, with free tier restricted to GPT-3.5/Gemini and paid tier unlocking GPT-4 access. The system retrieves relevant sections from stored medical records in response to user queries, though the exact retrieval mechanism (RAG, semantic search, or keyword matching) is undocumented. Supports 40 languages for query input and response generation.
Unique: Implements model abstraction layer allowing users to switch between GPT-3.5, GPT-4, and Gemini backends with pricing-based access control (free tier limited to weaker models), with 40-language support for both input and output — most health AI tools lock users into single-model ecosystems
vs alternatives: Broader language support (40 languages) than most medical AI tools (typically English-only or 5-10 languages), making it more accessible to non-English-speaking populations in underserved regions
Implements pricing-based access control to AI models, with free tier restricted to GPT-3.5 and Google Gemini, while paid tier unlocks GPT-4 access. Users can select which model to use for analysis (if multiple are available in their tier), with model choice affecting response quality and potentially latency. The pricing structure and tier definitions are not publicly documented.
Unique: Implements transparent model abstraction layer with pricing-based access control, allowing users to understand which model they're using and upgrade for better performance — most health AI tools hide model selection and lock users into single-model ecosystems
vs alternatives: Explicit model selection with tiered access enables cost-conscious users to start free while offering upgrade path for higher-quality analysis, compared to competitors with fixed model choices
Supports analysis of NHS app screenshots and UK-specific medical record formats, enabling British users to upload records directly from the NHS digital health platform. The system recognizes NHS-specific data structures and can extract information from NHS app screenshots without requiring manual transcription.
Unique: Implements NHS app screenshot recognition and extraction, enabling UK patients to directly upload NHS digital records without manual transcription — most health AI tools don't support NHS-specific formats or screenshot extraction
vs alternatives: Direct NHS app integration reduces friction for UK users by eliminating manual data entry from NHS digital health platform
Announced but not yet live feature providing AI-based psychiatric consultation and mental health analysis. The system will analyze mental health symptoms and provide preliminary psychiatric guidance, though implementation details, model architecture, and launch timeline are undocumented. Feature status is 'coming soon' with no ETA.
Unique: Announced feature for AI-based psychiatric consultation, extending health analysis beyond physical medicine to mental health — most health AI tools focus on physical health analysis only
vs alternatives: Planned psychiatric AI would differentiate from physical-health-only competitors, but feature is not yet live and carries vaporware risk
Analyzes uploaded medical records and user queries to identify potential drug-drug interactions, contraindications, and medication safety concerns by cross-referencing extracted medication lists against an undocumented drug interaction database. The system integrates with the chatbot interface, allowing users to ask about specific medication combinations or receive proactive warnings based on their prescription history.
Unique: Integrates medication extraction from multiformat medical records with real-time interaction checking via LLM-mediated chatbot, allowing conversational queries about drug combinations rather than requiring structured input — most drug interaction tools require manual medication entry or API integration
vs alternatives: Automatically extracts medications from uploaded records rather than requiring manual entry, reducing friction for users with complex medication histories
Analyzes extracted blood test values from medical records using LLM-based interpretation, providing context-aware explanations of test results (normal/abnormal ranges, clinical significance, potential causes of abnormalities). The system compares values against reference ranges and generates natural language summaries of findings, supporting multi-test analysis when multiple lab reports are uploaded.
Unique: Combines automated extraction of lab values from multiformat records with LLM-based contextual interpretation, generating natural language summaries of clinical significance — most lab analysis tools either require manual value entry or provide only reference range comparisons without clinical context
vs alternatives: Provides clinical interpretation beyond simple reference range comparison, explaining what abnormal values might indicate and their potential significance
Offers optional human expert review of uploaded medical records and AI analysis, with a licensed medical team generating detailed reports that synthesize AI findings with professional clinical judgment. The exact workflow (manual review, AI-assisted review, or hybrid) is undocumented, as are SLAs, pricing, and which medical specialties are available. Reports are generated asynchronously with unknown turnaround time.
Unique: Implements human-in-the-loop workflow where licensed medical experts review and synthesize AI analysis of medical records, generating credible reports for medical-legal use — most health AI tools provide AI-only analysis without professional verification pathway
vs alternatives: Adds professional medical credibility through expert review, enabling reports suitable for insurance, employment, or legal purposes where AI-only analysis would lack authority
+5 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Health Scanner at 41/100. Health Scanner leads on adoption and quality, while Supabase is stronger on ecosystem.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities