Agentic AI Comparison:
HIA (Health Insights Agent) vs Paige AI
vs
Introduction
This report provides a detailed comparison between HIA (Health Insights Agent), an open-source health AI tool, and Paige AI, a clinical-grade digital pathology platform for cancer diagnostics, across key metrics: autonomy, ease of use, flexibility, cost, and popularity. Scores are rated 1-10 (higher is better) based on available data from GitHub, deployments, partnerships, and industry recognition.
Overview
HIA (Health Insights Agent)
HIA is an open-source Health Insights Agent available on GitHub (https://github.com/harshhh28/hia) and deployed as a Streamlit app (https://hiahealth.streamlit.app). It provides health-related insights likely through a user-friendly web interface for querying health data or generating recommendations, targeting general health analysis as an accessible AI agent.
Paige AI
Paige AI is an enterprise AI-powered digital pathology platform specializing in cancer detection, biomarker analysis, and workflow automation for pathologists. It leverages foundation models trained on millions of histopathology slides, with FDA-cleared applications like Paige Prostate Detect, and major integrations with Microsoft Azure, Epic, and healthcare systems.
Metrics Comparison
autonomy
HIA (Health Insights Agent): 6
As a standalone Streamlit app, HIA operates independently for health queries without needing extensive setup, but lacks advanced clinical autonomy like fully automated diagnostics seen in enterprise tools.
Paige AI: 9
High autonomy in clinical workflows, automating cancer detection, subtyping, and multimodal analysis with FDA-cleared models that function independently in pathology labs.
Paige AI excels in specialized clinical autonomy, while HIA offers basic independent operation for general insights.
ease of use
HIA (Health Insights Agent): 9
Streamlit-based web app enables instant access and simple interactions for users without technical expertise, ideal for quick health queries.
Paige AI: 7
User-friendly for pathologists with co-pilot workflows and cloud viewers like FullFocus, but requires healthcare IT integration and training for enterprise deployment.
HIA is easier for casual users; Paige AI prioritizes professional pathology workflows.
flexibility
HIA (Health Insights Agent): 8
Open-source GitHub repo allows full customization, forking, and adaptation for various health insights applications.
Paige AI: 7
Flexible for multimodal data (imaging, genomics) and customizable via Azure models, but constrained by clinical validation and regulatory compliance.
HIA offers greater open customization; Paige AI provides flexibility within regulated healthcare environments.
cost
HIA (Health Insights Agent): 10
Free open-source software with no licensing fees; runs on low-cost Streamlit hosting.
Paige AI: 4
Enterprise solution with high costs for deployment, Azure infrastructure (VMs, GPUs, AKS), and clinical subscriptions; not accessible for individuals.
HIA is vastly more affordable; Paige AI targets institutions with significant budgets.
popularity
HIA (Health Insights Agent): 3
Limited visibility as a niche GitHub project and Streamlit app with no widespread mentions or adoption metrics.
Paige AI: 9
High industry recognition with Microsoft partnerships, FDA clearances, migrations for petabyte-scale data, and collaborations with Epic, Providence; featured in major healthcare AI announcements.
Paige AI dominates in popularity and enterprise adoption; HIA remains an emerging open-source tool.
Conclusions
Paige AI outperforms in autonomy, popularity, and clinical relevance, making it ideal for professional pathology and cancer diagnostics in healthcare institutions. HIA shines in cost, ease of use, and flexibility for individual developers or general health insights, serving as an accessible entry-level agent. Selection depends on use case: enterprise pathology favors Paige AI, while prototyping or personal use suits HIA.