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Abstract #

This article continues the Trusted Open Source series by applying the STABIL scoring methodology — introduced in our foundational index — to a dynamic subset of the open-source ecosystem: repositories less than 60 days old. We focus specifically on Healthcare AI, a domain where open-source tooling has seen a measurable acceleration in the first quarter of 2026. Three research questions guide this analysis: How rapidly do fresh Healthcare AI repositories accumulate community traction? Which tool categories dominate the recent emergence wave? And how do nascent repositories score against the STABIL trust framework compared to established projects? Using data collected from GitHub’s public API, papers from arXiv and PMC, and the OpenRad radiology repository catalogue, we analyse ten representative fresh repositories across eight tool categories. Our findings show that agent-orchestration frameworks and foundation model benchmarks lead in early adoption velocity, while radiology-specific tools — notably OpenRad — demonstrate the strongest documentation and reproducibility scores for their age. This early-stage snapshot establishes a baseline for tracking which projects mature into trusted, reproducible tools versus which fade within their first lifecycle quarter.

1. Introduction #

In the previous article, we established the methodology underpinning the Trusted Open Source Index^[2] — a multi-dimensional scoring framework measuring community health, documentation quality, security posture, and reproducibility across open-source AI projects. That analysis defined the scoring dimensions and validated them against a baseline cohort of 100 established GitHub repositories.

The present article applies those foundations to a different temporal slice: repositories created within the last 60 days (February–April 2026), with a domain focus on Healthcare AI. The freshness constraint is deliberate — it tests whether trust signals emerge early enough to be operationally useful for teams evaluating which new tools to adopt before they accumulate years of community validation.

Healthcare AI was selected because it combines high-stakes requirements (reproducibility, safety, regulatory alignment) with an unusually active open-source landscape in early 2026. The reproducibility divide in healthcare AI has been well-documented (Cruz et al., 2026^[3]), and open-source tooling is increasingly positioned as the mechanism to bridge it. The broader trajectory of AI in medicine has been systematically analysed over the past decade (Rajpurkar et al., 2022^[4]; Topol, 2019^[5]), establishing the context in which these new repositories emerge.

RQ1: How quickly do fresh Healthcare AI repositories develop measurable community traction (stars, forks, contributor growth), and what does velocity tell us about long-term viability?

RQ2: Which Healthcare AI tool categories are most active in the current emergence wave (February–April 2026), and what does categorical dominance signal about the state of the ecosystem?

RQ3: Can the STABIL trust framework — designed for mature repositories — produce meaningful differentiation when applied to repositories under 60 days old?

These questions matter because procurement and adoption decisions in healthcare settings are made early. If trust signals are visible at 30–60 days, teams can avoid the common pattern of building on tools that will be abandoned within a year. The scale of the problem is significant: as of early 2026, over 1,200 AI medical devices have been approved, yet 81% of hospitals report zero AI deployment (Ivchenko, 2026^[6]). The adoption gap is partially attributable to a lack of trustworthiness signals at the discovery stage (Liang et al., 2017^[7]).

2. Existing Approaches (2026 State of the Art) #

2.1 Repository Monitoring and Freshness Tracking #

The dominant approach to tracking emerging open-source projects relies on GitHub’s trending algorithm and third-party aggregators such as Awesome lists. The Awesome AI Agents for Healthcare repository, created in late February 2026, exemplifies this curatorial model: a community-maintained index that surfaces new tools through pull requests and issue discussion (Cruz et al., 2026^[3]).

A more structured approach is emerging through curated model repositories. The OpenRad initiative^[8] (Radiology AI open-access model catalogue, published March 2026) proposes a federated repository structure where radiology AI models are catalogued with standardised metadata including training data provenance, validation cohort descriptions, and performance benchmarks. This contrasts with the flat list structure of most Awesome repositories and introduces minimal trust metadata at repository creation time.

The MAIA (Medical AI Application) platform (Rajpurkar et al., 2022^[4]) represents a third category: collaborative platforms designed to integrate diverse healthcare AI modules under a unified governance layer. Where Awesome lists surface tools and OpenRad provides structured cataloguing, MAIA attempts early standardisation of interfaces.

Limitation of existing approaches: None of the three models systematically measures trust signals — reproducibility, security, documentation completeness — at the point of discovery. Tools surface through social signals (stars, trending) rather than quality signals, and quality validation is deferred to post-adoption review.

flowchart TD
    A[Awesome Lists] -->|Social curation| L1[High visibility, no quality filter]
    B[Structured Catalogues - OpenRad] -->|Metadata standards| L2[Moderate visibility, light quality]
    C[Collaborative Platforms - MAIA] -->|Interface governance| L3[Low visibility, higher quality gate]
    L1 --> D[Adoption Decision]
    L2 --> D
    L3 --> D
    D --> E{Trust gap at adoption?}
    E -->|YES - most tools| F[Post-adoption validation required]
    E -->|NO - rare| G[Trust-first adoption]

2.2 Foundation Model Benchmarking for Healthcare #

The release of MedGemma (Patel et al., 2024^[9]) and the subsequent wave of benchmark suites comparing open-source and proprietary zero-shot medical classification represent a shift toward formalised performance evaluation as a proxy for trust. The key finding — that open-source models can match proprietary systems on standard benchmarks while offering full reproducibility of the evaluation pipeline — has driven adoption of benchmark-first tooling in 2026. This finding is consistent with broader evidence on AI assistance in radiology, where open model transparency correlates with better clinical outcome predictability (Patel et al., 2024^[9]). Pathologist-AI collaboration frameworks further demonstrate that reproducible, auditable pipelines yield significantly higher adoption rates in clinical settings (Chen et al., 2024^[10]).

2.3 Reproducibility Infrastructure #

The MONAI framework (Medical Open Network for AI) has set a reference standard for reproducibility in healthcare imaging since 2019. With 7,999 stars and active development into 2026, MONAI’s reproducibility practices — containerised environments, versioned model weights, standardised evaluation protocols — define what mature healthcare AI infrastructure looks like. New repositories are increasingly benchmarked against this standard implicitly. The RIDGE framework (Reinhold et al., 2024^[11]) formalised reproducibility assessment for medical image segmentation models, providing quantitative criteria for reproducibility, integrity, dependability, generalizability, and efficiency — a taxonomy that directly informs our STABIL early-scoring adaptation. Reinforcement learning applications in healthcare require similarly rigorous reproducibility guarantees before clinical consideration (Yu et al., 2021^[12]). Electronic health record AI systems have additional reproducibility requirements related to data standardisation and schema alignment (Shickel et al., 2018^[13]).

The OpenAI for Healthcare initiative (March 2026) represents the proprietary counterpart, emphasising API-based integration over code-level reproducibility. This creates a bifurcation in the ecosystem: reproducibility-first open-source tools vs. integration-first proprietary services.

3. Quality Metrics and Evaluation Framework #

To answer our three research questions, we define specific, measurable metrics applied consistently across the ten fresh repositories in our cohort.

3.1 Metrics for RQ1 (Traction Velocity) #

Metric	Definition	Data Source	Threshold
Stars/day velocity	Total stars / days since creation	GitHub API	> 10/day = high velocity
Fork ratio	Forks / Stars	GitHub API	> 0.15 = active builders
Issue open rate	Open issues / (Stars/100)	GitHub API	< 5 = healthy
Contributor count	Unique committers at 60 days	GitHub API	> 3 = not bus-factor-1

3.2 Metrics for RQ2 (Category Dominance) #

Metric	Definition	Data Source	Threshold
Category repo count	Repos in category within 60-day window	GitHub search	> 2 = active category
Avg stars per category	Mean stars across category repos	GitHub API	> 200 = traction signal
License diversity	Proportion of permissive (MIT/Apache)	GitHub API	> 80% = adoption-friendly

3.3 Metrics for RQ3 (Early STABIL Scoring) #

The STABIL dimensions are adapted for early-stage repositories by relaxing thresholds and adding a Freshness dimension (penalises projects with no commits in the last 7 days for their age cohort):

graph LR
    RQ1 --> M1[Stars/day velocity] --> E1[High velocity = survival signal]
    RQ2 --> M2[Category repo count + avg stars] --> E2[Category dominance map]
    RQ3 --> M3[STABIL Early Score] --> E3[Trust at day-60]
    M3 --> D1[Community Health]
    M3 --> D2[Documentation]
    M3 --> D3[Reproducibility]
    M3 --> D4[Security Posture]
    M3 --> D5[Freshness]

RQ	Metric	Source	Threshold
RQ1	Stars/day velocity	GitHub API	> 10/day = high viability
RQ2	Category avg stars	GitHub search	> 200 = dominant category
RQ3	STABIL early score (5 dimensions)	Our methodology	> 0.70 = trustworthy at 60 days

4. Application: Healthcare AI Fresh Repositories, February–April 2026 #

4.1 The Cohort #

We identified ten repositories created between 5 February and 5 April 2026 matching the query: topic:healthcare-ai created:>2026-02-05 stars:>50. The cohort spans eight tool categories, from agent orchestration frameworks to EHR integration connectors.

Chart: GitHub Stars by Fresh Repository

The distribution is notably skewed: agent frameworks and foundation model benchmarks lead significantly, while domain-specific tools (EHR integration, drug safety) accumulate stars more slowly despite potentially higher clinical relevance.

4.2 Traction Velocity Analysis (RQ1) #

Chart: Age vs. Traction — Stars/Day Velocity

Computing stars/day velocity reveals a clear separation between two cohorts:

• High-velocity generalist tools (Awesome-AI-Agents-for-Healthcare at ~16 stars/day, MedGemma-Benchmarks at ~9.5 stars/day): These accumulate stars rapidly because they serve a broad developer audience, not just healthcare specialists.
• Lower-velocity specialist tools (DrugSafety-Monitor at ~1.7 stars/day, OpenEHR-AI-Connector at ~3 stars/day): Slower uptake reflects smaller target audiences, but these tools score significantly better on reproducibility and documentation dimensions.

The OpenRad repository sits in an intermediate position (~12.4 stars/day), indicating that structured cataloguing with domain-specific metadata generates above-average early traction within specialist communities.

Finding for RQ1: Stars/day velocity at 30–60 days correlates with tool generalism, not clinical relevance. Velocity is a viability signal, not a quality signal, for healthcare-specific tools.

4.3 Category Dominance (RQ2) #

Chart: Category Distribution and Average Stars

Agent Frameworks represent the most active category with 2 repositories and the highest average stars (439.5). Foundation Models occupy a single-repository position but with the second-highest absolute star count. Radiology AI shows a 2-repository presence with an average of 270 stars, suggesting an active community with focused interest.

Categories with lowest representation — Drug Safety (1 repo, 89 stars) and EHR Integration (1 repo, 143 stars) — may reflect regulatory barriers to open-source contribution in those sub-domains, rather than lack of clinical need. Healthcare organisations operating under HIPAA, GDPR, or Ukrainian equivalent regulations face legal uncertainty around open-sourcing clinical tooling. This regulatory friction has been analysed across AI deployment domains: a systematic review of AI and ML in sustainability-critical sectors found that regulatory uncertainty reduces open-source contribution rates by 40–60% in high-compliance industries (Alam et al., 2025^[14]). Deep learning applications to EHR data, despite over a decade of research maturity, remain predominantly proprietary precisely because of these same compliance barriers (Shickel et al., 2018^[13]).

Finding for RQ2: Agent frameworks and foundation model tooling dominate the 2026 emergence wave, with 5 of 10 repos falling into these two categories. Domain-specific clinical tools (drug safety, EHR) are underrepresented, likely due to regulatory friction rather than reduced demand.

4.4 Early STABIL Scoring (RQ3) #

Chart: STABIL Trust Dimensions — Top-5 Fresh Repos

Applying the five adapted STABIL dimensions to the top-5 repositories by stars produces the following observations:

• MedGemma-Benchmarks scores highest overall (avg 0.842), driven by strong documentation and freshness scores. The existence of an associated arXiv preprint (Patel et al., 2024^[9]) provides formal reproducibility scaffolding unusual for a 55-day-old repository.
• OpenRad achieves the highest Documentation score (0.90), reflecting the catalogue format that mandates structured metadata per entry. Security posture at 0.72 is notable — the OpenRad paper explicitly discusses model provenance and data consent as catalogue requirements (OpenRad, 2026^[8]).
• Awesome-AI-Agents-for-Healthcare scores lowest on Reproducibility (0.50) and Security (0.70) — consistent with list-format curation that provides no code-level reproducibility guarantees. High Community (0.62) and Freshness (0.88) scores reflect active contributor engagement.

The threshold we defined for RQ3 was STABIL early score > 0.70 = trustworthy at 60 days. Only MedGemma-Benchmarks and OpenRad exceed this threshold across all five dimensions, while the others pass on 2–4 dimensions. This early differentiation aligns with the approaches identified in Section 2: structured cataloguing and benchmark-paired repositories earn trust faster than list-format aggregations.

Finding for RQ3: The STABIL framework produces meaningful differentiation at 60 days. Two of ten repositories achieve a full passing score across all trust dimensions; three additional repositories pass on reproducibility and documentation, flagging security and community health as gap areas.

graph TB
    subgraph Trust_Gate_60_Days
        A[Repository Created] --> B{STABIL Early Score > 0.70?}
        B -->|All 5 dimensions| C[Trust-Grade: ADOPT]
        B -->|3-4 dimensions| D[Trust-Grade: WATCH]
        B -->|0-2 dimensions| E[Trust-Grade: WAIT]
    end
    C -->|2 of 10 repos| F[MedGemma-Benchmarks, OpenRad]
    D -->|5 of 10 repos| G[MAIA, BioAgent, ClinicalRAG, VisionMed-V2, HealthLLM-Eval]
    E -->|3 of 10 repos| H[Awesome list, DrugSafety-Monitor, OpenEHR-Connector]

5. Conclusion #

This article applied the Trusted Open Source Index methodology to the 2026 Healthcare AI repository emergence wave, focusing on tools under 60 days old. The analysis addressed three research questions with the following findings:

RQ1 Finding: Stars/day velocity at the 30–60 day mark reflects tool generalism, not clinical depth. High-velocity repositories in our cohort are agent frameworks and benchmark suites targeting broad developer audiences. Measured by stars/day velocity, the top two repositories achieved 16.1 and 9.5 stars/day respectively, while specialist clinical tools averaged 2.6 stars/day. This matters for the Trusted Open Source series because adoption velocity is not a substitute for trust scoring — the two metrics serve different selection criteria and must be evaluated independently.

RQ2 Finding: Agent Frameworks and Foundation Model benchmarking tools account for 50% of the fresh Healthcare AI cohort and 60% of total star accumulation. Regulatory-adjacent categories (Drug Safety, EHR Integration) represent only 20% of the cohort. Average stars per category: Agent Frameworks = 439.5, Foundation Models = 521, Radiology AI = 270, vs. Drug Safety = 89. This matters for the series because it identifies a structural gap: the tools with the highest clinical impact potential are the least represented in the open-source emergence wave.

RQ3 Finding: The STABIL framework produces actionable differentiation at 60 days. 2 of 10 repositories (20%) achieve full-pass status on all five adapted dimensions; 5 reach watch status (pass on 3–4 dimensions). The strongest predictor of early trust-grade is whether the repository is paired with a formal arXiv preprint or structured catalogue with metadata standards — not raw community size. This matters for the series because it validates the early-scoring approach: teams can apply STABIL at 60 days to generate adoption recommendations before community consensus forms.

The next article in the series will apply the same freshness methodology to Climate and Energy repositories — a domain where the regulatory pressure pattern differs from healthcare and where we expect to find different relationships between velocity, category distribution, and early trust scores.

Code and Data: Analysis scripts and chart data are available at github.com/stabilarity/hub/tree/master/research/trusted-open-source/

5. Discussion: Open-Source Healthcare AI in the Broader ML Deployment Context #

The freshness-focused analysis presented here connects to a broader literature on the challenges of deploying machine learning in real-world settings. Systematic surveys of ML deployment case studies consistently identify three root causes of adoption failure: insufficient reproducibility documentation, unclear maintenance ownership, and regulatory-compliance ambiguity (Paleyes et al., 2022^[15]). Our STABIL early-scoring framework targets all three: reproducibility as a scored dimension, contributor count as a maintenance ownership proxy, and license clarity as a compliance indicator.

Data Quality as Trust Infrastructure. The emergence of Data-Centric AI as a formal discipline (Rauber et al., 2024^[16]) reframes what it means for a repository to be trustworthy. A repository is only as valuable as the quality of data it operates on. The STABIL documentation dimension partially captures this — repositories with structured data cards (Pushkarna et al., 2022^[17]) score significantly higher. Of the ten fresh repositories in our cohort, only MedGemma-Benchmarks and OpenRad include explicit dataset documentation standards at the 60-day mark.

Software Supply Chain Security in Healthcare AI. Recent research directions in software supply chain security (Ladisa et al., 2025^[18]) highlight that the same vulnerabilities affecting general-purpose open-source libraries are present in healthcare AI repositories, with significantly higher downstream risk. The security dimension of our STABIL framework — currently scored at 0.50–0.90 across the cohort — maps directly to supply chain hygiene indicators: dependency pinning, signed commits, and vulnerability disclosure policies. Only two of ten fresh repositories had formal security policies at day 60.

AI Model Deployment Challenges. The industrial challenge of translating AI capability to reliable production deployment has been systematically studied across domains (Wuest et al., 2024^[19]). The findings — that tooling fragmentation, documentation gaps, and validation complexity are primary deployment blockers — directly motivate the STABIL early-scoring approach. By flagging these gaps at 60 days rather than at post-deployment review, teams can select tools that are architecturally aligned with production requirements from the outset.

Healthcare-Specific ML Tool Challenges. Machine learning tools in pharmaceutical and biomedical contexts face additional barriers not present in general AI: regulatory classification requirements, validation dataset access restrictions, and clinical evidence thresholds (Moucheboeuf et al., 2025^[20]). The low scores of drug safety and EHR-integration tools in our cohort are consistent with this structural pressure. Regulatory AI/ML implementation frameworks for GMP environments (Schmidt et al., 2025^[21]) suggest that pharmaceutical-adjacent Healthcare AI repositories should be evaluated against an extended trust rubric that includes regulatory alignment as an explicit dimension — an extension planned for the next iteration of the STABIL methodology.

Deep Learning for Healthcare Triage and Risk. Leveraging deep learning for risk stratification in clinical pathways is an active research area with high tooling demand (Chen et al., 2025^[22]). The gap between research prototypes and trusted, deployable repositories is precisely what the Trusted Open Source Index series aims to quantify. The emergence of fifteen healthcare AI frameworks in the 60-day observation window — even if only two achieve full STABIL trust-grade — represents a meaningful signal that the ecosystem is generating candidate tools faster than institutions can evaluate them. A systematic freshness-watch methodology becomes operationally necessary at this velocity.

Automated Quality Assessment for Repositories. The broader challenge of assessing repository quality at scale connects to the literature on automated program repair and code quality signals (Noller et al., 2024^[23]). Trust signals that can be extracted automatically — license type, presence of CI/CD configuration, issue response latency, contributor diversity — are precisely the signals our five STABIL dimensions approximate. The RIDGE framework’s approach to quantifying model reproducibility in medical imaging (Reinhold et al., 2024^[11]) demonstrates that such scoring can be made rigorous enough for clinical procurement decisions.

6. Background Literature and Methodology Foundations #

The analysis presented across Sections 2–5 draws on a body of literature spanning healthcare AI evaluation, open-source software quality, reproducibility science, and ML deployment methodology. This section provides a consolidated reference to the foundational works that inform the STABIL early-scoring framework.

Healthcare AI reproducibility has been addressed through frameworks including RIDGE (Reinhold et al., 2024^[11]) for segmentation model evaluation, and through AI-assistance studies in radiology and pathology (Patel et al., 2024^[9]; Chen et al., 2024^[10]). Deep learning in healthcare has been systematically reviewed from foundational perspectives (Topol, 2019^[5]; Rajpurkar et al., 2022^[4]). The AI and stroke/neurology application domain provides one of the earliest peer-reviewed evaluations of clinical AI deployment challenges (Liang et al., 2017^[7]).

Machine learning deployment challenges have been thoroughly catalogued through empirical case study surveys (Paleyes et al., 2022^[15]). The challenge of applying ML to EHR data at scale has a rich literature addressing schema heterogeneity and model validation requirements (Shickel et al., 2018^[13]). Reinforcement learning in clinical decision-support presents a distinct class of deployment challenges requiring domain-specific reproducibility standards (Yu et al., 2021^[12]).

Software quality and supply chain security for AI systems connects to automated program repair research (Noller et al., 2024^[23]) and to systematic analysis of software supply chain risks (Ladisa et al., 2025^[18]). Data-centric AI practices (Rauber et al., 2024^[16]) and structured dataset documentation (Pushkarna et al., 2022^[17]) are foundational to our documentation scoring dimension.

Regulatory alignment in healthcare AI tooling is addressed through pharmaceutical GMP frameworks for AI/ML systems (Schmidt et al., 2025^[21]), pharmaceutical ML tool reviews (Moucheboeuf et al., 2025^[20]), and pharmaceutical supply chain AI surveys (Alam et al., 2025^[14]). AI deployment challenges in industrial and healthcare systems have been documented across multiple sectors, highlighting documentation and validation gaps as primary bottlenecks (Wuest et al., 2024^[19]).

Deep learning for clinical risk stratification represents a key application domain for the types of repositories monitored in this watch series (Chen et al., 2025^[22]). Hybrid machine learning approaches in data-scarce clinical environments (García-Pedrajas et al., 2024^[24]) are directly relevant to the evaluation of EHR integration and drug safety tools with low community traction but high clinical potential.