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arxiv: 2604.18302 · v1 · submitted 2026-04-20 · 💻 cs.AI

Recognition: unknown

Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support

Eranga Bandara , Asanga Gunaratna , Ross Gore , Anita H. Clayton , Christopher K. Rhea , Sachini Rajapakse , Isurunima Kularathna , Sachin Shetty
show 4 more authors
Ravi Mukkamala Xueping Liang Preston Samuel Atmaram Yarlagadda
Authors on Pith no claims yet

Pith reviewed 2026-05-10 05:15 UTC · model grok-4.3

classification 💻 cs.AI
keywords on-device AIpsychiatric diagnosisprivacy-preservingmental healthLLM deploymentmobile applicationDSM-5 assessmentzero-egress
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The pith

A mobile app runs fine-tuned LLMs entirely locally to deliver psychiatric assessments without any patient data leaving the device.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that psychiatric decision support can shift from cloud servers to on-device execution using a consortium of compact, quantized language models. This matters because current cloud pipelines force sensitive mental health data to exit the device, which can discourage people from seeking help in military, correctional, or remote settings. The system orchestrates local inference to generate DSM-5-aligned outputs for clinicians and patients while keeping all processing on the phone. If the accuracy holds, the approach removes a major barrier to AI use in environments that reject external data transmission.

Core claim

The work presents a zero-egress cross-platform mobile application that integrates three lightweight, fine-tuned, and quantized open-source LLMs coordinated by an on-device orchestration layer to perform ensemble inference and consensus-based diagnostic reasoning, yielding DSM-5-aligned assessments for differential diagnosis and symptom mapping with accuracy comparable to the server-side version and real-time latency on commodity hardware.

What carries the argument

An on-device orchestration layer that coordinates ensemble inference and consensus-based diagnostic reasoning among three quantized LLMs.

If this is right

  • Clinicians gain local access to differential diagnosis support and evidence-linked symptom mapping without data transmission.
  • Patients can use self-screening features with built-in safeguards while data remains on-device.
  • The platform becomes usable in operational environments that prohibit any external data flow.
  • Real-time performance is maintained on standard mobile hardware rather than requiring specialized servers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The design could extend to other privacy-sensitive medical domains by swapping the diagnostic focus while retaining the local orchestration layer.
  • Long-term, repeated local use might allow the models to adapt to individual users through on-device updates without cloud involvement.
  • Testing the system on diverse populations would reveal whether quantization introduces biases in specific demographic groups.

Load-bearing premise

The quantized and fine-tuned models retain enough diagnostic fidelity for DSM-5 assessments to match the accuracy of their full server-side versions.

What would settle it

A direct comparison of diagnostic outputs from the on-device system versus the server version on the same set of clinical cases, measuring agreement rates and specific error types.

Figures

Figures reproduced from arXiv: 2604.18302 by Anita H. Clayton, Asanga Gunaratna, Atmaram Yarlagadda, Christopher K. Rhea, Eranga Bandara, Isurunima Kularathna, Preston Samuel, Ravi Mukkamala, Ross Gore, Sachini Rajapakse, Sachin Shetty, Xueping Liang.

Figure 1
Figure 1. Figure 1: Zero-egress on-device architecture for privacy-preserving psychiatric AI decision [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: End-to-end offline fine-tuning and on-device deployment pipeline. The [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: On-device ensemble inference flow. A clinical conversation is captured via the [PITH_FULL_IMAGE:figures/full_fig_p021_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: AI model selection panel in the mobile application. Three modes are available: [PITH_FULL_IMAGE:figures/full_fig_p025_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Home screen of the mobile application, subtitled [PITH_FULL_IMAGE:figures/full_fig_p028_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Conversational interface (AI Session screen) of the mobile application. The [PITH_FULL_IMAGE:figures/full_fig_p031_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: SOAP Notes task flow. Upon receiving a Generate SOAP note request, the platform returns a structured intake prompt requesting four categories of clinical informa￾tion: (1) Subjective — patient complaints, symptoms, and history; (2) Objective — vital signs, physical examination findings, lab results, and imaging; (3) Assessment — clinical impressions and differential diagnoses; and (4) Plan — proposed treat… view at source ↗
read the original abstract

Privacy represents one of the most critical yet underaddressed barriers to AI adoption in mental healthcare -- particularly in high-sensitivity operational environments such as military, correctional, and remote healthcare settings, where the risk of patient data exposure can deter help-seeking behavior entirely. Existing AI-enabled psychiatric decision support systems predominantly rely on cloud-based inference pipelines, requiring sensitive patient data to leave the device and traverse external servers, creating unacceptable privacy and security risks in these contexts. In this paper, we propose a zero-egress, on-device AI platform for privacy-preserving psychiatric decision support, deployed as a cross-platform mobile application. The proposed system extends our prior work on fine-tuned LLM consortiums for psychiatric diagnosis standardization by fundamentally re-architecting the inference pipeline for fully local execution -- ensuring that no patient data is transmitted to, processed by, or stored on any external server at any stage. The platform integrates a consortium of three lightweight, fine-tuned, and quantized open-source LLMs -- Gemma, Phi-3.5-mini, and Qwen2 -- selected for their compact architectures and proven efficiency on resource-constrained mobile hardware. An on-device orchestration layer coordinates ensemble inference and consensus-based diagnostic reasoning, producing DSM-5-aligned assessments for conditions. The platform is designed to assist clinicians with differential diagnosis and evidence-linked symptom mapping, as well as to support patient-facing self-screening with appropriate clinical safeguards. Initial evaluation demonstrates that the proposed zero-egress deployment achieves diagnostic accuracy comparable to its server-side predecessor while sustaining real-time inference latency on commodity mobile hardware.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a zero-egress on-device AI platform for privacy-preserving psychiatric decision support, implemented as a cross-platform mobile application. It extends prior work on fine-tuned LLM consortiums by deploying an ensemble of three quantized lightweight models (Gemma, Phi-3.5-mini, Qwen2) with an on-device orchestration layer for consensus-based, DSM-5-aligned diagnostic reasoning. The system targets differential diagnosis assistance and patient self-screening in sensitive settings (military, correctional, remote care) while ensuring no patient data leaves the device. The abstract asserts that initial evaluation shows diagnostic accuracy comparable to the server-side predecessor alongside real-time inference latency on commodity mobile hardware.

Significance. If the accuracy and latency claims are substantiated, the work would offer a concrete technical path to address a major adoption barrier for AI in mental healthcare: the privacy risk of data egress in high-stakes environments. Demonstrating a practical, fully local ensemble deployment using open-source models could enable safer clinician tools and self-screening applications without external servers, potentially increasing help-seeking behavior. The emphasis on consensus reasoning and clinical safeguards adds operational relevance beyond pure model compression.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'initial evaluation demonstrates that the proposed zero-egress deployment achieves diagnostic accuracy comparable to its server-side predecessor' is presented without any quantitative metrics (accuracy, F1, Cohen's kappa), dataset description (size, conditions, ground-truth source), evaluation protocol, baselines, or error analysis. This absence leaves the primary performance assertion unsupported and prevents assessment of whether quantization and mobile constraints degrade DSM-5 diagnostic fidelity.
  2. [Abstract] Evaluation/Results section (implied by abstract claim): No details are provided on how the on-device ensemble was tested against the server-side predecessor, including any ablation on quantization effects, inter-rater agreement with clinicians, or statistical significance of the 'comparable' result. Without these, the claim that the architecture preserves diagnostic quality cannot be evaluated and is load-bearing for the paper's contribution.
minor comments (2)
  1. [Architecture] The description of the orchestration layer and consensus mechanism would benefit from a high-level diagram or pseudocode to clarify how the three models coordinate DSM-5 symptom mapping and differential diagnosis.
  2. [Introduction] Explicit citation to the prior server-side LLM consortium paper should be added in the introduction to clearly delineate the novel on-device re-architecture from the earlier fine-tuning work.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments correctly identify that the abstract's performance claim requires quantitative substantiation to be evaluable. We will revise the manuscript to add a dedicated Evaluation section with metrics, dataset details, ablations, and analysis, while updating the abstract accordingly. Our point-by-point responses follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'initial evaluation demonstrates that the proposed zero-egress deployment achieves diagnostic accuracy comparable to its server-side predecessor' is presented without any quantitative metrics (accuracy, F1, Cohen's kappa), dataset description (size, conditions, ground-truth source), evaluation protocol, baselines, or error analysis. This absence leaves the primary performance assertion unsupported and prevents assessment of whether quantization and mobile constraints degrade DSM-5 diagnostic fidelity.

    Authors: We agree that the abstract claim is currently unsupported without supporting numbers and context. In revision we will expand the abstract to report key quantitative results (e.g., accuracy, F1, Cohen's kappa) and will add a new Evaluation section that fully describes the test dataset (size, conditions, ground-truth source), protocol, baselines, and error analysis so readers can assess any impact of quantization and on-device constraints. revision: yes

  2. Referee: [Abstract] Evaluation/Results section (implied by abstract claim): No details are provided on how the on-device ensemble was tested against the server-side predecessor, including any ablation on quantization effects, inter-rater agreement with clinicians, or statistical significance of the 'comparable' result. Without these, the claim that the architecture preserves diagnostic quality cannot be evaluated and is load-bearing for the paper's contribution.

    Authors: We accept this assessment. The current manuscript presents only a high-level claim. We will add a full Results/Evaluation section containing ablation studies on quantization, inter-rater agreement (Cohen's kappa) with clinicians and the server-side model, statistical significance tests, and error analysis. These additions will directly substantiate the comparability claim and allow evaluation of diagnostic fidelity preservation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; architecture and claims are self-contained

full rationale

The paper presents an engineering description of an on-device LLM ensemble for psychiatric decision support, extending prior fine-tuned models via re-architecting for local execution. No mathematical derivations, equations, fitted parameters, or predictions appear in the provided text. The accuracy comparability claim is asserted from 'initial evaluation' without reducing by construction to the inputs or prior work; it is an evidentiary assertion rather than a self-referential loop. Self-citation of the authors' earlier LLM consortium work is present but does not bear load on any derivation chain, as the deployment pipeline stands independently. This matches the default expectation of no circularity for descriptive systems papers.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The claim rests on the untested premise that existing open-source LLMs, after fine-tuning and quantization, can produce clinically reliable DSM-5 outputs when orchestrated locally; no new entities or free parameters are introduced in the abstract.

axioms (2)
  • domain assumption Lightweight open-source LLMs can be fine-tuned to produce DSM-5-aligned psychiatric assessments
    Invoked when selecting Gemma, Phi-3.5-mini, and Qwen2 for the consortium
  • domain assumption Quantization preserves sufficient diagnostic accuracy for clinical decision support
    Required for the on-device performance claim

pith-pipeline@v0.9.0 · 5638 in / 1350 out tokens · 23888 ms · 2026-05-10T05:15:59.137640+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction

    cs.CL 2026-05 unverdicted novelty 5.0

    Small open-weight language models can self-optimize prompts for clinical named entity recognition in dental notes, reaching micro F1 of 0.864 after DPO on Qwen2.5-14B.

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