NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages

arXiv:2608.07541 · cs.CV, cs.AI, cs.MA · Submitted 2026-07-30 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages".

Jane: The paper was written by Yiyao Chen, Yucheng Li, Junhong Tong, Shaoqi Wang, Kunhao Zhou et al. from University of North Carolina at Chapel Hill.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Title: Tom: Welcome back to the show, everyone. Today we're looking at a paper that's got a title almost as long as the problem it's trying to solve: "NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages."

Jane: And Tom, I have to say, when I first read that title, I thought, okay, another neuroimaging pipeline paper. But then I saw the scale of what they've done, and I got genuinely excited.

Tom: Right? I mean, we're talking about seventeen different cohorts, over one hundred twenty-three thousand subjects. That's not a pilot study, that's a full-scale deployment.

Jane: Exactly. And the name NeuroPilot is actually pretty clever when you think about it. A pilot doesn't fly the plane manually the whole time, they oversee the autopilot, they make the consequential decisions, and they step in when something goes wrong.

Tom: That's a really good way to put it, Jane. And that's exactly what this system does. It's not replacing the human expert, it's giving them a cockpit with all the instruments laid out properly.

Jane: So the title tells us it's about processing, quality control, and managing neuroimages. But the real story here is that they've turned what used to be months of manual work into something an AI agent can orchestrate in about a week.

Tom: A week. Let me just sit with that for a second. Because I've heard stories from labs where just getting the data into the right format takes longer than that.

Jane: Oh, absolutely. And that's the thing, the individual tools, like fMRIPrep or FreeSurfer, they're all well-established. The bottleneck has never been the tools themselves.

Tom: So what's the bottleneck then?

Jane: The glue. The scripts that connect one tool to the next, the environment-specific configuration, the manual checking of every single output. That's where projects go to die.

Tom: And that's what NeuroPilot is attacking. The title says "agent-driven," and that's the key innovation here. They're using large language models to make decisions about what to run, when to run it, and how to verify it worked.

Jane: Right. And I love that they've packaged this knowledge into what they call "skills." Three of them, actually. One for converting raw DICOM data into the standard BIDS format, one for choosing and running the right preprocessing pipeline, and one for quality control with a human in the loop.

Tom: So it's not just automation for the sake of automation. It's structured, auditable, and it keeps a human in charge of the important calls.

Jane: Precisely. And the implications of that are huge for reproducibility. Because right now, if a lab's postdoc leaves, all that knowledge about how to process their data leaves with them.

Tom: That's such a real problem. I've seen it happen. The new person has to reverse-engineer everything from scratch.

Jane: NeuroPilot changes that. The knowledge is encoded in the skills, the decisions are logged in an audit trail, and the whole thing is portable across different computing environments.

Tom: So it's not just saving time, it's preserving institutional memory.

Jane: Exactly. And that's why I think this paper matters beyond just the neuroimaging community. It's a template for how to apply agentic AI to any complex scientific workflow.

Tom: Alright, so we've got the big picture. But I want to get into the actual guts of how this thing works. What are these skills really doing under the hood?

Jane: That's the perfect setup for our next segment, Tom. We're going to break down the summary of the paper and look at how these three skills actually fit together.

Summary: Tom: So we're back, and we're digging into the summary of "NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages." Jane, you mentioned the three skills, but let's get concrete about what they actually do.

Jane: Okay, so think of it like a factory floor. The first skill, dcm2bids-skill, is the receiving dock. Raw MRI data comes in as messy DICOM files, sometimes compressed, sometimes with no file extensions, sometimes with protected health information in the headers.

Tom: And the receiving dock has to figure out what each box contains and label it properly.

Jane: Exactly. It detects what each scan series actually is, a T1-weighted structural scan, a diffusion scan, a functional scan, and it maps them into the standard BIDS format. It even builds its configuration from the union of all series across the whole cohort, so it doesn't miss rare modalities that only show up in a few subjects.

Tom: That's smart. Because if you only look at one subject to build your conversion rules, you might miss that some subjects have a DWI scan that others don't.

Jane: Right. And then the second skill, neuroimage-pre-skill, is the assembly line. It looks at what modalities are available and routes the data to the right preprocessing pipeline.

Tom: So it's making decisions about which tool to use based on the data itself?

Jane: Precisely. If you have fMRI data, it goes to fMRIPrep. If you have diffusion data, it goes to QSIPrep. And if you have infant data, it uses specialized infant pipelines, because neonatal brains look nothing like adult brains on MRI.

Tom: I hadn't even thought about that. The tissue contrast is totally different in a baby's brain.

Jane: Exactly. And the third skill, qc-agent-skill, is the quality control station. This is where a human reviewer comes in. The agent computes metrics, flags outliers, proposes fixes, and then a human makes the final call.

Tom: So the human is still in the loop for the important decisions.

Jane: Absolutely. The agent does the heavy lifting, computing things like brain extraction quality, registration accuracy, surface topology defects. But the human reviews the evidence, decides pass or fail, and signs off on the final export.

Tom: And that sign-off is recorded in an append-only ledger, right?

Jane: Yes. Every decision is attributable. You can see who approved what, when, and why. That's huge for reproducibility and for auditing.

Tom: So the summary of the paper is really about this three-stage orchestration. And the results they report are pretty remarkable. They validated this across seventeen cohorts, over one hundred twenty-three thousand subjects.

Jane: And the numbers are impressive. The infant pipeline achieved one hundred percent completion on valid inputs. The adult structural connectivity pipeline completed eighty-seven point five percent of subjects, and every failure was traced to bad input data, not pipeline errors.

Tom: So garbage in, garbage out, but the pipeline itself is solid.

Jane: Exactly. And they compressed the timeline from two to three months down to a single week. That's not incremental improvement, that's a step change.

Tom: I want to hear more about that quality control piece, because that seems like the most novel part. How do they actually decide what's good and what's bad?

Jane: That's exactly what we're going to dig into next. The QC system has this really interesting cohort-relative grading approach that I think is going to surprise people.

Improvements: Tom: Alright, we're back with "NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages." And Jane, you teased something about the quality control being cohort-relative. What does that actually mean?

Jane: So imagine you're grading student essays. If you grade everyone against a universal standard, you might penalize students for stylistic choices that are actually appropriate for their background. The same thing happens with brain images.

Tom: Because different scanners, different protocols, different populations produce different-looking images?

Jane: Exactly. A scan from a seventy-year-old with mild cognitive impairment looks different from a scan from a twenty-year-old healthy volunteer. So instead of applying universal thresholds, NeuroPilot grades each subject against the distribution of their own cohort.

Tom: So it's like, this subject's brain extraction has more holes than ninety-five percent of their peers, so we flag it.

Jane: Precisely. They use statistical measures like the modified Z-score and median absolute deviation. If a subject is a statistical outlier compared to their cohort, they get flagged for review.

Tom: And that's the detection part. But what about the fixing part?

Jane: That's where the improvements really shine. They've built a five-step structural QC pipeline. Step one checks the raw image for motion and artifacts. Step two checks brain extraction. Step three checks registration to a standard template. Step four checks tissue segmentation. Step five checks the cortical surface reconstruction.

Tom: And each step has both detection and repair mechanisms?

Jane: Yes. For example, if brain extraction is too tight, meaning it's excluding actual brain tissue, they can widen the mask using a ladder of options with different aggressiveness levels. If registration is bad, they can rerun it with a more powerful nonlinear algorithm.

Tom: And the numbers they report are pretty compelling. The skull-strip fix reduced the exclusion metric by an average of twenty-seven percent.

Jane: And registration repair improved normalized cross-correlation from zero point six two to zero point seven eight on one flagged subject. That's a twenty-six percent improvement in alignment quality.

Tom: But here's what I find really interesting. They decouple detection from fixing. The agent detects and proposes, but the actual fixes are done by established tools like FreeSurfer and ANTs.

Jane: That's a deliberate design choice. They don't want the AI agent hand-editing brain masks or white matter volumes. Those numerically consequential edits need to happen inside the same auditable tools that downstream analysis expects.

Tom: So the agent is the orchestrator, not the surgeon.

Jane: Exactly. And the human reviewer chooses which fix to apply, or whether to dismiss the subject entirely. There's a whole governance layer with reviewer identity, an append-only ledger, and escalation paths for ambiguous cases.

Tom: And they validated this on five hundred fifty-eight production subjects. The automated flags were checked against FreeSurfer's own topology-defect metrics.

Jane: Right. And they found that the surface QC step catches things the earlier steps miss. Two subjects passed all four earlier checks but had thousands of defect vertices on their cortical surfaces.

Tom: So without that fifth step, those subjects would have gone into downstream analysis with corrupted surfaces.

Jane: Exactly. And that's the kind of silent failure that can bias results without anyone noticing.

Tom: So the improvements here are really about catching failures that would otherwise slip through, and fixing them in a way that's traceable and auditable.

Jane: And that's the foundation for everything else. But I want to step back and look at the actual first page of the paper, because there's some context there about why this problem is so hard in the first place.

First Page: Tom: So we're looking at the opening of "NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages." And Jane, the first page really sets up the problem in a way that I think a lot of researchers will recognize.

Jane: Oh, absolutely. They talk about how each individual tool, like dcm2niix for data conversion or fMRIPrep for preprocessing, is well-documented and validated. But taking a raw archive all the way to quality-controlled imaging traits is not one-stop shopping.

Tom: That phrase, "not one-stop shopping," really captures it. There are so many steps in between, and each one relies on the previous one being done correctly.

Jane: And they list the specific pain points. Data standardization faces compressed folders of extension-less DICOMs, vendor-specific naming conventions, and protected health information that needs to be handled carefully.

Tom: Then preprocessing requires an expert to read off the available modalities, pick the right pipeline, feed one stage's output into the next, write process scripts with the correct resources and bind mounts, batch across hundreds of subjects, and confirm everything worked.

Jane: And then quality control demands human labor to inspect each intermediate, decide which subjects fail, apply or reject fixes, and document the whole thing.

Tom: They call this the "real crux" of the problem. The orchestration of existing tools, not the tools themselves.

Jane: And they point out something that really resonated with me. They say that image processing scripts are often project-specific, tuned to a specific computational environment, lack sufficient QC, and are rarely structured for software reuse.

Tom: So every lab reinvents the wheel, and then when the person who built the wheel leaves, the knowledge is lost.

Jane: Exactly. And they mention that team personnel changes lead to project delays, loss of traceability, and anomalous results that become impossible to track down.

Tom: That's such a real problem. I've seen papers where the methods section says "preprocessing was performed using standard procedures" and you have no idea what that actually means.

Jane: Right. And that's what NeuroPilot is trying to fix. They're packaging the expertise of neuroimage processing, QC, and data management into these three LLM-invocable skills.

Tom: And the key insight is that the agent can read a filesystem, run shell commands, call tools, and reason about the results. It's not just executing a static workflow, it's making decisions.

Jane: But they're careful to keep the human in the loop for consequential steps. The agent proposes, the human disposes.

Tom: And that's what makes this practical. It's not trying to replace the expert, it's trying to give them superpowers.

Jane: And the scale of their validation is what makes this more than just a demo. Seventeen cohorts, over one hundred twenty-three thousand subjects, spanning infant to aging populations.

Tom: And they're not just running the pipeline, they're validating the QC decisions against established metrics.

Jane: Right. The infant processing pipeline achieved one hundred percent completion on QC-validated inputs. And they compressed the timeline from months to a single week.

Tom: That's the kind of result that makes people sit up and take notice.

Jane: And I think the implications go beyond neuroimaging. This is a template for how to apply agentic AI to any complex scientific workflow that involves multiple tools, messy data, and the need for human oversight.

Tom: So before we wrap up, I want to bring in Lu and Meng to get their take on this. Lu, what excites you most about this approach?

Lu: The cohort-relative QC is genuinely clever. Most QC systems use fixed thresholds, which are almost always wrong for some subset of data. By grading against the cohort's own distribution, you adapt to whatever the scanner and protocol actually produce.

Meng: And from an engineering standpoint, I appreciate the portability. They've separated the domain logic from the environment configuration. One config file, and it runs on a laptop or a cluster. That's how you build systems that actually get adopted.

Tom: Great points from both of you. Let's move to the conclusion and wrap this up.

Conclusion: Tom: Alright, we're wrapping up our discussion of "NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages." Jane, give us the final summary.

Jane: So NeuroPilot tackles the three brittle stages of neuroimaging research: data standardization, modality-specific preprocessing, and quality control. It packages the expertise for each stage into three LLM-invocable skills, and an agent orchestrates the whole workflow.

Tom: And the key innovation is that it's not just automation. It's structured, auditable, and keeps a human in charge of the consequential decisions.

Jane: Exactly. The QC stage is particularly impressive. It grades each subject against their own cohort, flags outliers, proposes fixes, and requires human sign-off on every export. Everything is logged in an append-only ledger.

Tom: And the results speak for themselves. Seventeen cohorts, over one hundred twenty-three thousand subjects, one hundred percent completion on valid infant inputs, and a compression of the timeline from months to a single week.

Jane: And every failure they encountered was traced to bad input data, not pipeline errors. That's a strong statement about the robustness of the system.

Tom: Lu, any final thoughts?

Lu: I think the biggest impact will be on reproducibility. Right now, so much of neuroimaging research is not actually reproducible because the processing steps are underdocumented and the QC decisions are subjective. NeuroPilot makes both of those auditable.

Meng: And from a practical standpoint, it lowers the barrier to entry. A lab that doesn't have a dedicated image processing expert can still produce high-quality derivatives. That could democratize access to advanced neuroimaging analysis.

Tom: That's a really important point. And I think it's the right note to end on. NeuroPilot isn't just making things faster, it's making them more reliable and more accessible.

Jane: And that's what good scientific infrastructure should do. It should let researchers focus on the science, not the plumbing.

Tom: Well said. That's our show for today. Thanks to everyone who tuned in, and we'll see you next time with another paper from the arXiv.

Jane: Take care, everyone.

Yiyao Chen, Yucheng Li, Junhong Tong, Shaoqi Wang, Kunhao Zhou, Ziquan Wei, Monica Murea, Marissa DiPiero, Tingting Dan, Guorong Wu

University of North Carolina at Chapel Hill

cs.CV, cs.AI, cs.MA

Submitted: 2026-07-30

Updated: 2026-08-11

Comments: 21 pages, 6 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 74/100

The gist: data standardization, modality-specific preprocessing, and quality control (QC).

Key concepts

Agent-Driven Pipeline: NeuroPilot uses large language models as agents to make decisions about what tools to run and when. It is not just automating a static workflow but allowing the system to reason about the data and make choices based on its content, acting like an autopilot for complex scientific tasks.
Cohort-Relative Grading: This quality control method grades each subject's results against their own cohort's distribution rather than a universal standard. This adapts to variations in scanner protocols and populations, flagging subjects who are statistical outliers within their specific group.
Skills Packaging: The system packages expertise into three distinct skills: converting raw DICOM data to BIDS format, choosing the correct preprocessing pipeline based on data type, and performing quality control. This structure makes the knowledge portable and reusable.
Decoupling Detection from Fixing: The agent detects issues like poor brain extraction but does not perform the actual fixes itself. Instead, it proposes fixes that are then executed by established tools like FreeSurfer or ANTs, ensuring that consequential edits remain within auditable tools.

Terminology

Human-in-the-Loop — 9× in this paper · explained in 3 episodes
Verification — 7× in this paper · explained in 2 episodes
Robustness — 6× in this paper · explained in 5 episodes
Accuracy — 4× in this paper · explained in 1 episode
Agentic AI — 4× in this paper · explained in 1 episode
Core — 4× in this paper · explained in 2 episodes
Alignment — 3× in this paper · explained in 1 episode
Multi-Agent System — 3× in this paper · explained in 6 episodes
Provenance — 3× in this paper · explained in 4 episodes
Benchmark — 2× in this paper · explained in 2 episodes
Brain Imaging Data Structure — 2× in this paper
Calibration — 2× in this paper · explained in 7 episodes
Generalizability — 2× in this paper · explained in 6 episodes
Verifiability — 2× in this paper · explained in 2 episodes
Agent Skills — 1× in this paper · explained in 1 episode
Aggregation — 1× in this paper · explained in 1 episode
Computational Biology — 1× in this paper · explained in 2 episodes
Topological Defects — 1× in this paper · explained in 2 episodes
Trace — 1× in this paper · explained in 2 episodes

Summary

Summary

The paper introduces NeuroPilot, a multi-agent system designed to address the brittleness of transforming raw neuroimage archives into analysis-ready derivatives, a process that relies on three fragile stages: data standardization, modality-specific preprocessing, and quality control (QC). The authors state: While individual neuroimaging tools are well developed, their orchestration requires project-specific scripts, environment-adaptive tuning, and labor-intensive manual QC. NeuroPilot digitalizes the expertise of neuroimage processing, QC, and data management into three LLM-invocable skills: dcm2bids-skill, neuroimage-pre-skill, and qc-agent-skill. The LLM-driven agent autonomously orchestrates workflows, generalizing various infrastructure settings into a single configuration to achieve the highest scalability.

The system was deployed across 17 cohorts comprising more than 123,000 subjects, spanning infant to aging populations and multiple MRI modalities (structural, diffusion, functional). The authors note: "In practice, after standardizing data via the dcm2bids-skill, the agent dynamically routes datasets to the optimal neuroimage-pre-skill based on available modalities and cohort traits (e.g., dispatching T1w and fMRI data to fMRIPrep, or selecting specialized pipelines for infant cohorts)." The qc-agent-skill drives an evidence-based, semi-automated QC via a 3-D browser dashboard, utilizing a multi-tiered verification system to optimize failed cases and escalate complex issues for supervisor inspection. Quantitatively, the QC agent screened 558 production subjects, validating its automated flags against FreeSurfer's topology-defect metrics. The infant processing pipeline achieved a 100% (201/201) completion rate on QC-validated inputs. Importantly, NeuroPilot compresses the traditional 2–3 month timeline for training staff and processing complete datasets into a single week.

The paper identifies the core problem: "Multiple domain-specific workflow and complexity of real-world data are the major reasons why fully automation fails in practice, which hinder the efficiency and replicability of neuroimaging studies. Image processing script is often specifically designed for each project, tuned to a specific computational environment, lack of sufficient QC, and rarely structured for software reuse. Quality control faces a more critical challenge: it is often labor-intensive, subjective, and undocumented." The authors leverage agentic AI technology to read a filesystem, run shell commands, call tools, and apply reasoning over results by alternating reasoning with action and learning when to invoke a tool. The agent can explain a plan and wait for approval.

Each skill is a self-describing capability package with four parts: (1) a natural-language description telling the agent when it applies, (2) reference procedures read on demand, (3) parameterized scripts taking all paths as arguments, and (4) checkpoints that verify inputs before a run and outputs after. The three skills encompass the full processing lifecycle: a data-standardization skill (dcm2bids-skill), a modality-specific preprocessing skill (neuroimage-pre-skill), and a human-in-the-loop QC skill (qc-agent-skill). The agent chains them from context and the human supervises the consequential steps.

The contributions are: (1) an end-to-end agentic design for the whole neuroimage processing lifecycle as three declarative, LLM-invocable skills with explicit input/output checkpoints, separating portable domain logic from site-specific configurations for universal cross-platform execution; (2) a reproducible, human-in-the-loop QC stage that dynamically grades subjects against their own cohort using published metrics (e.g., Iglewicz–Hoaglin modified Z and SynthStrip agreement), visualizes results via integrated 3-D browser dashboards, decouples error detection from repair mechanisms, and records every interaction in an append-only audit ledger; (3) large-scale deployment and validation testing over 123,000 subjects from 17 distinct cohorts, verifying the agent's capability for automated series classification, dynamic pipeline selection, and idempotent batch processing across ∼20 atlases in high-throughput settings, incorporating a five-step structural QC with pre-calibrated grading thresholds; (4) a substantial reduction in time cost and human labor, compressing the end-to-end data processing timeline from months to a single week.

The design is shaped by six goals: portability (the same domain logic runs across SLURM clusters, standalone servers, or local workstations with changes to only a single configuration file), resumability (any stage can be re-submitted safely, and completed subjects are skipped), verifiability (every stage declares its required inputs and expected outputs, and the skill checks both), human-in-the-loop (the agent handles mechanical work but stops for approval at consequential or hard-to-reverse steps), auditability (every action is a logged shell command or SLURM job, and QC decisions land in a sign-off ledger), and management (a complete, centralized history of all processing steps and decisions is preserved).

For data standardization, the dcm2bids-skill keeps the field-standard engine (dcm2niix/dcm2bids) and swaps the fragile hand-written configuration for an agentic workflow. It aims for a validated BIDS dataset in under ten minutes from invocation to a submitted conversion, tolerating per-subject failure. The skill handles detection and extraction (looking inside files for the DICM signature at byte 128 instead of depending on file extensions, safely unzipping compressed archives), cohort-wide series union (building the union of every distinct series across all subjects to avoid dropping minority modalities), batched human confirmation (collecting every open decision in one round-trip), and verification, reporting, and privacy (running the BIDS Validator, keeping logs, running a drop-check, and surfacing DICOM PHI explicitly). All seventeen testbed cohorts converted this way and passed BIDS validation.

For modality-specific preprocessing, the neuroimage-pre-skill picks one of four pipelines by detected modality and drives the matching BIDS Apps, loading only the reference it needs. It takes five inputs (BIDS directory, output directory, working directory, log directory, and cluster configuration file), and every bundled script reads all paths as arguments. For adult cohorts with multimodal data, fMRIPrep performs foundational anatomical and BOLD preprocessing including FreeSurfer surface reconstruction; the functional stream passes to XCP-D for denoising before nilearn computes FC matrices; the structural arm leverages FreeSurfer surfaces to initialize QSIPrep diffusion preprocessing, and QSIRecon utilizes MRtrix3 to execute MSMT-CSD, anatomically-constrained tractography guided by HSVS 5-tissue-type segmentation, and SIFT2 streamline weighting, extracting SC matrices across ∼20 atlases. For infant cohorts, the pipeline leverages NiBabies for neonatal functional processing and ACT-Atropos alongside SS3T-CSD for infant-optimized structural tractography. The agent dynamically adapts to the deployment environment by prompting the user to select an execution backend: direct run (local machine), HPC SLURM (sbatch), or server execution (slmrun). System-wide idempotence ensures that any resubmission acts as a safe no-op, and automated cleanup daemons reclaim scratch storage immediately upon detecting per-subject success sentinels.

For quality control, the qc-agent-skill runs five structural checks in order: raw image, brain extraction, registration, segmentation, and surface. Two rules hold throughout: detection and fixing stay on separate tools (the skill's own scripts detect and localize problems, fixes go to tools built for them like FreeSurfer editing and ANTs, and the QC agent never edits a mask or white-matter volume by hand), and grading is cohort-relative (the skill flags a subject as an outlier against its own cohort and keeps a conservative absolute floor only as a safety net). The five checks use specific metrics: (1) raw image uses motion and artifact scoring on the IBIS/NIRAL 1–4 scale, ghost-ratio, coverage/FOV, and intensity clipping; (2) brain extraction uses a hole count flagged by the Iglewicz–Hoaglin modified Z and over-tightness measured against SynthStrip; (3) registration uses normalized cross-correlation against the MNI template, flagged at cohort mean − 2sigma with a floor of NCC < 0.40, and TalAviQA ≥ 0.96 for FreeSurfer outputs; (4) segmentation uses MRIQC-style WM–GM contrast-to-noise and intracranial-volume tissue fractions, with wm autofix plus a partial recon-all for fragmentation and SynthSeg fallback for more than 50% missing labels; (5) surface monitors the topology-defect count (total number of surface vertices corrected by FreeSurfer's topology fixer), flagging outliers exceeding the cohort median + k · MAD (k ≈ 3). Each check generates a self-contained HTML dashboard served by a lightweight backend, with subject cards displaying three-view screenshots, the agent's proposed grade and metrics, and an interactive 3-D viewer (niivue embedded in-page). The agent computes metrics, localizes defects, generates fix candidates, builds dashboards, and applies the reviewer's chosen fix to the working copy; the human retains the pass/fail/dismiss authority, the choice among fix candidates, and the signed export. Hard cases move up a multi-tier path: the agent auto-flags and proposes a fix, a reviewer accepts or overrides it, and genuinely ambiguous subjects are escalated to a supervisor for in-depth inspection. Every interaction is immutably recorded in a centralized ledger.

The experiments validate each integrated skill. For DICOM to BIDS conversion, a small-batch pilot (50 subjects per cohort) achieved 100% success, and a large-scale run (500 subjects per cohort where available) achieved success rates from 96.5% to 99.8%, with every failure traced to source DICOM issues (malformed headers, missing phase-encoding metadata, or incomplete series) rather than the conversion tool. For infant structural connectivity (EBDS), QSIPrep completed 201/213 (94.4%), with all twelve non-completions being input-data defects (eight had DWI with no phase-encoding metadata, two had truncated DWI, one was missing bval/bvec, one had a corrupt multi-run DWI); on the 201 QC-valid inputs, ACT-Atropos produced a connectome for every subject (201/201, 100%), yielding 402 matrices across the AAL and dHCP atlases. For adult connectivity and surface reconstruction, the adult FC arm on the PPMI pilot cohort produced 98 Shen268 functional-connectivity matrices and 1,372 native XCP-D matrices for every subject with BOLD (98/98), all valid correlation matrices (100%: symmetric, unit diagonal, off-diagonal in [−1, 1], no NaNs); FreeSurfer surface reconstruction completed for 98/98 subjects (100%); the adult SC arm produced a final SIFT2-weighted connectome for 105/120 subjects (87.5%), with every non-completion being an input-data defect. For quality control detection and repair, the five-step structural QC was applied to 558 fMRIPrep+FreeSurfer subjects, with 160 flagged for review: 97 flagged at Step 1 (raw image), 133 flagged at Step 4 (segmentation), and the brain-extraction fix cut the skull-strip exclusion metric by a mean of 27% (range 16–43%); ANTs full SyN raised cross-correlation by 26% (NCC 0.62 → 0.78) on one flagged subject; on a 90-subject FreeSurfer subset, flagged surfaces carried a mean of 9,271 defect vertices against 2,499 for PASS surfaces (a 3.7× separation), and the surface repair reduced the Euler number by a mean of 9.3% on affected surfaces.

Deployment observations include: all 17 cohorts converted to BIDS and passed validation; the cohort-union config caught minority modalities (DWI/ASL) that a one-subject config would have dropped; series classification was 100% accurate on small test cohorts and 96.5–99.8% on large ones; the agent read modalities straight from BIDS and routed each cohort correctly; post-run checkpoints caught failures and resubmitted them without recomputing completed subjects; configuring the pipeline for the cluster meant editing one pipeline.env file; the pipeline enforced subject privacy through automated DICOM de-identification with 100% success across 1,518 validation subjects; data integrity was verified by per-file checksums detecting zero corruption events; and a centralized audit log recorded every executed command, software version, and exit code per subject.

The discussion argues that the agent operates at the critical intersections of the workflow, serving as a cognitive orchestrator rather than the underlying computational engine. Making QC a scripted, cohort-relative, human-signed stage is described as the most consequential reproducibility gain here. The three skills carry no assumptions about population or modality, and a new capability is a new reference procedure inside one of the three skills, not a rewrite. Limitations acknowledged include: the evaluation stresses heterogeneity, routing, and structural QC at scale rather than statistical robustness of downstream connectivity estimates; portability rests on the architecture and one config file; LLMs can still misclassify a series or misread an ambiguous acquisition; cohort-relative grading screens rather than diagnoses; and the validity of derived measures rests on the cited community tools. The conclusion states that NeuroPilot delivers three core advantages: it acts as a cognitive orchestrator for dynamic workflow routing, ensures infrastructure-agnostic portability across any hardware, and enforces a fully auditable quality control stage with rigorous human oversight, compressing months of manual pipeline engineering into a single week of automated orchestration.

Improvements for AI systems

Based on the paper, here are the specific improvements I can make to AI systems and what the improved system can do:

Improvement: Implement a four-part skill architecture where each capability includes: (a) natural-language applicability description, (b) on-demand reference procedures, (c) parameterized scripts with all paths as arguments, and (d) pre/post-run checkpoints.

What the improved AI can do:

Select the correct tool from natural-language context instead of hard-coded control flow
Verify inputs exist before executing (abort early to save compute) and confirm outputs after execution
Load only the minimal reference documentation needed for the current task, keeping working context small
Reuse the same skill across different computational environments (SLURM, local, server) by reading environment variables from a single config file

Improvement: Replace fixed global thresholds with a dual-threshold system: (a) statistical outlier detection relative to the cohort's empirical distribution (e.g., Iglewicz–Hoaglin modified Z-score, mean ± 2σ), and (b) conservative absolute floors as a safety net.

Improvement: Implement a three-tier decision system: (1) AI auto-flags and proposes fixes, (2) reviewer accepts/overrides, (3) ambiguous cases escalate to supervisor. Every interaction is recorded in an immutable, append-only ledger with reviewer identity, timestamp, and summary.

Improvement: Separate the three functions: (a) AI's own scripts detect and localize defects, (b) delegated specialized tools (FreeSurfer, ANTs) perform the actual numerical repairs, (c) a 3-D browser dashboard (niivue) handles visualization.

Improvement: Instead of building conversion configs from a single representative subject, aggregate the union of all distinct series across the entire cohort before writing the config.

Improvement: Implement a routing system that reads available modalities from BIDS metadata and cohort demographics (adult vs. infant) to select the optimal preprocessing pipeline.

Improvement: Implement system-wide idempotence where resubmission acts as a safe no-op, and automated daemons reclaim scratch storage upon detecting per-subject success sentinels.

Improvement: Detect DICOM files by looking for the DICM signature at byte 128 rather than relying on file extensions, and extract compressed archives into temporary workspaces without altering originals.

Improvement: Use a battery of complementary metrics per QC step: motion/artifact scores (IBIS/NIRAL 1–4), ghost ratio, coverage/FOV, intensity clipping, hole counts with modified Z, SynthStrip agreement, NCC against MNI, WM-GM contrast-to-noise, intracranial tissue fractions, and topology-defect vertex counts.

Improvement: Implement repair workflows with quantified before/after metrics: brain-mask expansion (27% mean reduction in skull-strip exclusion), ANTs SyN registration (NCC 0.62 → 0.78, 26% improvement), wm autofix with partial recon-all (Euler number reduced by 9.3% mean).

Improvement: Centralize all site-specific settings (container paths, FreeSurfer license, atlas directories, APPTAINER BIND mounts, execution backend defaults) into one pipeline.env file.

Improvement: Make reports mandatory: a conversion run that produces data but no conversion report.md counts as incomplete.