Aug 2026· Future Internet· Vol 18, pp. 409· 0 citations· 27 references
TL;DR
The Multi-Agent Readiness Score (MARS), a standardized evaluation framework assessing models across four structural dimensions, provides a crucial, reproducible metric to audit model maturity, guide system architecture, and ensure future models are structurally prepared for the rigorous regulatory demands of precision medicine workflows.
Abstract
The emergence of Large Language Models (LLMs) has significantly advanced computational biology, yet their integration into autonomous, multi-agent systems (MASs) and clinical workflows remains challenging due to systemic architectural fragmentation. To quantify the operational readiness and regulatory compliance of bioinformatics LLMs, we developed the Multi-Agent Readiness Score (MARS), a standardized evaluation framework assessing models across four structural dimensions: Governance & Accessibility, Biological Competence, Technical Maturity, and Agentic Orchestration. The framework incorporates compliance criteria from the EU AI Act, HL7 FHIR, HL7 CDA, and MyHealth@EU standards. To empirically validate this domain-agnostic methodology, we applied it to a highly mature subset of the field: a diverse cohort of 43 prominent genomic LLMs. Our assessment revealed a severe, industry-wide readiness gap: the majority of models fell into “Not Suitable” or “Research Prototype” tiers, lacking essential technical interfaces, structured communication schemas, and provenance tracking. Furthermore, the data demonstrated a ’competence-readiness gap’, where models scale in biological predictive competence without corresponding improvements in engineering utility. The primary barrier to scalable bioinformatics AI is no longer biological competence, but operational and architectural incompatibility. By quantifying integration friction, MARS provides a crucial, reproducible metric to audit model maturity, guide system architecture, and ensure future models are structurally prepared for the rigorous regulatory demands of precision medicine workflows.
It is argued that agentic bioinformatics should be assessed through workflow correctness rather than final-answer correctness alone, and the Function--Evidence--Validation (FEV) framework is introduced, which separates demonstrated workflow operations, traceable support for actions and claims, and use-case-specific validation.
The rapid adoption of large language models (LLMs) has accelerated the use of conversational agents in digital health. However, in regulated and safety-critical environments, challenges related to trust, governance, and controlled integration with clinical information systems continue to limit their practical deployment. In particular, reliance on model-level behavior alone is insufficient to ensure operational safety and compliance.This paper proposes a modular architectural pattern for governed orchestration of LLM-based conversational agents in healthcare. The approach integrates (i) explicit separation between institutional knowledge, structured clinical data, and session-level context, (ii) a configurable governance layer that decouples prompts, model parameters, and operational constraints from workflow implementation, and (iii) controlled interaction with clinical systems through rule-based, tool-restricted operations.The architecture was implemented using a low-code orchestration platform and evaluated in a test healthcare environment through controlled scenario-based testing. The evaluation included repeated execution of operational scenarios, inspection of workflow logs, and analysis of system behavior. Observations indicate consistent enforcement of architectural constraints, absence of unsupported data generation in tested scenarios, and response latency ranging from 5 to 10 seconds depending on the type of request.The results suggest that embedding governance and control mechanisms at the architectural level can improve transparency, traceability, and operational reliability of conversational agents in healthcare. The proposed approach highlights the importance of system-level design in supporting trustworthy AI beyond model-centric optimization.
José Trajano Mendes, Francisco Milton Mendes, Cláudia Leite Rolim Moreira· International Conference on...· 0 citations
Large language model (LLM)-based agents are increasingly being explored for healthcare tasks such as clinical decision support, care coordination and autonomous workflow execution. Beyond static pipelines, recent systems claim to self-evolve by adapting their tools, memory, reasoning, policy, context and coordination strategies over time. However, what constitutes true self-evolution in healthcare agents remains unclear and is inconsistently evaluated. In this work, we introduce a six-dimensional taxonomy to rigorously characterize self-evolving LLM agents in healthcare, comprising Tools & Capabilities, Memory & Knowledge, Context & Prompt, Policy & Strategy, Multi-Agent Coordination and Reasoning. Using this taxonomy, we systematically evaluate 18 representative healthcare agent papers under strict mechanism-level criteria that separate genuine self-evolution from surface-level performance improvements. Our results reveal that memory evolution is the most common capability with over half of the surveyed systems exhibiting reflective or distilled memory growth, while only 5.6% demonstrates memory refactoring. In contrast, prompt self-revision appears in 33.3% of the papers and tool-selection evolution appears in only 5.6%. Multi-agent architectures (i.e., systems with multiple interacting agent roles/modules) are frequent, yet learned coordination remains limited, with only 16.7% exhibiting evolving protocols or topologies. We find no evidence of formal policy optimization or reasoning-mechanism evolution across these 18 papers. Overall, our analysis shows a clear gap between claimed adaptivity and verifiable self-evolution in current healthcare agents. This taxonomy provides a principled foundation for evaluating future self-evolving LLM systems and supports more rigorous, reproducible progress toward adaptive clinical AI.
This systematic review assessed 64 fashion datasets identified through a PRISMA 2020-guided two-stream search, evaluating each on FAIR compliance, a composite AI-Readiness Score, and a five-level ontology maturity model. The AI-Readiness assessment yielded a grade distribution concentrated in the middle tiers (B: 60.3%, C: 36.5%), while 79.7% of datasets provided only flat attribute annotations (Level 2 or below) on the ontology maturity scale. Beyond diagnosis, the review distills its findings into evidence-derived recommendations for future dataset construction — most consequentially, that new fashion datasets should adopt machine-readable open licenses with persistent identifiers, and should structure annotations at least at the Taxonomy level (Level 3) of the proposed maturity model. Together, the three assessment perspectives — FAIR compliance, AI-readiness, and ontology maturity — and these recommendations provide actionable tools for evaluating future data investments and guiding the development of next-generation fashion AI datasets.
Y. Lee, Mi kyoung Kim, Seung-Yeul Ji· Frontiers of Computer Scienc...· 0 citations
The operationalization of machine learning (ML) introduces distinct engineering and lifecycle management challenges—such as extreme data dependence, silent model degradation (concept drift), and inherent non-determinism—which traditional software engineering workflows fail to adequately address. This systematic literature review provides a rigorous, comprehensive mapping of the ML lifecycle domain between 2015 and 2025 using the PRISMA protocol. Out of an initial pool of 12,450 articles, a highly specialized cohort of 22 primary studies was extracted, classified, and synthesized to map out contemporary Machine Learning Operations (MLOps) patterns, technical debt structures, governance models, and security vulnerabilities. To address the documented “production gap,” this paper formalizes the findings into a synthesized operational mapping and introduces a preliminary conceptual layout for an Adaptive Lifecycle Framework (ALF), juxtaposing it with legacy paradigms like CRISP-DM. Furthermore, we expand the scope to investigate domain-specific lifecycle complexities in healthcare systems and Large Language Model (LLM) pipelines, providing an essential evolutionary baseline for sustainable MLOps.
The rapid evolution of large language models (LLMs) has introduced new challenges in model development, deployment, monitoring, and governance. Traditional software-focused Continuous Integration (CI) pipelines are insufficient for managing the iterative and data-intensive lifecycle of LLMs, which require continuous data validation, model retraining, bias and safety auditing, reproducibility checks, and scalable deployment. This paper proposes a comprehensive CI pipeline architecture tailored to the unique requirements of LLM lifecycle management. The framework integrates automated data quality assessment, modular training workflows, version-controlled model artifacts, continuous evaluation against multi-dimensional metrics, and responsible AI checks including fairness, robustness, and alignment. We discuss implementation patterns using modern MLOps tooling, highlight operational challenges, and present best practices for ensuring reliability, traceability, and ethical compliance in LLM-centric systems. The proposed approach facilitates faster iteration cycles, safer model updates, and more efficient long-term governance of LLM deployments.
H. Mohamed· International Journal of Art...· 0 citations
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