@inproceedings{wasi-ridoy-2026-position,
title = "Position: Real-World Clinical {NLP} Robustness Requires Messy, Multimodal, Longitudinal, Privacy-Preserving Corpora",
author = "Wasi, Azmine Toushik and
Ridoy, Shahriyar Zaman",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.59/",
pages = "1163--1180",
ISBN = "979-8-89176-444-6",
abstract = "Real-world healthcare data is inherently complex: noisy, incomplete, heterogeneous, and temporally irregular, making clinically robust AI development difficult. Traditional models, trained on idealized datasets, fail to capture these operational realities, limiting real-world performance. We propose the \textbf{\textit{Messy Clinic}} paradigm: a dataset and blueprint for building AI systems grounded in authentic clinical complexity. This framework enables seamless multimodal data integration across EHRs, imaging, genomics, and wearables, using privacy-preserving methods such as federated learning, synthetic data, and differential privacy. It provides (1) structured methodologies for longitudinal multimodal management, (2) AI techniques resilient to noise and missing data, and (3) governance mechanisms for consent, accountability, and bias mitigation. By embracing the ``Messy Clinic,'' healthcare AI can shift from artificial idealism to real-world robustness, accelerating research, enabling personalized care, and aligning AI with the true complexity of clinical practice."
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<abstract>Real-world healthcare data is inherently complex: noisy, incomplete, heterogeneous, and temporally irregular, making clinically robust AI development difficult. Traditional models, trained on idealized datasets, fail to capture these operational realities, limiting real-world performance. We propose the Messy Clinic paradigm: a dataset and blueprint for building AI systems grounded in authentic clinical complexity. This framework enables seamless multimodal data integration across EHRs, imaging, genomics, and wearables, using privacy-preserving methods such as federated learning, synthetic data, and differential privacy. It provides (1) structured methodologies for longitudinal multimodal management, (2) AI techniques resilient to noise and missing data, and (3) governance mechanisms for consent, accountability, and bias mitigation. By embracing the “Messy Clinic,” healthcare AI can shift from artificial idealism to real-world robustness, accelerating research, enabling personalized care, and aligning AI with the true complexity of clinical practice.</abstract>
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%0 Conference Proceedings
%T Position: Real-World Clinical NLP Robustness Requires Messy, Multimodal, Longitudinal, Privacy-Preserving Corpora
%A Wasi, Azmine Toushik
%A Ridoy, Shahriyar Zaman
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F wasi-ridoy-2026-position
%X Real-world healthcare data is inherently complex: noisy, incomplete, heterogeneous, and temporally irregular, making clinically robust AI development difficult. Traditional models, trained on idealized datasets, fail to capture these operational realities, limiting real-world performance. We propose the Messy Clinic paradigm: a dataset and blueprint for building AI systems grounded in authentic clinical complexity. This framework enables seamless multimodal data integration across EHRs, imaging, genomics, and wearables, using privacy-preserving methods such as federated learning, synthetic data, and differential privacy. It provides (1) structured methodologies for longitudinal multimodal management, (2) AI techniques resilient to noise and missing data, and (3) governance mechanisms for consent, accountability, and bias mitigation. By embracing the “Messy Clinic,” healthcare AI can shift from artificial idealism to real-world robustness, accelerating research, enabling personalized care, and aligning AI with the true complexity of clinical practice.
%U https://aclanthology.org/2026.findings-aacl.59/
%P 1163-1180
Markdown (Informal)
[Position: Real-World Clinical NLP Robustness Requires Messy, Multimodal, Longitudinal, Privacy-Preserving Corpora](https://aclanthology.org/2026.findings-aacl.59/) (Wasi & Ridoy, Findings 2026)
ACL