Machine Intelligence in
Quality Assessment of Biomedical Data
Half-day workshop with invited talks, panel discussion and presentations by authors of selected papers.
The advancement of state-of-the-art (SOTA) Machine Intelligence (MI) in early disease diagnosis, treatment planning, and clinical decision-making is fundamentally dependent on the availability of high-quality, diverse, and reliable biomedical data. Despite the proliferation of both open-source and proprietary biomedical datasets, they frequently suffer from critical limitations such as missing values, sampling biases, measurement noise, and manual data entry errors. These issues can significantly impair the performance, reliability, and reproducibility of artificial intelligence (AI) models deployed in real-world healthcare scenarios. This workshop aims to explore and highlight the latest advancements in leveraging MI—spanning machine learning, deep learning, few-shot learning, reinforcement learning, meta-learning, cyber security and Internet of Medical Things (IoMT)—for the assessment, control, and enhancement of biomedical data quality.
Co-located with PReMI 2025 | IIT Delhi, India | Half-Day Workshop
We invite contributions that address data quality challenges across a broad spectrum of biomedical modalities including: physiological signals (e.g., ECG, EEG, EMG, PPG, PCG), medical imaging (e.g., MRI, CT, X-ray, ultrasound, angiography), endoscopic video data (e.g., colonoscopy, laparoscopy, WCE), and multi-omics datasets (e.g., genomics, proteomics, metabolomics).
Additionally, we encourage research focused on the integrity of textual and structured data found in electronic medical records (e.g., pre- and post-operative reports, clinical notes, audit logs). A key focus is placed on the detection and mitigation of artefacts, noise, and data doppelgängers—datasets that exhibit superficial similarity but differ in origin or quality—which can otherwise undermine AI model validity.
Submissions may include methodologies for automatic artefact detection, noise suppression, quality metric assessment, classification, segmentation, localization, detection, fusion, and computer scoring. Applications should target improvement in data quality for a variety of clinical domains such as neuropsychiatric, cardiovascular, gastrointestinal, nephrological, gynaecological, and hereditary diseases etc. We especially encourage studies utilizing or contributing open-source biomedical datasets. By encouraging innovation in MI-driven data quality control, this session aims to support the creation of accurate, reproducible, and ethically responsible AI in healthcare.
Advanced preprocessing and data enhancement techniques
Quality control and checks
Quality metrics and optimization
Data cleaning and precise annotations
Types of biases and mitigation
Benchmarking and reproducibility
AI-enabled integrity validations
Cybersecurity challenges
Cross-domain, multi-centre trials
Real-time quality monitoring in data acquisition systems
Quality-aware data augmentation techniques
Artefact and noise detection, suppression, addition
Automated quality scoring of biomedical data
Human-in-the-loop systems for clinical data validation
Synthetic data generation for data quality benchmarking
Multimodal data fusion for quality enhancement
Use of and contributions to open-source datasets
Ethical and reproducible AI models
Submit original, unpublished research papers (up to 8 pages, excluding references)
All submissions will undergo thorough peer review
Format: Follow the PReMI 2025 guidelines at https://premi25.iitd.ac.in/submission.html
Submission link: https://openreview.net/group?id=PReMI/2025/Workshop/MIQABD
Selected workshop papers will appear in an edited volume after PReMI.
Technical paper submission deadline: September 30, 2025 11:59PM IST
Notification of acceptance: October 15, 2025
Camera Ready deadline: October 25, 2025
Submission Guidelines
The Workshop on Machine Intelligence in Quality Assessment of Biomedical Data 2025 welcomes a wide range of contributions in the areas specified in the Call for Papers. When submitting a paper to the workshop, authors are required to specify one or more keywords from the list of topics outlined in the CFP. The Program Committee will endeavour to facilitate the presentation of papers from contributors worldwide.
At least one author of each paper must register for PReMI 2025 as mentioned in the PReMI guidelines.
Submissions should follow the norms, templates and guidelines of PReMI 2025.
Workshop papers will be of 8 pages (maximum) and must use the template given by the conference organizers.
Papers will be submitted via OpenReview. Each author must create a profile at OpenReview for submissions. Please note that as per OpenReview policy, new profiles created without an institutional email will go through a moderation process that can take up to two weeks and new profiles created with an institutional email will be activated automatically.
Paper submission link: https://openreview.net/group?id=PReMI/2025/Workshop/MIQABD
Organizing committee
Dean (Digital Education) and Full-Professor in Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India
Email:s.indu@dce.ac.in
Phone: +91-9868108678
Postdoctoral Scientist in Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems, Austria
Email: palak.handa@dp-uni.ac.at
Phone: +43 676 6121290, +91-8588813044
Reviewing committee
Assistant Professor at KIET Group of Institutions, India
Postdoctoral Scientist at Danube Private University, Austria
Full Stack Engineer at Fidelity Investments (US)