Piotr Kuszmiersz MD PhD
Poland
Piotr is a PhD in Medical Sciences and an Internal Medicine Specialist, currently undertaking his rheumatology training at the Department of Rheumatology and Immunology, Jagiellonian University Medical College in Kraków, Poland. His research interests focus on Patient-Reported Outcome Measures, epidemiology, and the application of AI in medicine. Piotr is a member of the EMEUNET Country Liaison Sub-Committee.
| Oral – PARE – OP0256-PARE | Thursday, 4 June 2026 PARE Abstract Session: Evidence meets Experience Author: : J. Knitza (Germany) Title: Turning Guidelines to Answers: Patient Evaluation of AI-Based Guideline Chatbots in Rheumatology  For the first time, patients with rheumatic diseases evaluated AI-based chatbots built on EULAR guidelines across multiple conditions. The study assessed accuracy, comprehensibility, and usability from the patient perspective. As patients increasingly seek health information online, this abstract asks a timely question: can AI reliably translate complex guidelines into accessible answers? The findings establish an important benchmark for future AI-driven patient support in rheumatology. |
| Oral – OP0344 | Friday, 5 June 2026 Clinical Abstract Sessions: Imaging Innovation in Rheumatoid Arthritis Author: : A. Saraux (France) Title: Artificial Intelligence-Based Analysis of 20-Year Radiographic Progression in the ESPOIR Rheumatoid Arthritis Inception Cohort  Radiographic scoring in RA requires two expert readers and is burdensome over long follow-up periods. This study applies AI-based automated scoring to the landmark 20-year ESPOIR inception cohort, demonstrating that AI can streamline structural damage assessment at scale. Beyond reducing workload, AI-driven radiographic analysis enables re-evaluation of existing large datasets, potentially transforming how long-term structural progression is measured in both research and clinical trials. |
| Oral – OP0261| Friday, 5 June 2026 Basic Abstract Sessions: Decoding the Immune System – OMICS and beyond Author: : K. Wójcik (Poland) Title: Stimulated Emission Depletion (STED) Microscopy for ANA Pattern Identification: First Dataset and Machine Learning Comparison with Fluorescence Microscopy Standard ANA pattern detection by indirect immunofluorescence is limited by inter-observer variability and resolution constraints. This study introduces STED super-resolution microscopy combined with machine learning for automated ANA pattern classification — the first dataset of its kind. By capturing finer cellular detail invisible to standard fluorescence, this innovative fusion of advanced microscopy and AI may uncover novel ANA patterns and substantially improve the reproducibility of autoantibody diagnostics. |
| Poster Tour – POS0148| Thursday, 4 June 2026 Clinical Poster Tours: Extinguishing the Fires of Inflammatory Arthritis Author: : S. Klapa (Germany) Title: Robot-Assisted Arthrosonography (ARTHUR) with AI Analysis (DIANA) for the Initial Diagnosis and Follow-Up of Rheumatoid Arthritis: Real-World Data from 255 Patients with Suspected Arthritis ARTHUR (robot-assisted arthrosonography) combined with DIANA (AI-driven ultrasound analysis) was evaluated for RA diagnosis and monitoring in 255 real-world patients with suspected arthritis. This proof-of-concept study demonstrates that standardized robotic ultrasound with AI interpretation is feasible in routine practice. As musculoskeletal ultrasound remains highly operator-dependent, robotic AI-assisted sonography could democratize access to specialist-level joint assessment and transform early RA diagnosis. |
| Poster Tour – POS0221| Friday, 5 June 2026 Clinical Poster Tours: Ouch! Pain in RMDs Author: : V. Venerito (Italy) Title: fAI-BRO: Multimodal AI Sentiment Analysis for Fibromyalgia Diagnosis – A Proof-of-Concept Study Fibromyalgia diagnosis is typically delayed by 5–7 years due to absent objective biomarkers. fAI-BRO integrates large language model (LLM)-based analysis of patient transcripts with AI-powered facial expression recognition in a novel multimodal diagnostic approach. By simultaneously capturing verbal and non-verbal pain cues, this proof-of-concept represents an innovative step towards objective, AI-assisted characterization of fibromyalgia — a condition that has long eluded objective measurement in clinical practice. |