The increasing complexity of medical diagnosis and billing necessitates improving International Classification of Diseases (ICD) coding practices. It also requires partnerships between academia and industry to support rigor and implementation in real-world settings. We conducted an academic-industry collaboration involving four institutions to explore pretrained language models for artificial intelligence (AI)-driven ICD-10 coding in cancer-specific populations. We created a cancer-focused dataset comprising 101,224 clinical notes and 36,040 ICD code assignments across five note types, reflecting real-world scenarios. By fine-tuning and evaluating two existing pretrained language-model approaches designed to process long contexts, PLM-ICD and KEPTLongformer, we developed an institutional benchmark for oncology ICD-10 coding using 20 selected three-character ICD-10 parent codes. The best-performing model achieved an F1-macro of 0.768 and an F1-micro of 0.792 on the test set. Our feasibility analysis showed that integrating these models into clinical workflows could potentially reduce coding time by approximately 10 minutes per case, with a GPU runtime of 7 seconds. Additionally, an exploratory clinical-coder evaluation of attention-based model interpretability showed that 13 of 20 predictions contained high-attention tokens aligned with the corresponding ICD code descriptions; coders rated these tokens as "very helpful" for code assignment. The results demonstrate the potential of AI-driven coding support systems within clinical workflows.
M. Zitu, Dana Rollison, Kedar S. Kulkarni et al.· IEEE/ACM International Confe...· 0 citations
Recent years have witnessed a surge in FDA approved AI tools for healthcare applications. While this growth offers considerable potential benefits for clinical practice, it also introduces substantial challenges related to ethics, regulation, and patient safety. These challenges are further compounded by previously documented gaps in the regulatory approval pathway. These gaps include inconsistent pre-market evaluation practices, over-reliance on retrospective studies, and the limited systematic post-market surveillance of AI devices in real-world clinical settings. Using publicly available FDA data, we developed therefore an interactive web-based dashboard for assessing and predicting the performance of FDA-approved AI software, called PROACTIVE-AI, for the purpose of pro-viding the user with a structured guidance on the anticipated performance of AI-enabled medical devices in real-world clinical settings. The dashboard supports exploratory analysis by diverse stakeholders via knowledge graph visualization and longitudinal trend monitoring of performance indicators, including device recalls and safety-related issues. In addition, PROACTIVE-AI incorporates an AI-aided post-market surveillance risk assessment calculator, derived from historical recall data, to identify device characteristics and con-textual factors associated with elevated deployment risk. Our findings using the PROACTIVE-AI dashboard highlight some of the important challenges related to real-world monitoring and accountability of deployed AI medical devices. Furthermore, it illustrates the potential value of such dashboard in narrowing the trust gap surrounding AI in healthcare by providing quantitative metrics of expected clinical performance and recall-related risk factors.
Isadora Oliveira Grasel, Naveena Gorre, I. E. El Naqa· Research Square· 0 citations
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