Jul 2026· International Conference Computing Methodologies and Communication· pp. 1632-1637· 0 citations· 25 references
Abstract
The extensive adoption of electronic health records has necessitated the development of automated systems that are capable of understanding unstructured clinical documents. Medical records, such as lab results, radiology findings, and discharge summaries, thus make manual analysis a slow and error-prone process. The paper introduces an AI-driven medical report analysis framework that employs natural language processing and deep learning to automatically locate and interpret the clinically significant information. The system proposed in this paper first preprocesses the medical text to identify the major entities such as diseases, symptoms, and drugs, and then translates them into structured clinical data. An attention-based neural model is used to produce brief analytical summaries, which help clinical decision-making. Experimentally, it was found that the proposed system not only outperformed the manual process in accuracy but also reduced the time. The framework, therefore, increases the efficiency of healthcare and opens up the potential for better utilization of electronic medical records.
This paper summarizes the main challenges currently facing, including medical data privacy and labeling problems, interpretability and clinical credibility barriers of the model, and systemic barriers to multimodal fusion.
In recent years, natural language processing has become an important tool in healthcare for extracting useful information from unstructured clinical text such as electronic health records, physician notes, and medical literature. Deep learning has significantly improved the performance of NLP systems, enabling stronger results in tasks such as disease prediction, clinical decision support, and patient risk assessment. However, healthcare NLP still faces major challenges in real-world deployment. Clinical text is often noisy, fragmented, and inconsistent, which can reduce model reliability. In addition, deep learning models lack transparency, which limits their adoption by clinicians who require explainable outputs for clinical decision-making. Privacy and security also remain major barriers because patient data is highly sensitive and subject to strict legal and ethical requirements. Bias in training data can further lead to uneven performance across patient populations. This paper combines a literature review with a healthcare-oriented case study to examine these issues in real-world settings. The findings show that although deep learning offers strong potential for healthcare analytics, progress depends on solving problems related to data quality, interpretability, privacy, and domain adaptation.
Madhurima Kommuru, Swathi Thatraju, Appala Nooka Kumar Doodala· International Journal of Mac...· 0 citations
Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.
M. U. K. Gunawardhna, Pirunthavi Wijikumar, D. Weerasinghe· Sri Lankan Journal of Applie...· 0 citations
Clinical text is an important part of healthcare systems because it is used to store and manage patient information in documents such as discharge summaries, doctor notes, and diagnostic reports. Among these documents, discharge summaries are especially important because they provide a brief overview of a patient’s diagnosis, treatment procedures, medications, and follow-up instructions after hospitalization. These summaries are also useful for healthcare research and medical data analysis. However, strict privacy regulations and hospital policies restrict access to real clinical records, making it difficult for researchers to collect large datasets for developing and testing machine learning models in healthcare. To address this issue, this study proposes a framework for generating and validating synthetic clinical discharge summaries using transformer-based biomedical language models. Initially, the clinical text is preprocessed using cleaning, formatting, and tokenization techniques to improve consistency and readability. Biomedical language models such as BioBERT, RoBERTa, and DistilBERT are then used to generate contextual embeddings and capture semantic relationships within medical text. In addition, semantic similarity analysis, entailment-based validation, and faithfulness evaluation are applied to verify the consistency and reliability of the generated summaries while preserving patient privacy and maintaining clinical relevance.
Mohammad Imran, M. Irfan, Sajida Sultana.Sk et al.· 2026 7th International Confe...· 0 citations
The rapid growth of medical data and the increasing complexity of clinical diagnostics requires effective computational frameworks to help people in healthcare industry. This paper presents an integrated intelligent healthcare system which is aimed at improving disease prediction and personalized drug recommendations. Based on the recent advancements in Artificial Intelligence, the proposed system uses hybrid deep learning architecture. First, it makes use of Transformer-based Natural Language Processing (NLP) models to analyze clinical symptoms and Electronic Health Records (EHR), which greatly increases diagnostic accuracy compared to traditional decision-tree methods. Second, the system combines the Graph Neural Networks (GNNs) with an ontology driven databases like DrugBank and UMLS to provide safe and effective drug recommendations. This module specifically models drug to drug interactions and checks for safety using OpenFDA label data, which ensures reliable treatment planning. By merging symptom-based disease inference with the reinforcement learning based personalization, this approach fills some of the significant gaps in automated healthcare. It offers scalable solution for precision medicine and better resource allocation.
Gayathri Tippani, Datta Sai, K. Sai et al.· 2026 6th International Confe...· 0 citations
This approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships and successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support system.
U. Luke, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations