AI-Enabled Text Mining: A Paradigm Shift in Disease Prediction, Drug Discovery, and Clinical Research

AI-enabled text mining transforms unstructured biomedical data into actionable clinical insights.
NLP and transformer models enhance disease prediction, drug discovery, and clinical research efficiency.
AI-driven systems achieve high diagnostic accuracy and accelerate drug development timelines
Automated text mining reduces systematic review time and improves clinical decision-making speed.
Challenges include data heterogeneity, bias, privacy concerns, and real-world implementation barriers.

KEYWORDS

1. Artificial intelligence

2. Text mining

3. Natural language processing

4. Data Mining

TOPICS

ABSTRACT

The exponential growth of unstructured biomedical data, comprising nearly 80% of healthcare information, has created both unprecedented opportunities and formidable challenges for extracting actionable insights from clinical notes, electronic health records, biomedical literature, and patient-reported outcomes. This review examines the evolution of AI-driven text mining from experimental application to clinical necessity, with emphasis on disease prediction, drug discovery, and clinical research. Literature encompassing natural language processing (NLP), transformer-based models, clinical case studies, and regulatory frameworks was systematically analyzed across oncology, cardiology, neurology, and pharmacovigilance domains. AI-enabled text mining demonstrates robust performance across multiple applications: disease prediction models achieve 67–98% accuracy in early diagnosis and risk stratification; transformer-based methods yield 80.6% F1-scores for drug–target interaction extraction; and adverse drug reaction detection from social media achieves 84.2% sensitivity and 98% specificity. In clinical research, systematic review timelines are reduced by up to 70%, and clinical trial recruitment screening requirements decline by nearly 80%. Real-time clinical decision support powered by large language models reduces diagnostic time from over 30 minutes to less than one minute, maintaining accuracy comparable to expert teams. Despite remarkable progress, challenges persist, including data heterogeneity, annotation quality, computational demands, translational gaps, algorithmic bias, and privacy concerns. Future directions include multimodal integration with genomics, imaging, and biosensors, explainable AI frameworks, and federated learning for collaborative research. AI-enabled text mining represents a transformative paradigm shift toward predictive, preventive, and personalized medicine, bridging the gap between exponential data growth and human cognitive limitations while improving patient outcomes and accelerating scientific discovery.

ABBREVIATIONS

AI – Artificial Intelligence
NLP – Natural Language Processing
EHR – Electronic Health Records
LLM – Large Language Model
ML – Machine Learning
DL – Deep Learning
ADR – Adverse Drug Reaction
DTI – Drug–Target Interaction
CDSS – Clinical Decision Support System
NER – Named Entity Recognition
F1 – F1 Score
AUC – Area Under Curve
RWE – Real-World Evidence
API – Application Programming Interface
TCGA – The Cancer Genome Atlas
GDPR – General Data Protection Regulation

ACKNOWLEDGEMENTS

The authors thankfully acknowledge the Electric Power Research Institute, 1300 W W T Harris Blvd, Charlotte, NC, USA for providing necessary facilities for performing this study.

FUNDING

This research received no external funding. The study was conducted without any financial support from public, commercial, or not-for-profit funding agencies. All resources utilized for this work were provided by the author respective institutions.

CONFLICT OF INTEREST

The authors declare that they have no known financial, personal, academic, or other relationships that could inappropriately influence, or be perceived to influence, the work reported in this manuscript. All authors confirm that there are no competing interests to declare.

PEER REVIEW INFORMATION

Article has been screened for originality

REFERENCES (75)

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