Blockchain-Driven Traceability for AI Algorithms in Healthcare Diagnostics
Abstract
This research explores the integration of blockchain to enhance the traceability and accountability of AI algorithms used in healthcare diagnostics. By recording AI model training, updates, and decision-making processes on a blockchain, the system ensures transparency and compliance with regulatory standards. A use case involving AI-powered cancer detection demonstrates the framework's effectiveness in fostering trust and reducing biases. The study advocates for the adoption of blockchain to promote ethical AI usage in healthcare.
References
Davuluri, M. (2024). An Overview of Natural Language Processing in Analyzing Clinical Text Data for Patient Health Insights. Research-gate journal, 10(10).
Davuluri, M., & Yarlagadda, V. S. T. (2024). NOVEL DEVICE FOR ENHANCING TUBERCULOSIS DIAGNOSIS FOR FASTER, MORE ACCURATE SCREENING RESULTS. International Journal of Innovations in Engineering Research and Technology, 11(11).
Davuluri, M. (2022). Comparative Study of Machine Learning Algorithms in Predicting Diabetes Onset Using Electronic Health Records. Research-gate journal, 8(8).
Vattikuti, M. C. (2024). Improving Drug Discovery and Development Using AI: Opportunities and Challenges. Research-gate journal, 10(10).
Vattikuti, M. C. (2022). Comparative Analysis of Deep Learning Models for Tumor Detection in Medical Imaging. Research-gate journal, 8(8).
Vattikuti, M. C. (2020). A Comprehensive Review of AI-Based Diagnostic Tools for Early Disease Detection in Healthcare. Research-gate journal, 6(6).
Kolla, V. R. K. (2020). Forecasting the Future of Crypto currency: A Machine Learning Approach for Price Prediction. International Research Journal of Mathematics, Engineering and IT, 7(12).
Kolla, V. R. K. (2018). Forecasting the Future: A Deep Learning Approach for Accurate Weather Prediction. International Journal in IT & Engineering (IJITE).
Kolla, V. R. K. (2016). Analyzing the Pulse of Twitter: Sentiment Analysis using Natural Language Processing Techniques. International Journal of Creative Research Thoughts
Velaga, S. P. (2014). DESIGNING SCALABLE AND MAINTAINABLE APPLICATION PROGRAMS. IEJRD-International Multidisciplinary Journal, 1(2), 10.
Velaga, S. P. (2016). LOW-CODE AND NO-CODE PLATFORMS: DEMOCRATIZING APPLICATION DEVELOPMENT AND EMPOWERING NON-TECHNICAL USERS. IEJRD-International Multidisciplinary Journal, 2(4), 10.
Velaga, S. P. (2017). “ROBOTIC PROCESS AUTOMATION (RPA) IN IT: AUTOMATING REPETITIVE TASKS AND IMPROVING EFFICIENCY. IEJRD-International Multidisciplinary Journal, 2(6), 9.
Gatla, T. R. An innovative study exploring revolutionizing healthcare with ai: personalized medicine: predictive diagnostic techniques and individualized treatment. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320-2882.
Gatla, T. R. ENHANCING CUSTOMER SERVICE IN BANKS WITH AI CHATBOTS: THE EFFECTIVENESS AND CHALLENGES OF USING AI-POWERED CHATBOTS FOR CUSTOMER SERVICE IN THE BANKING SECTOR (Vol. 8, No. 5). TIJER–TIJER–INTERNATIONAL RESEARCH JOURNAL (www. TIJER. org), ISSN: 2349-9249.
Gatla, T. R. (2017). A SYSTEMATIC REVIEW OF PRESERVING PRIVACY IN FEDERATED LEARNING: A REFLECTIVE REPORT-A COMPREHENSIVE ANALYSIS. IEJRD-International Multidisciplinary Journal, 2(6), 8.
Gatla, T. R. (2019). A CUTTING-EDGE RESEARCH ON AI COMBATING CLIMATE CHANGE: INNOVATIONS AND ITS IMPACTS. INNOVATIONS, 6(09).
Pindi, V. (2018). NATURAL LANGUAGE PROCESSING(NLP) APPLICATIONS IN HEALTHCARE: EXTRACTING VALUABLE INSIGHTS FROM UNSTRUCTURED MEDICAL DATA. International Journal of Innovations in Engineering Research and Technology, 5(3), 1-10.
Pindi, V. (2019). A AI-ASSISTED CLINICAL DECISION SUPPORT SYSTEMS: ENHANCING DIAGNOSTIC ACCURACY AND TREATMENT RECOMMENDATIONS. International Journal of Innovations in Engineering Research and Technology, 6(10), 1-10.
PINDI, V. (2022). ETHICAL CONSIDERATIONS AND REGULATORY COMPLIANCE IN IMPLEMENTING AI SOLUTIONS FOR HEALTHCARE APPLICATIONS. IEJRD-International Multidisciplinary Journal, 5(5), 11.
Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).
Deekshith, A. (2021). Data engineering for AI: Optimizing data quality and accessibility for machine learning models. International Journal of Management Education for Sustainable Development, 4(4), 1-33.
Boppiniti, S. T. (2021). Real-time data analytics with ai: Leveraging stream processing for dynamic decision support. International Journal of Management Education for Sustainable Development, 4(4).
Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).
Boppiniti, S. T. (2019). Machine Learning for Predictive Analytics: Enhancing Data-Driven Decision-Making Across Industries. International Journal of Sustainable Development in Computing Science, 1(3).
