An Ethical AI Approach for Disease Diagnosis Using Prompt-Based History Summarisation
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Abstract
Medical history is a cornerstone of accurate diagnosis and effective treatment planning. In light of recent challenges such as the COVID-19 pandemic, the need for efficient, reliable, and secure history-taking methods has become more pressing than ever. Traditional approaches are often time-consuming, prone to human error, and reliant on subjective interpretation. This study proposes an AI-driven solution that automates patient history collection using prompt-based techniques, transforming complex, unstructured data into concise and coherent summaries. Ethical compliance and data privacy are central to the system's design, ensuring the secure handling of sensitive patient information. The integration of artificial intelligence, machine learning, and deep learning enhances both the accuracy of disease prediction and the overall efficiency of healthcare delivery. Through expert validation and responsible implementation, this research highlights the potential of generative AI to support early breast cancer diagnosis and improve the quality of patient care. We evaluated the fluency and semantic alignment of clinical text fields using auxiliary metrics, including ROUGE, BLEU, and BERTScore. Additionally, SHAP-based feature attribution was incorporated to interpret multi-class predictions. Together, these improvements provide a strong semantic foundation for evaluating and filtering decision support in medical AI systems.
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Journal of Studies in Science and Engineering is licensed under a Creative Commons Attribution License 4.0 (CC BY-4.0).
References
F. Fukuzawa, Y. Yanagita, D. Yokokawa, S. Uchida, S. Yamashita, Y. Li, K. Shikino, T. Tsukamoto, K. Noda, T. Uehara, and M. Ikusaka, “Importance of patient history in artificial intelligence-assisted medical diagnosis: Comparison study,” JMIR Medical Education, vol. 10, p. e52674, 2024, doi: https://doi.org/10.2196/52674.
C. Lee, S. W. Fu, S. J. Liao, et al., “Prospects for AI clinical summarization to reduce the burden of patient chart review,” Frontiers in Digital Health, vol. 6, p. 1475092, 2024, doi: https://doi.org/10.3389/fdgth.2024.1475092.
D. Van Veen et al., “Adapted large language models can outperform medical experts in clinical text summarization,” Nature Medicine, vol. 30, no. 4, pp. 1134–1142, Apr. 2024, doi: https://doi.org/10.1038/s41591-024-02855-5.
P. Gupta and M. K. Pandey, “Role of AI for smart health diagnosis and treatment,” in Smart Medical Imaging for Diagnosis and Treatment Planning, N. Dey, B. Misra, and S. Chakraborty, Eds. Boca Raton, FL, USA: Chapman & Hall/CRC, 2024, pp. 23–45, doi: https://doi.org/10.1201/9781003464884-4.
L. Ting, M. Khan, A. Sharma, and M. D. Ansari, “A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing,” Journal of Intelligent Systems, vol. 31, no. 1, pp. 221–236, 2022, doi: https://doi.org/10.1515/jisys-2022-0012.
A. K. Dubey, A. K. Sinhal, and R. Sharma, “An improved auto categorical PSO with ML for heart disease prediction,” Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8567–8573, Jun. 2022, doi: https://doi.org/10.48084/etasr.4854.
S. Nuanmeesri and W. Sriurai, “Multi-layer perceptron neural network model development for chili pepper disease diagnosis using filter and wrapper feature selection methods,” Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7714–7719, Oct. 2021, doi: https://doi.org/10.48084/etasr.4383.
D. Soria, J. M. Garibaldi, F. Ambrogi, E. M. Biganzoli, and I. O. Ellis, “A ‘non-parametric’ version of the naive Bayes classifier,” Knowledge-Based Systems, vol. 24, no. 6, pp. 775–784, 2011, doi: https://doi.org/10.1016/j.knosys.2011.02.014.
J. Zaghir, M. Naguib, M. Bjelogrlic, A. Névéol, X. Tannier, and C. Lovis, “Prompt engineering paradigms for medical applications: Scoping review,” Journal of Medical Internet Research, vol. 26, p. e60501, 2024, doi: https://doi.org/10.2196/60501.
D. A. Mahmood and S. A. Aminfar, “Efficient machine learning and deep learning techniques for detection of breast cancer tumor,” BioMed Target Journal, vol. 2, no. 1, pp. 1–13, 2024, doi: https://doi.org/10.59786/bmtj.211.
J. Jonnagaddala and Z. Shui-Yee Wong, “Privacy-preserving strategies for electronic health records in the era of large language models,” npj Digital Medicine, vol. 8, art. no. 34, 2025, doi: https://doi.org/10.1038/s41746-025-01429-0.
M. Y. Ng et al., “Development of secure infrastructure for advancing generative artificial intelligence research in healthcare at an academic medical center,” Journal of the American Medical Informatics Association, vol. 32, no. 3, pp. 586–588, 2025, doi: https://doi.org/10.1093/jamia/ocaf005.
R. M. Abdullah, H. D. I. Masseh, A. Salihi, and B. M. Faraj, “Unleashing the power of OpenAI in shaping the future of cancer research,” BioMed Target Journal, vol. 1, no. 1, pp. 2–11, 2023, doi: https://doi.org/10.59786/bmtj.112.
M. M. Khan, N. Shah, N. Shaikh, A. Thabet, T. Alrabayah, and S. Belkhair, “Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges,” International Journal of Medical Informatics, vol. 195, Art. no. 105780, Mar. 2025, doi: https://doi.org/10.1016/j.ijmedinf.2024.105780 .
W. Badawy, H. Zinhom, and M. Shaban, “Navigating ethical considerations in the use of artificial intelligence for patient care: A systematic review,” International Nursing Review, vol. 72, no. 3, Art. no. e13059, Sep. 2025, doi: https://doi.org/10.1111/inr.13059.
H.-J. Dai et al., “Leveraging large language models for the deidentification of clinical notes,” npj Digital Medicine, 2025, doi: https://doi.org/10.1038/s41746-025-01921-7.
M. Fathima and M. Moulana, “Revolutionizing breast cancer care: AI-enhanced diagnosis and patient history,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 28, no. 5, pp. 642–654, 2025, doi: https://doi.org/10.1080/10255842.2023.2300681.
P. Rajesh, A. Murugan, B. Muruganantham, and S. Ganesh Kumar, “Lung cancer diagnosis and treatment using AI and mobile applications,” International Journal of Interactive Mobile Technologies, vol. 14, no. 17, pp. 189–203, 2020, doi: https://doi.org/10.3991/ijim.v14i17.16607.
N. Bhandari, R. Walambe, K. Kotecha, and M. Kaliya, “Integrative gene expression analysis for the diagnosis of Parkinson’s disease using machine learning and explainable AI,” Computers in Biology and Medicine, vol. 163, p. 107140, 2023, doi: https://doi.org/10.1016/j.compbiomed.2023.107140.
S. R. Babu, N. B. S., V. Kumar, A. Divya, and B. Thanuja, “AI-driven healthcare: Predictive analytics for disease diagnosis and treatment,” International Journal for Modern Trends in Science and Technology, vol. 10, no. 6, pp. 5–9, 2024, doi: https://doi.org/10.46501/IJMTST1006002.
P. Sahoo, A. K. Singh, S. Saha, V. Jain, S. Mondal, and A. Chadha, “A systematic survey of prompt engineering in large language models: Techniques and applications,” arXiv preprint arXiv:2402.07927, 2024, doi: https://doi.org/10.48550/arXiv.2402.07927.
H. H. S. Junaid, F. Daneshfar, and M. A. Mohammad, “Automatic colorectal cancer detection using machine learning and deep learning based on feature selection in histopathological images,” Biomedical Signal Processing and Control, vol. 107, p. 107866, 2025, doi: https://doi.org/10.1016/j.bspc.2025.107866.
P. Yamini, F. Daneshfar, and A. Ghorbani, “KurdSM: Transformer-based model for Kurdish abstractive text summarization with an annotated corpus,” Iranian Journal of Electrical and Electronic Engineering, vol. 20, no. 4, pp. 8–22, 2024, doi: https://doi.org/10.22068/IJEEE.20.4.3299.
P. Rahman, F. Daneshfar, and H. Parvin, “Multi-objective manifold representation for opinion mining,” Expert Systems, vol. 42, 2025, doi: https://doi.org/10.1111/exsy.70092.