Can Artificial Intelligence be a Trustable Tool for Future Difficult-Treat Diseases?
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Published
Nov 24, 2025
Abstract
Artificial intelligence holds immense promise for combating the world’s most difficult-to-treat diseases by analyzing complex biological data, accelerating drug discovery, and personalizing treatments. However, trust remains the central challenge preventing full integration into clinical medicine. For AI to become reliably trusted, it must overcome issues of transparency, bias, fairness, accountability, and clinical validation. Its decisions must be explainable, its training datasets representative, and its performance rigorously proven in real-world settings. Regulatory bodies must establish standards for evolving AI systems, and ethical safeguards must ensure patient agency and equitable outcomes. The future lies not in replacing clinicians but in forming an effective human-AI partnership that enhances decision-making and improves patient care. With careful development and governance, AI could become a dependable tool that transforms the management of complex diseases worldwide.
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Keywords
Artificial Intelligence, Difficult-To-Treat Diseases, Medical Trust, Predictive Modeling, Personalized Medicine
Supporting Agencies
No funding source declared.
References
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Anderson, B., & Sutherland, E. (2024). Collective action for responsible AI in health. OECD Artificial Intelligence Papers. DOI: https://doi.org/10.1787/f2050177-en
Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: Focus on clinicians. Journal of Medical Internet Research, 22(6). DOI: https://doi.org/10.2196/15154
Dalal, J. (2025). Rare diseases and the unseen burden: A patient-centered perspective. Journal of Critical Care & Emergency Medicine, 1. DOI: https://doi.org/10.47363/jccem/2025(4)181
Eschenbach, W. J. von. (2021). Transparency and the black box problem: Why we do not trust AI. Philosophy & Technology, 34(4), 1607. DOI: https://doi.org/10.1007/s13347-021-00477-0
Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025). Artificial intelligence in healthcare and medicine: Clinical applications, therapeutic advances, and future perspectives. European Journal of Medical Research, 30(1). DOI: https://doi.org/10.1186/s40001-025-03196-w
Fahrner, L. J., Chen, E., Topol, E. J., & Rajpurkar, P. (2025). The generative era of medical AI [Review of The generative era of medical AI]. Cell, 188(14), 3648. DOI: https://doi.org/10.1016/j.cell.2025.05.018
Göktaş, P., & Grzybowski, A. (2025). Shaping the future of healthcare: Ethical clinical challenges and pathways to trustworthy AI. Journal of Clinical Medicine, 14(5), 1605. DOI: https://doi.org/10.3390/jcm14051605
Harald, H., Nicola, T., Babiloni, C., Filippo, B., L., B. K., L.W., B. A., Soo, B., Francesco, C. D., Cavedo, E., A., C. P., Colliot, O., Cristina-Maria, C., Bruno, D., Andrea, D., Stanley, D., Maria-Teresa, F., Nathalie, G., Genthon, R., Marie-Odile, H., … Lista, S. (2018). Revolution of Alzheimer precision neurology: Passageway of systems biology and neurophysiology. In Advances in Alzheimer’s disease. IOS Press. DOI: https://doi.org/10.3233/978-1-61499-876-1-45
Jha, D., Durak, G., Sharma, V., Keleş, E., Çiçek, V., Zhang, Z., Srivastava, A., Rauniyar, A., Hagos, D. H., Tomar, N. K., Miller, F. H., Topcu, A. E., Yazidi, A., Håkegård, J. E., & Bağcı, U. (2025). A conceptual framework for applying ethical principles of AI to medical practice. Bioengineering, 12(2), 180. DOI: https://doi.org/10.3390/bioengineering12020180
Kant, S., Deepika, D., & Roy, S. C. (2025). Artificial intelligence in drug discovery and development: Transforming challenges into opportunities, Discover Pharmaceutical Sciences, 1(1), 7. DOI: https://doi.org/10.1007/s44395-025-00007-3
Keyl, J., Keyl, P., Montavon, G., Hosch, R., Brehmer, A., Mochmann, L. H., Jurmeister, P., Dernbach, G., Kim, M., Koitka, S., Bauer, S., Bechrakis, N. E., Forsting, M., Führer‐Sakel, D., Glas, M., Grünwald, V., Hadaschik, B., Haubold, J., Herrmann, K., … Kleesiek, J. (2025). Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence. Nature Cancer, 6(2), 307. DOI: https://doi.org/10.1038/s43018-024-00891-1
Krishnan, G., Singh, S., Pathania, M., Gosavi, S., Abhishek, S., Parchani, A., & Dhar, M. (2023). Artificial intelligence in clinical medicine: Catalyzing a sustainable global healthcare paradigm [Review of Artificial intelligence in clinical medicine: Catalyzing a sustainable global healthcare paradigm]. Frontiers in Artificial Intelligence, 6. DOI: https://doi.org/10.3389/frai.2023.1227091
Minnis, H., Harmelen, A. van, Gajwani, R., Rizeq, J., Combet, E., Reynolds, R. M., Gillberg, C., Henderson, M., Ho, F. K., Mondelli, V., Pell, J. P., Smith, J., & Shiels, P. G. (2024). The bio-exposome: Intracellular processes, stress physiology and the environment. Nature Mental Health, 2(2), 132. DOI: https://doi.org/10.1038/s44220-023-00180-3
Reis, A., Malpani, R., Vayena, E., Majumder, P. P., Swaminathan, S., Pujari, S., Reeder, J., Bernardo, M., Shorbachi, N. A., Ema, A., Ghoulia, A., Gibson, J. K., Goodman, K. W., Hoven, J. van den, Jayaram, M., Jjingo, D., Leong, T.-Y., London, A. J., Marwala, T., … Zeng, Y. (2021). Ethics and governance of artificial intelligence for health: WHO guidance (p. 150). Technical University of Denmark. https://local.forskningsportal.dk/local/dki-cgi/ws/cris-link?src=ku&id=ku-e505319d-a404-4d93-bfa7-577735b36fdd&ti=ETHICS%20AND%20GOVERNANCE%20OF%20ARTIFICIAL%20INTELLIGENCE%20FOR%20HEALTH%20%3A%20WHO%20GUIDANCE
Sagona, M., Dai, T., Macis, M., & Darden, M. (2025). Trust in AI-assisted health systems and AI’s trust in humans, 2(1). DOI: https://doi.org/10.1038/s44401-025-00016-5
Seoni, S., Vicnesh, J., Salvi, M., Barua, P. D., Molinari, F., & Acharya, U. R. (2023). Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023) [Review of Application of uncertainty quantification to artificial intelligence in healthcare]. Computers in Biology and Medicine, 165, 107441. DOI: https://doi.org/10.1016/j.compbiomed.2023.107441
Serrano, D. R., Luciano, F. C., Anaya, B. J., Őngoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simón, J. M., Tomietto, G., Rapti, C., Ruiz, H. K., Rawat, S., Kumar, D., & Lalatsa, A. (2024). Artificial intelligence (AI) applications in drug discovery and drug delivery: Revolutionizing personalized medicine [Review of Artificial intelligence (AI) applications in drug discovery and drug delivery]. Pharmaceutics, 16(10), 1328. DOI: https://doi.org/10.3390/pharmaceutics16101328
Sharma, A., Lysenko, A., Jia, S., Boroevich, K. A., & Tsunoda, T. (2024). Advances in AI and machine learning for predictive medicine [Review of Advances in AI and machine learning for predictive medicine]. Journal of Human Genetics, 69(10), 487. DOI: https://doi.org/10.1038/s10038-024-01231-y
Siddiqui, Z. A., Vyas, N., Acharya, S., & Patel, S. (2025). Artificial intelligence in predictive analytics of patient outcomes and disease management (p. 48). DOI: https://doi.org/10.9734/bpi/aodhr/v2/5641
Yadav, S., Singh, A., Singhal, R., & Yadav, J. P. (2024). Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry. Intelligent Pharmacy, 2(3), 367. DOI: https://doi.org/10.1016/j.ipha.2024.02.009
How to Cite
Neumann, T. (2025). Can Artificial Intelligence be a Trustable Tool for Future Difficult-Treat Diseases?. Science Insights, 47(5), 2049–2053. https://doi.org/10.15354/si.25.re1231
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