Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models

Ufuk Burak Karcıoğlu

TR EN

Simbiyotik Yapay Zeka Teşhisi: Klinik Karar Destek Sistemlerinde Makine Öğrenmesi Modellerinin Hekimlere Gerçek Zamanlı Eğitimi İçin Bir Tersine Mentorluk Çerçevesi

Öz

Bu çalışma, yapay zekâ destekli klinik karar destek sistemlerinde (KKDS) “tersine mentorluk” yaklaşımını temel alan yeni bir kavramsal çerçeve olan Simbiyotik Yapay Zekâ Tanı (Symbiotic AI Diagnosis) modelini önermektedir. Geleneksel yaklaşımlarda yapay zekâ klinik kararları destekleyici bir araç olarak konumlanırken, bu çalışma yapay zekânın aynı zamanda hekimler için gerçek zamanlı bir eğitim aracı olarak işlev görebileceğini savunmaktadır. Açıklanabilir yapay zekâ (XAI) mekanizmaları aracılığıyla, modeller yalnızca tahmin üretmekle kalmayıp, bu tahminlerin gerekçelerini de sunarak klinik öğrenmeyi destekleyen bir “öğretici” rol üstlenmektedir. Literatürün hedefe yönelik sentezi ile geliştirilen bu kavramsal model, yapay zekâ ile klinisyen arasında çift yönlü bir öğrenme döngüsü tanımlamaktadır. Bu döngü; açıklama sunumu, klinisyen yansıması ve model geri bildirime dayalı adaptasyon aşamalarından oluşmaktadır. Model, klinik karar kalibrasyonu, bilgi aktarımı ve sezgisel gelişim gibi ölçütlerle yapay zekânın eğitimsel etkisini değerlendirmeyi önermektedir. Sonuç olarak çalışma, yapay zekânın yalnızca karar destekleyici değil, aynı zamanda sürekli mesleki gelişimi destekleyen bir “öğretici ortak” olarak yeniden konumlandırılmasının, daha şeffaf, etkili ve insan merkezli sağlık hizmetlerine katkı sağlayabileceğini ileri sürmektedir.

Anahtar Kelimeler

Yapay zekâ, Sağlıkta dijital dönüşüm, Makine Öğrenme

Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models

Öz

This study proposes a novel conceptual framework, Symbiotic AI Diagnosis, grounded in the principle of reverse mentorship within AI-driven clinical decision support systems (CDSS). While traditional approaches position artificial intelligence as an assistive tool for clinicians, this work argues that AI can also function as a real-time educational agent for physicians. Through explainable AI (XAI) mechanisms, machine learning models not only generate predictions but also provide interpretable explanations, thereby transforming CDSS into active learning systems. Based on a targeted synthesis of the literature, the proposed framework defines a bidirectional learning loop between AI systems and clinicians, consisting of three iterative phases: explanation delivery, clinician reflection and action, and feedback-driven model adaptation. The framework introduces novel evaluation dimensions such as diagnostic calibration, knowledge transfer efficacy, and heuristic evolution to assess the educational impact of AI in clinical settings. It is argued that reframing AI as a “teaching partner” rather than a passive decision-support tool can enhance continuous professional development, improve clinical reasoning, and strengthen human–AI collaboration. In conclusion, the Symbiotic AI Diagnosis model offers a shift from augmentation-oriented AI toward a learning-oriented paradigm, with the potential to foster more transparent, adaptive, and human-centered healthcare systems.

Anahtar Kelimeler

Artificial Intelligence, Mentoring, Machine Learning

Kaynakça

1. OECD. (2024a). Artificial intelligence and the health workforce. OECD Publishing. https://doi.org/10.1787/9a31d8af- en
2. World Health Organization. (2024). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. https://www.who.int/publications/i/ item/9789240084759
3. OECD. (2024b). AI in health: Huge potential, huge risks. OECD Publishing.
4. Tun, H. M., Rahman, H. A., Naing, L., & Malik, O. A. (2025). Trust in artificial intelligence–based clinical decision support systems among health care workers: systematic review. Journal of Medical Internet Research, 27, e69678.https://doi. org/10.2196/69678
5. Yeo, H. J., Noh, D., Kim, T. H., Jang, J. H., Lee, Y. S., Park, S., ... & Kang, H. K. (2024). Development and validation of a machine learning-based model for post-sepsis frailty. ERJ Open Research, 10(5). https://doi.org/10.1183/23120541.00166-2024
6. World Health Organization Regional Office for Europe. (2025). [Readiness assessment and AI integration in European health systems].
7. Tenajas, Rebeca, and David Miraut. “1) Sustainable Communication Workflows Through Smart Message Delegation in Public Healthcare.” (2025).
8. World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. https://www. who.int/publications/i/item/9789240029200
9. Huang, Junyan, et al. “Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.” Discover Oncology 16.1 (2025): 1625. doi: 10.1007/s12672-025-03307-3
10. Elhaddad M, Hamam S. AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential. Cureus. 2024 Apr 6;16(4):e57728. doi: 10.7759/cureus.57728

11. Marcinkus Murphy, Wendy. “Reverse mentoring at work: Fostering cross-generational learning and developing millennial leaders.” Human resource management 51.4 (2012): 549-573.
12. Giebel GD, Raszke P, Nowak H, Palmowski L, Adamzik M, Heinz P, Tokic M, Timmesfeld N, Brunkhorst FM, Wasem J, Blase N. Improving AI-Based Clinical Decision Support Systems and Their Integration into Care From the Perspective of Experts: Interview Study Among Different Stakeholders. JMIR Med Inform. 2025 Jul 7;13:e69688. doi: 10.2196/69688.
13. Caterson J, Lewin A, Williamson E. The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review. Digit Health. 2024 Oct 30;10:20552076241272657. doi: 10.1177/20552076241272657.
14. Bragazzi, I., Fekih-Romdhane, F., Hallit, S., Loch, A. A., Glenn, J. M., Fessi, M. S., ... & Ben Saad, H. (2024). ChatGPT is not ready yet for use in providing mental health assessment and interventions. Frontiers
15. Athukorala, V. S., & Ilmini, W. M. K. S. (2026). Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/ s12911-026-03344-0
16. Salimparsa, Mozhgan, et al. “Explainable AI for clinical decision support systems: literature review, key gaps, and research synthesis.” Informatics. Vol. 12. No. 4. MDPI, 2025.
17. Yang, X., Liu, J., Huang, X., Liang, H., Cui, P., He, S., ... & Huang, J. (2025). Machine learning-driven clinical decision support for low bone mineral density: a web-based prediction model with explainable AI integration. Bone, 117592. https:// doi.org/10.1016/j.bone.2025.117592
18. Bienefeld, N., Boss, J. M., Lüthy, R., Brodbeck, D., Azzati, J., Blaser, M., ... & Keller, E. (2023). Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals. npj digital medicine, 6(1), 94. https://doi.org/10.1038/s41746- 023-00837-4
19. Duesing, C., Cimiano, P., Rehberg, S., Scherer, C., Kaup, O., Koester, C., ... & Borgstedt, R. (2024). Integrating federated learning for improved counterfactual explanations in clinical decision support systems for sepsis therapy. Artificial Intelligence in Medicine, 157, 102982. https://doi.org/10.1016/j. artmed.2024.102982
20. Abbas Q, Jeong W, Lee SW. Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges. Healthcare (Basel). 2025 Aug 29;13(17):2154. doi: 10.3390/healthcare13172154.
21. Boussina A, Krishnamoorthy R, Quintero K, Joshi S, Wardi G, Pour H, Hilbert N, Malhotra A, Hogarth M, Sitapati AM, VanDenBerg C, Singh K, Longhurst CA, Nemati S. Large Language Models for More Efficient Reporting of Hospital Quality Measures. NEJM AI. 2024 Oct 24;1(11):10.1056/ aics2400420. doi: 10.1056/aics2400420.
22. Garcia-Gomez JM, Blanes-Selva V, Alvarez Romero C, de Bartolomé Cenzano JC, Pereira Mesquita F, Pazos A, Doñate-Martínez A. Mitigating patient harm risks: A proposal of requirements for AI in healthcare. Artif Intell Med. 2025 Sep;167:103168. doi: 10.1016/j.artmed.2025.103168.
23. Rosenbacke R, Melhus Å, McKee M, Stuckler D. AI and XAI second opinion: the danger of false confirmation in human- AI collaboration. J Med Ethics. 2025 May 21;51(6):396-399. doi: 10.1136/jme-2024-110074
24. Yang, C. C. (2022). Explainable artificial intelligence for predictive modeling in healthcare. Journal of healthcare informatics research, 6(2), 228-239. https://doi.org/10.1007/ s41666-022-00114-1
25. Yang, Y., & Liu, X. (2026). Application of explainable artificial intelligence integrating with electronic health record in oncology. Exploration of Targeted Anti-tumor Therapy, 7, 1002357. https://doi.org/10.37349/etat.2026.1002357

Ayrıntılar

Birincil Dil

İngilizce

Konular

Sağlık Yönetimi

Bölüm

Derleme

Yazarlar

Ufuk Burak Karcıoğlu ^*
0009-0006-9131-6407
Türkiye

Yayımlanma Tarihi

29 Temmuz 2026

Gönderilme Tarihi

27 Nisan 2026

Kabul Tarihi

21 Temmuz 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 7 Sayı: 3

IZ

https://izlik.org/JA67WJ45FW

APA

Karcıoğlu, U. B. (2026). Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models. Yeni Yüzyıl Journal of Medical Sciences, 7(3), 106-118. https://izlik.org/JA67WJ45FW

AMA

1.Karcıoğlu UB. Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models. JMS. 2026;7(3):106-118. https://izlik.org/JA67WJ45FW

Chicago

Karcıoğlu, Ufuk Burak. 2026. “Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models”. Yeni Yüzyıl Journal of Medical Sciences 7 (3): 106-18. https://izlik.org/JA67WJ45FW.

EndNote

Karcıoğlu UB (01 Temmuz 2026) Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models. Yeni Yüzyıl Journal of Medical Sciences 7 3 106–118.

IEEE

[1]U. B. Karcıoğlu, “Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models”, JMS, c. 7, sy 3, ss. 106–118, Tem. 2026, [çevrimiçi]. Erişim adresi: https://izlik.org/JA67WJ45FW

ISNAD

Karcıoğlu, Ufuk Burak. “Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models”. Yeni Yüzyıl Journal of Medical Sciences 7/3 (01 Temmuz 2026): 106-118. https://izlik.org/JA67WJ45FW.

JAMA

1.Karcıoğlu UB. Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models. JMS. 2026;7:106–118.

MLA

Karcıoğlu, Ufuk Burak. “Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models”. Yeni Yüzyıl Journal of Medical Sciences, c. 7, sy 3, Temmuz 2026, ss. 106-18, https://izlik.org/JA67WJ45FW.

Vancouver

1.Ufuk Burak Karcıoğlu. Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models. JMS [Internet]. 01 Temmuz 2026;7(3):106-18. Erişim adresi: https://izlik.org/JA67WJ45FW

Simbiyotik Yapay Zeka Teşhisi: Klinik Karar Destek Sistemlerinde Makine Öğrenmesi Modellerinin Hekimlere Gerçek Zamanlı Eğitimi İçin Bir Tersine Mentorluk Çerçevesi

Öz

Anahtar Kelimeler

Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

IZ

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