The Impact of Artificial Intelligence and Deep Learning on Health Informatics: A Literature-Based Review

Yıl 2025, Cilt: 8 Sayı: 2, 11 - 27, 29.08.2025

Ömer Çelik , Nezihe Tüfekci

https://doi.org/10.51536/tusbad.1702172

Öz

Artificial Intelligence (AI) and Deep Learning (DL) have a transformative impact on health informatics, reshaping the delivery, management, and decision-making processes of healthcare services. This study systematically examines the integration of AI and DL into health informatics based on a literature review. Technologies such as Electronic Health Records (EHR), big data analytics, and Clinical Decision Support Systems (CDSS) offer the potential to personalize patient care, enhance diagnostic accuracy, and optimize operational efficiency. Notably, deep learning has improved diagnostic precision and reduced clinicians' workload in areas such as medical imaging and chronic disease risk prediction. However, challenges such as data privacy, algorithmic bias, ethical dilemmas, and regulatory gaps hinder the widespread adoption of these technologies. The study emphasizes the need for interdisciplinary collaboration, algorithmic transparency, and standardized ethical frameworks to ensure the responsible implementation of AI. The findings indicate that AI and DL have the potential to create a human-centered, equitable, and sustainable ecosystem in health informatics, provided that technical, ethical, and societal dimensions are balanced.

Anahtar Kelimeler

Artificial intelligence , Clinical decision support systems , Deep learning , Digital technology , Medical informatics

Yapay Zeka ve Derin Öğrenmenin Sağlık Bilişimi Üzerine Etkisi: Literatür Tabanlı Bir İnceleme

Öz

Yapay zekâ (YZ) ve derin öğrenme (DÖ), sağlık bilişimi alanında dönüştürücü bir etkiye sahiptir ve sağlık hizmetlerinin sunum, yönetim ve karar verme süreçlerini yeniden şekillendirmektedir. Bu çalışma, YZ ve DÖ'nün sağlık bilişimine entegrasyonunu literatür temelinde sistematik bir şekilde incelemektedir. Elektronik sağlık kayıtları (ESK), büyük veri analitiği ve klinik karar destek sistemleri (KKDS) gibi teknolojiler, hasta bakımını kişiselleştirme, teşhis doğruluğunu artırma ve operasyonel verimliliği optimize etme potansiyeli sunmaktadır. Özellikle derin öğrenme, tıbbi görüntüleme ve kronik hastalık risk tahmini gibi alanlarda klinisyenlerin iş yükünü azaltarak teşhis hassasiyetini artırmıştır. Ancak, veri gizliliği, algoritmik önyargı, etik ikilemler ve düzenleyici boşluklar, bu teknolojilerin yaygınlaşmasını zorlaştırmaktadır. Çalışma, YZ'nin sorumlu bir şekilde uygulanması için disiplinler arası iş birliği, algoritma şeffaflığı ve etik çerçevelerin standartlaştırılması gerektiğini vurgulamaktadır. Sonuç, YZ ve DÖ'nün sağlık bilişiminde insan odaklı, adil ve sürdürülebilir bir ekosistem oluşturma potansiyeline sahip olduğunu; ancak, bu fırsatların teknik, etik ve toplumsal boyutlarla dengelenmesi gerektiğini göstermektedir.

Anahtar Kelimeler

Derin öğrenme , Dijital teknoloji , Klinik karar destek sistemleri , Tıbbi bilişim , Yapay zekâ

Etik Beyan

Makale etik kurallar çerçevesinde yazılmıştır. Derleme türünde makale olmasından dolayı etik kurul izni alınmamıştır.

Kaynakça

• 1. Collen MF, Shortliffe EH. The creation of a new discipline. The History of Medical Informatics in the United States. 2015:75-120.
• 2. Hersh W. Health Informatics. 2022.
• 3. Demirhan A, Güler İ. Bilişim ve sağlık. Bilişim Teknolojileri Dergisi. 2011;4(3):13-20.
• 4. Coiera E. Guide to health informatics: CRC press; 2015.
• 5. Yang H-L, Hsiao S-L. Mechanisms of developing innovative IT-enabled services: A case study of Taiwanese healthcare service. Technovation. 2009;29(5):327-37.
• 6. Fang R, Pouyanfar S, Yang Y, Chen S-C, Iyengar S. Computational health informatics in the big data age: a survey. ACM Computing Surveys (CSUR). 2016;49(1):1-36.
• 7. Gartner I. Glossary: big data. Gartner Inc Available (accessed on 137 2017): https://research gartner com/definition-whatis-bigdata. 2014:1-8163325102.
• 8. Sukumar SR, Natarajan R, Ferrell RK. Quality of Big Data in health care. International journal of health care quality assurance. 2015;28(6):621-34.
• 9. Ahmad HF, Rafique W, Rasool RU, Alhumam A, Anwar Z, Qadir J. Leveraging 6G, extended reality, and IoT big data analytics for healthcare: A review. Computer Science Review. 2023;48:100558.
• 10. Ahmed A, Xi R, Hou M, Shah SA, Hameed S. Harnessing big data analytics for healthcare: A comprehensive review of frameworks, implications, applications, and impacts. IEEE Access. 2023.
• 11. Adhiya J, Barghi B, Azadeh-Fard N. Predicting the risk of hospital readmissions using a machine learning approach: a case study on patients undergoing skin procedures. Frontiers in Artificial Intelligence. 2024;6:1213378.
• 12. Johnson AE, Pollard TJ, Shen L, Lehman L-wH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Scientific data. 2016;3(1):1-9.
• 13. Nuthakki S, Neela S, Gichoya JW, Purkayastha S. Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks. arXiv preprint arXiv:191212397. 2019.
• 14. Küçük F, Çelik Ö. Sağlık kurumlarında bilgi sistemleri ve e-hizmetler. : Nobel Yayıncılık.; 2023. 107-30 p.
• 15. Abdelaziz T, Rosa E. Electronic health records with decision support systems for sharper diagnoses: bibliometric analysis. International Journal of Advances in Applied Sciences. 2024;13.
• 16. Işık O, Akbolat M. Bilgi teknolojileri ve hastane bilgi sistemleri kullanımı: Sağlık çalışanları üzerine bir araştırma. Bilgi Dünyası. 2010;11(2):365-89.
• 17. Arnott D, Pervan G, Dodson G. Decision Support Systems Research 1990 to 2003: A Descriptive Analysis. 2004.
• 18. Cresswell K, Rigby M, Magrabi F, Scott P, Brender J, Craven CK, et al. The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision. Health policy. 2023;136:104889.
• 19. Scott PJ, Brown AW, Adedeji T, Wyatt JC, Georgiou A, Eisenstein EL, Friedman CP. A review of measurement practice in studies of clinical decision support systems 1998–2017. Journal of the American Medical Informatics Association. 2019;26(10):1120-8.
• 20. Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one. 2019;14(2):e0212356.
• 21. Montani S, Striani M. Artificial intelligence in clinical decision support: a focused literature survey. Yearbook of medical informatics. 2019;28(01):120-7.
• 22. Osheroff JA, Teich J, Levick D, Saldana L, Velasco F, Sittig D, et al. Improving outcomes with clinical decision support: an implementer's guide: Himss Publishing; 2012.
• 23. Kim JT. Application of machine and deep learning algorithms in intelligent clinical decision support systems in healthcare. J Health Med Inform. 2018;9:321.
• 24. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education. 2023;23(1):689.
• 25. McCorduck P, Cfe C. Machines who think: A personal inquiry into the history and prospects of artificial intelligence: AK Peters/CRC Press; 2004.
• 26. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017;2(4).
• 27. Lau AY, Staccini P. Artificial intelligence in health: new opportunities, challenges, and practical implications. Yearbook of medical informatics. 2019;28(01):174-8.
• 28. Shaban-Nejad A, Michalowski M, Buckeridge DL. Health intelligence: how artificial intelligence transforms population and personalized health. NPJ digital medicine. 2018;1(1):53.
• 29. Ventola CL. Social media and health care professionals: benefits, risks, and best practices. Pharmacy and therapeutics. 2014;39(7):491.
• 30. Najjar R. Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics. 2023;13(17):2760.
• 31. Eisemann N, Bunk S, Mukama T, Baltus H, Elsner SA, Gomille T, et al. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. Nature Medicine. 2025:1-8.
• 32. Olawade DB, David-Olawade AC, Wada OZ, Asaolu AJ, Adereni T, Ling J. Artificial intelligence in healthcare delivery: Prospects and pitfalls. Journal of Medicine, Surgery, and Public Health. 2024:100108.
• 33. Dicuonzo G, Donofrio F, Fusco A, Shini M. Healthcare system: Moving forward with artificial intelligence. Technovation. 2023;120:102510.
• 34. Hayat Y, Tariq M, Hussain A, Tariq A, Rasool S. A Review of Biosensors and Artificial Intelligence in Healthcare and Their Clinical Significance. International Research Journal of Economics and Management Studies IRJEMS. 2024;3(1).
• 35. Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A review of the role of artificial intelligence in healthcare. Journal of personalized medicine. 2023;13(6):951.
• 36. Helaly HA, Badawy M, Haikal AY. A review of deep learning approaches in clinical and healthcare systems based on medical image analysis. Multimedia Tools and Applications. 2024;83(12):36039-80.
• 37. Panch T, Szolovits P, Atun R. Artificial intelligence, machine learning and health systems. Journal of global health. 2018;8(2).
• 38. Makond B, Wang K-J, Wang K-M. Benchmarking prognosis methods for survivability–A case study for patients with contingent primary cancers. Computers in Biology and Medicine. 2021;138:104888.
• 39. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-30.
• 40. Elhanashi A, Dini P, Saponara S, Zheng Q. TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices. Journal of Real-Time Image Processing. 2024;21(4):121.
• 41. Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning: MIT press Cambridge; 2016.
• 42. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436-44.
• 43. Nielsen MA. Neural networks and deep learning: Determination press San Francisco, CA, USA; 2015.
• 44. Schmidhuber J. Deep learning in neural networks: An overview. Neural networks. 2015;61:85-117.
• 45. Russell SJ, Norvig P. Artificial intelligence: a modern approach: pearson; 2016.
• 46. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. nature. 2017;542(7639):115-8.
• 47. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Medical image analysis. 2017;42:60-88.
• 48. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics. 2018;19(6):1236-46.
• 49. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ digital medicine. 2018;1(1):18.
• 50. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019;25(1):44-56.
• 51. Solares JRA, Raimondi FED, Zhu Y, Rahimian F, Canoy D, Tran J, et al. Deep learning for electronic health records: A comparative review of multiple deep neural architectures. Journal of biomedical informatics. 2020;101:103337.
• 52. Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing artificial intelligence for clinical decision-making. Frontiers in digital health. 2021;3:645232.
• 53. Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC medical informatics and decision making. 2021;21:1-23.
• 54. Chen M, Wang Y, Wang Q, Shi J, Wang H, Ye Z, et al. Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation. NPJ Digital Medicine. 2024;7(1):349.
• 55. Khalifa M, Albadawy M. AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update. 2024:100146.
• 56. Sarvamangala D, Kulkarni RV. Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence. 2022;15(1):1-22.
• 57. Upreti M, Pandey C, Bist AS, Rawat B, Hardini M. Convolutional neural networks in medical image understanding. Aptisi Transactions on Technopreneurship. 2021;3(2):120-6.
• 58. Wang Y, Gao R, Wei T, Johnston L, Yuan X, Zhang Y, et al. Predicting long-term progression of Alzheimer’s disease using a multimodal deep learning model incorporating interaction effects. Journal of Translational Medicine. 2024;22(1):265.
• 59. Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, et al. Deep learning workflow in radiology: a primer. Insights into imaging. 2020;11:1-15.
• 60. Grout R, Gupta R, Bryant R, Elmahgoub MA, Li Y, Irfanullah K, et al. Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach. Frontiers in Artificial Intelligence. 2024;6:1287541.
• 61. Wang J, Zhu H, Wang S-H, Zhang Y-D. A review of deep learning on medical image analysis. Mobile Networks and Applications. 2021;26(1):351-80.
• 62. Taherdoost H, Ghofrani A. AI and the Evolution of Personalized Medicine in Pharmacogenomics. Intelligent Pharmacy. 2024.
• 63. Unger M, Kather JN. Deep learning in cancer genomics and histopathology. Genome medicine. 2024;16(1):44.
• 64. Humby C. Data is the New Oil. ANA Senior marketer’s summit. Kellogg School. 2006.
• 65. Kastrup N, Holst-Kristensen AW, Valentin JB. Landscape and challenges in economic evaluations of artificial intelligence in healthcare: A systematic review of methodology. BMC Digital Health. 2024;2(1):1-12.
• 66. Elhaddad M, Hamam S. AI-driven clinical decision support systems: an ongoing pursuit of potential. Cureus. 2024;16(4).
• 67. Ghebrehiwet I, Zaki N, Damseh R, Mohamad MS. Revolutionizing personalized medicine with generative AI: a systematic review. Artificial Intelligence Review. 2024;57(5):128.
• 68. Raza MM, Venkatesh KP, Kvedar JC. Generative AI and large language models in health care: pathways to implementation. npj Digital Medicine. 2024;7(1):62.
• 69. Wornow M, Xu Y, Thapa R, Patel B, Steinberg E, Fleming S, et al. The shaky foundations of large language models and foundation models for electronic health records. npj digital medicine. 2023;6(1):135.
• 70. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future healthcare journal. 2019;6(2):94-8.
• 71. Faiyazuddin M, Rahman SJQ, Anand G, Siddiqui RK, Mehta R, Khatib MN, et al. The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency. Health Science Reports. 2025;8(1):e70312.
• 72. Ngiam KY, Khor W. Big data and machine learning algorithms for health-care delivery. The Lancet Oncology. 2019;20(5):e262-e73.
• 73. Wainberg M, Merico D, Delong A, Frey BJ. Deep learning in biomedicine. Nature biotechnology. 2018;36(9):829-38.
• 74. Amal S, Safarnejad L, Omiye JA, Ghanzouri I, Cabot JH, Ross EG. Use of multi-modal data and machine learning to improve cardiovascular disease care. Frontiers in cardiovascular medicine. 2022;9:840262.
• 75. Chen M, Hao Y, Hwang K, Wang L, Wang L. Disease prediction by machine learning over big data from healthcare communities. Ieee Access. 2017;5:8869-79. 76. Morley J, Murphy L, Mishra A, Joshi I, Karpathakis K. Governing data and artificial intelligence for health care: developing an international understanding. JMIR formative research. 2022;6(1):e31623.
• 77. Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, Sharp RR. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ digital medicine. 2021;4(1):140.
• 78. Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A systematic review of the barriers to the implementation of artificial intelligence in healthcare. Cureus. 2023;15(10).