Horizon of Medical Education Development

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

Questionnaires of Articles

About Journal

Aims and Scope

Editorial Board

Names of Reviewers

Publication Ethics

Editorial policy

Peer Review Process

Advertising policy

Indexing and Abstracting

FAQ

Corrections, Retractions and Matters Arising

Authors General Guideline

Guide for Reviewers

Guideline of preparing Author Contribition

Audio Abstract Setting Guideline

Forms

The Potential of Artificial Intelligence-Based Technologies in Disease Diagnosis, Treatment, and Medical Care

,

Document Type : Original Article
10.22038/hmed.2024.25092

Abstract

Introduction: In recent years, the increasing availability of big data in the health sector has provided a suitable platform for the development and application of artificial intelligence-based technologies. With the ability to analyze complex data and extract hidden clinical patterns, this technology has great potential to improve the accuracy of medical decisions and improve the quality of healthcare.
Materials & Methods:  The present study was conducted using a descriptive method and based on a systematic review of published scientific sources, the applications of artificial intelligence in various medical fields, including diagnosis, treatment, health systems management, and medical education, were analyzed. The challenges of implementing this technology and its future prospects were also examined.
Results: The results show that AI-based technologies have been remarkably effective in areas such as accurate and rapid disease diagnosis, disease prevention, treatment planning, and robotic surgery, 3D bio printing, mixed reality, hospital management, and controlling pandemics such as COVID-19. These technologies have been able to significantly improve the efficiency, accuracy, and speed of medical processes.
Conclusion:   Despite the broad benefits of AI in healthcare, challenges such as data quality, ethical considerations, legal requirements, and institutional resistance have hindered its widespread implementation. However, adopting a responsible approach, based on multidisciplinary collaboration and clear ethical frameworks, can pave the way for effective and sustainable exploitation of this technology in future health systems.

Keywords

References
  1. Abelson S, Collord G, Ng SWK, Weissbrod O, Mendelson Cohen N, Niemeyer E, Gonen R. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature. 2018; 559(7714):400-404.
  2. Bajorath J, Kearnes S, Walters WP, Meanwell NA, and Georg GI, Wang S. Artificial Intelligence in Drug Discovery: Into the Great Wide Open. J Med Chem. 2020; 63(16):8651-8652.
  3. Awad A, Fina F, Goyanes A, Gaisford S, Basit AW. 3D printing: Principles and pharmaceutical applications of selective laser sintering. Int J Pharm. 2020; 586:119594.
  4. Angehrn Z, Haldna L, Zandvliet AS, Gil Berglund E, Zeeuw J, Amzal B, et al. Artificial Intelligence and Machine Learning Applied at the Point of Care. Front Pharmacol. 2020; 11:759.
  5. Bertin H, Huon JF, Praud M, Fauvel F, Salagnac JM, Perrin JP, et al. Bilateral sagittal split osteotomy training on mandibular 3-dimensional printed models for maxillofacial surgical residents. Br J Oral Maxillofac Surg. 2020; 58(8):953-958.
  6. Bohl MA, McBryan S, Pais D, Chang SW, Turner JD, Nakaji P, et al. The Living Spine Model: A Biomimetic Surgical Training and Education Tool. Oper Neurosurg (Hagerstown). 2020; 19(1):98-106.
  7. Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, et al. AI applications to medical images: From machine learning to deep learning. Phys Med Biol. 2021; 83:9-24.
  8. Corona PS, Vicente M, Tetsworth K, Glatt V. Preliminary results using patient-specific 3d printed models to improve preoperative planning for correction of post-traumatic tibial deformities with circular frames. Injury. 2018; 49 Suppl 2:S51-S59.
  9. Creighton FX, Unberath M, Song T, Zhao Z, Armand M, Carey J. Early Feasibility Studies of Augmented Reality Navigation for Lateral Skull Base Surgery. Otol Neurotol. 2020; 41(7):883-888.
  10. Crossnohere NL, Elsaid M, Paskett J, Bose-Brill S, Bridges JFP. Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks. J Med Internet Res. 2022; 24(8):e36823.
  11. Dekker I, De Jong EM, Schippers MC, De Bruijn-Smolders M, Alexiou A, Giesbers B. Optimizing Students' Mental Health and Academic Performance: AI-Enhanced Life Crafting. Front Psychol. 2020; 11:1063.
  12. Feng ZH, Li XB, Phan K, Hu ZC, Zhang K, Zhao J, et al. Design of a 3D printed magnetic resonance imaging-guided microwave coagulation system for liver tumor therapy. PLoS One. 2018; 13(8):e0201975.
  13. Froiio C, Berlth F, Capovilla G, Tagkalos E, Hadzijusufovic E, Mann C, et al. Robotic-assisted surgery for esophageal submucosal tumors: a singlecenter case series. Updates Surg. 2022; 74(3):1043-1054.
  14. Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. In: Artificial Intelligence in Healthcare. Elsevier; 2020. p. 295-33.
  15. Gong J, Liu JY, Sun XW, Zheng B, Nie SD. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules. Phys Med Biol. 2018; 63(3):35036.
  16. Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B, et al. Artificial intelligence in luminal endoscopy. Ther Adv Gastroenterol. 2020; 13:2631774520935220.
  17. Hashimoto DA, Witkowski E. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020; 132(2):379-394.
  18. Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020; 132(2):379-394.
  19. Hwang Y, Lee HH, Park C, Tama BA, Kim JS, Cheung DY, et al. Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network. Dig Endosc. 2021; 33(4):598-607.
  20. Iqbal JD, Vinay R. Are we ready for Artificial Intelligence in Medicine? Swiss Med Wkly. 2022; 152:w30179.
  21. Keshavarzi Arshadi A, Webb J, Salem M, Cruz E, Calad-Thomson S, Ghadirian N, et al. Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development. Front Artif Intell. 2020; 3:65.
  22. Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019; 475(2):131-138.
  23. Kumar A, Gadag S, Nayak UY. The Beginning of a New Era: Artificial Intelligence in Healthcare. Adv Pharm Bull. 2021; 11(3):414-425.
  24. Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother. 2020; 128:110255.
  25. Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Radiation Oncology: Status, Challenges, and Prospects. Cancer Commun (Lond). 2021; 41(8):712-727.
  26. Liu Y, Li J, Zhang Y, Wang X, Liu S. Artificial intelligence-assisted diagnosis and treatment of COVID-19. Engineering. 2020; 6(11):1222-1229.
  27. Mills B, Heath DJ, Grant-Jacob JA, Eason RW.
    Predictive capabilities for laser machining via a neural network. Optics express. 2018; 26(13): 17245-53.
  28. Navarrete AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020; 14(4):369-381.
  29. Nguyen DT, Pham TD, Batchuluun G, Yoon HS, Park KR. Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains. J Clin Med. 2019; 8(11):1976.
  30. Sakagianni A, Feretzakis G, Kalles D, Koufopoulou C, Kaldis V. Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans. Stud Health Technol Inform. 2020; 272:13-16.
  31. Stoel BC. Artificial intelligence in detecting early RA. Semin Arthritis Rheum. 2019; 49(3S):S25-S28.
  32. Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019; 103(2):167-175.
  33. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020; 14(4):337-339.
  34. Wang X, Wang Y, Hu Z, Zhang X, Zhang Y. Deep learning for identifying COVID-19 using CT images. Radiol Cardiothorac Imaging. 2020; 2(2):e200033.
  35. Yang L, Xing C. Artificial intelligence for drug discovery and development. Curr Opin Chem Biol. 2020; 57:34-40.
  36. Zhang Y. Artificial intelligence in clinical decision support: A systematic review. J Med Internet Res. 2020; 22(4):e16457.
Horizon of Medical Education Development
Volume 16, Special Issue1
August 2025
Pages 6-21
Files
Share
How to cite
Statistics