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Year 2025, Volume: 11 Issue: 2, 143 - 150, 31.05.2025
https://doi.org/10.19127/mbsjohs.1527654

Abstract

References

  • Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019 Sep 1;111(9):916–22.
  • Al-shamasneh ARM, Binti Obaidellah UH. Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study. ESJ. 2017 Jan 31;13(3):342.
  • Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal of Cancer. 2019 May;113:47–54.
  • Haenssle HA, Fink C, Toberer F, Winkler J, Stolz W, Deinlein T, et al. Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Annals of Oncology. 2020 Jan;31(1):137–43.
  • Bhaskaranand M, Ramachandra C, Bhat S, Cuadros J, Nittala MG, Sadda SR, et al. The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes. Diabetes Technology & Therapeutics. 2019 Nov 1;21(11):635–43.
  • Yang H, Yang M, Chen J, Yao G, Zou Q, Jia L. Multimodal deep learning approaches for precision oncology: a comprehensive review. Brief Bioinform. 2024 Nov 22;26(1):bbae699.
  • Qiao Y, Zhao L, Luo C, Luo Y, Wu Y, Li S, et al. Multi-modality artificial intelligence in digital pathology. Briefings in Bioinformatics. 2022 Nov 19;23(6):bbac367.
  • Hu W, Yii FSL, Chen R, Zhang X, Shang X, Kiburg K, et al. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images. Transl Vis Sci Technol. 2023 Jul 3;12(7):14.
  • Lai LSW, Redington AN, Reinisch AJ, Unterberger MJ, Schriefl AJ. Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study: Computerized Diagnosis of Murmurs. Congenital Heart Disease. 2016 Sep;11(5):386–95.
  • Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial Intelligence in Surgery: A Systematic Review of Use and Validation. J Clin Med. 2024 Nov 24;13(23):7108.
  • Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020 Aug;14(4):369–81.
  • Holmes AA, Konig G, Ting V, Philip B, Puzio T, Satish S, et al. Clinical Evaluation of a Novel System for Monitoring Surgical Hemoglobin Loss. Anesthesia & Analgesia. 2014 Sep;119(3):588–94.
  • Stoker AD, Binder WJ, Frasco PE, Morozowich ST, Bettini LM, Murray AW, et al. Estimating surgical blood loss: A review of current strategies in various clinical settings. SAGE Open Med. 2024;12:20503121241308302.
  • He YS, Su JR, Li Z, Zuo XL, Li YQ. Application of artificial intelligence in gastrointestinal endoscopy. J of Digest Diseases. 2019 Dec;20(12):623–30.
  • 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. Digestive Endoscopy. 2021 May;33(4):598–607.
  • Durak S, Bayram B, Bakırman T, Erkut M, Doğan M, Gürtürk M, et al. Deep neural network approaches for detecting gastric polyps in endoscopic images. Med Biol Eng Comput. 2021 Aug;59(7–8):1563–74.
  • Keleş H. Artificial Intelligence Applications in Medicine. Kırıkkale Üniversitesi Tıp Fakültesi Dergisi. 2022 Dec 31;24(3):604–13.
  • 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.
  • Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan;26(1):80–93.
  • Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother. 2020 Aug;128:110255.

Artificial Intelligence in Health: Transforming Health in the Future

Year 2025, Volume: 11 Issue: 2, 143 - 150, 31.05.2025
https://doi.org/10.19127/mbsjohs.1527654

Abstract

Artificial intelligence (AI) is a rapidly developing technology that has the potential to revolutionise healthcare in recent years. AI is known to have various applications that can be used to improve the diagnosis of diseases, treatments and patient care. In addition, by analysing large data sets that will form the basis of diagnosis and treatment, it is aimed to capture details that may escape the human eye and reveal new information. AI enables physicians to make faster, more accurate diagnoses and select the most suitable treatments for patients. Administrative and clinical operations in the healthcare sector are undergoing a radical change under the influence of the digital transformation process. In this context, innovative steps towards process automation are being implemented at an increasing pace. Artificial intelligence technologies, in particular, have demonstrated remarkable adaptation by being integrated into both administrative and medical applications in the healthcare field. These technologies increase the efficiency of patient management systems along with the optimization of diagnosis and treatment processes, thus providing a significant reduction in both operational and clinical costs. Artificial intelligence-supported solutions reshape the organizational structure of healthcare services, optimize resource use and increase service quality. This transformation has a wide range of effects from patient care to institutional operations and contributes to the sustainability of healthcare systems.

References

  • Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019 Sep 1;111(9):916–22.
  • Al-shamasneh ARM, Binti Obaidellah UH. Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study. ESJ. 2017 Jan 31;13(3):342.
  • Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal of Cancer. 2019 May;113:47–54.
  • Haenssle HA, Fink C, Toberer F, Winkler J, Stolz W, Deinlein T, et al. Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Annals of Oncology. 2020 Jan;31(1):137–43.
  • Bhaskaranand M, Ramachandra C, Bhat S, Cuadros J, Nittala MG, Sadda SR, et al. The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes. Diabetes Technology & Therapeutics. 2019 Nov 1;21(11):635–43.
  • Yang H, Yang M, Chen J, Yao G, Zou Q, Jia L. Multimodal deep learning approaches for precision oncology: a comprehensive review. Brief Bioinform. 2024 Nov 22;26(1):bbae699.
  • Qiao Y, Zhao L, Luo C, Luo Y, Wu Y, Li S, et al. Multi-modality artificial intelligence in digital pathology. Briefings in Bioinformatics. 2022 Nov 19;23(6):bbac367.
  • Hu W, Yii FSL, Chen R, Zhang X, Shang X, Kiburg K, et al. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images. Transl Vis Sci Technol. 2023 Jul 3;12(7):14.
  • Lai LSW, Redington AN, Reinisch AJ, Unterberger MJ, Schriefl AJ. Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study: Computerized Diagnosis of Murmurs. Congenital Heart Disease. 2016 Sep;11(5):386–95.
  • Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial Intelligence in Surgery: A Systematic Review of Use and Validation. J Clin Med. 2024 Nov 24;13(23):7108.
  • Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020 Aug;14(4):369–81.
  • Holmes AA, Konig G, Ting V, Philip B, Puzio T, Satish S, et al. Clinical Evaluation of a Novel System for Monitoring Surgical Hemoglobin Loss. Anesthesia & Analgesia. 2014 Sep;119(3):588–94.
  • Stoker AD, Binder WJ, Frasco PE, Morozowich ST, Bettini LM, Murray AW, et al. Estimating surgical blood loss: A review of current strategies in various clinical settings. SAGE Open Med. 2024;12:20503121241308302.
  • He YS, Su JR, Li Z, Zuo XL, Li YQ. Application of artificial intelligence in gastrointestinal endoscopy. J of Digest Diseases. 2019 Dec;20(12):623–30.
  • 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. Digestive Endoscopy. 2021 May;33(4):598–607.
  • Durak S, Bayram B, Bakırman T, Erkut M, Doğan M, Gürtürk M, et al. Deep neural network approaches for detecting gastric polyps in endoscopic images. Med Biol Eng Comput. 2021 Aug;59(7–8):1563–74.
  • Keleş H. Artificial Intelligence Applications in Medicine. Kırıkkale Üniversitesi Tıp Fakültesi Dergisi. 2022 Dec 31;24(3):604–13.
  • 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.
  • Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan;26(1):80–93.
  • Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother. 2020 Aug;128:110255.
There are 20 citations in total.

Details

Primary Language English
Subjects Clinical Sciences (Other)
Journal Section Review
Authors

Eser Uyanik 0009-0006-5181-9755

Publication Date May 31, 2025
Submission Date August 3, 2024
Acceptance Date January 21, 2025
Published in Issue Year 2025 Volume: 11 Issue: 2

Cite

Vancouver Uyanik E. Artificial Intelligence in Health: Transforming Health in the Future. Mid Blac Sea J Health Sci. 2025;11(2):143-50.

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