ARTIFICIAL INTELLIGENCE IN GASTROINTESTINAL SURGERY

Artificial_Intelligence_in_Gastrointestinalkapak

ARTIFICIAL INTELLIGENCE (AI) IN MEDICINE

Melih Can Gül

Afyonkarahisar State Hospital, Department of Gastroenterology Surgery, Afyonkarahisar, Türkiye

Gül MC. Artificial Intelligence (AI) in Medicine. Çaycı HM, ed. Artificial Intelligence (AI) in Gastrointestinal Surgery. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.3-10.

ABSTRACT

The advent of artificial intelligence (AI) in the field of medicine has precipitated a fundamental transformation of the healthcare landscape, proffering unparalleled capabilities in the analysis of intricate datasets, the identification of complex patterns, and the enhancement of clinical decision-making processes. The applications of AI are manifold, encompassing a wide range of disciplines, including diagnostic imaging, clinical decision support systems (CDSS), personalised medicine, robotic surgery, and predictive analytics. The transformative potential of AI is most pronounced in its capacity to enhance diagnostic accuracy, optimise treatment pathways, and facilitate real-time patient monitoring. These innovations are not merely incremental improvements but rather represent a paradigm shift in the manner in which healthcare is both delivered and experienced. In the field of diagnostic imaging, AI-powered algorithms have been shown to detect anomalies with a level of precision that often surpasses that of human experts. In the domain of personalised medicine, AI enables the development of tailored treatment plans by integrating genetic and phenotypic data. In the field of robotic surgery, AI provides real-time guidance, enhancing procedural precision and reducing the occurrence of errors. Predictive analytics, supported by wearable devices, has the potential to transform preventive care by identifying risks before they manifest as clinical symptoms. Conversely, CDSS platforms facilitate clinician decision-making by synthesising extensive patient data into actionable insights, enhancing efficiency and patient outcomes. Notwithstanding the potential of AI, concerns regarding ethics and practicality persist. Issues such as data privacy, algorithmic bias, and the opaque nature of AI decision-making necessitate rigorous scrutiny. The absence of transparency in many AI systems gives rise to questions of accountability and trust, while biases in training datasets can perpetuate healthcare disparities. It is imperative that these concerns are addressed to ensure the equitable adoption of AI technologies. The future of AI in medicine lies in its integration with emerging technologies such as telemedicine, natural language processing (NLP), and genomics. These advancements hold the potential to enhance the precision, accessibility, and inclusivity of healthcare services. By promoting interdisciplinary collaboration and adhering to ethical principles, AI has the capacity to fulfil its role as a transformative agent in modern medicine.

Keywords: Artificial intelligence; Clinical decision support systems; Diagnostic imaging; Ethical dilemmas; Medicine; Precision medicine

Referanslar

  1. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth Summer Research Project on Artificial Intelligence, 1955. AI Magazine. 2006;27(4):12-14. [Crossref]
  2. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. [Crossref]  [PubMed]
  3. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. [Crossref]  [PubMed]  [PMC]
  4. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. [Crossref]  [PubMed]  [PMC]
  5. Obermeyer Z, Emanuel EJ. Predicting the future-big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-1219. [Crossref]  [PubMed]  [PMC]
  6. Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37-43. [Crossref]  [PubMed]  [PMC]
  7. Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Standalone artificial intelligence for breast cancer detection in mammography. J Natl Cancer Inst. 2019;111(9):916-922. [Crossref]  [PubMed]  [PMC]
  8. Cheung R, Chun J, Sheidow T, Motolko M, Malvankar-Mehta MS. Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis. Eye (Lond). 2022;36(5):994-1004. [Crossref]  [PubMed]  [PMC]
  9. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410. [Crossref]  [PubMed]
  10. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70-76. [Crossref]  [PubMed]  [PMC]
  11. Beam AL, Kohane IS. Artificial intelligence in medicine. N Engl J Med. 2023;388(13):1220-1221. [Crossref]  [PubMed]
  12. Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301-1309. [Crossref]  [PubMed]  [PMC]
  13. Lou N, Cui X, Lin X, Gao R, Xu C, Qiao N, et al. Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data. Translational lung cancer research. 2024;13(4):706-720. [Crossref]  [PubMed]  [PMC]
  14. Char DS, Shah NH, Magnus D. Implementing machine learning in health care-addressing ethical challenges. N Engl J Med. 2018;378(11):981-983. [Crossref]  [PubMed]  [PMC]
  15. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol.2018;71(23):2668-2679. [Crossref]  [PubMed]
  16. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism.2017;69:36-40. [Crossref]  [PubMed]
  17. Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170387. [Crossref]  [PubMed]  [PMC]
  18. Rajpurkar P, Irvin J, Zhu K, et al. CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning.arXiv preprint arXiv:1711.05225.2017. [Link]
  19. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. [Crossref]  [PubMed]
  20. Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-175. [Crossref]  [PubMed]  [PMC]
  21. Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and generative artificial intelligence for medical education: Potential impact and opportunity. Acad Med. 2024;99(1):22-27. [Crossref]  [PubMed]
  22. Lee SI, Celik S, Logsdon BA, et al. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat Commun. 2018;9(1):42. [Crossref]  [PubMed]  [PMC]
  23. Sun T, Wong AK, Kamel MS. Classification of imbalanced data: A review. Int J Pattern Recognit Artif Intell. 2009;23(4):687-719. [Crossref]
  24. Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017 Jan;24(1):198-208. [Crossref]  [PubMed]  [PMC]
  25. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-1358. [Crossref]  [PubMed]
  26. Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and generative artificial intelligence for medical education: Potential impact and opportunity. Acad Med. 2024;99(1):22-27. [Crossref]  [PubMed]
  27. Regulation (EU) 2016/679 of the European Parliament and of the Council (General Data Protection Regulation). [Link]
  28. Puthal D, Wu X, Surya N, Ranjan R, Chen J.A Selective Encryption Method to Ensure Confidentiality for Big Sensing Data Streams, IEEE Transactions on Big Data. 2019;5(3):379-392. [Crossref]
  29. Abbas Z, Ahmad SF, Syed MH, Anjum A, Rehman S. Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture. IEEE Access. 2024;12:10548-10574 [Crossref]
  30. Sasseville M, Ouellet S, Rhéaume C, Sahlia M, Couture V, Després P. et al. Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review. J Med Internet Res. 2025;27:60269. [Crossref]  [PubMed]  [PMC]
  31. Cary MP, Bessias S, McCall J, Pencina MJ, Grady SD, Lytle K, et al. Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare. Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing. 2025;57(1): 130-139. [Crossref]  [PubMed]  [PMC]
  32. Alzahrani, Ahmad A, Fahd S. Alharithi. Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing. Alexandria Engineering Journal. 2024;106: 582-593. [Crossref]
  33. Shah M, Sureja N. A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions. Arch Computat Methods Eng. 2025;32:255-267. [Crossref]
  34. Rabonato RT, Berton L. A systematic review of fairness in machine learning. AI Ethics. 2024:1-12. [Crossref]
  35. Timmons AC, Duong JB, Simo FN, Lee T, Vo HPQ, Ahle MW, et al. A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health. Perspectives on psychological science : a journal of the Association for Psychological Science. 2023;18(5):1062-1096. [Crossref]  [PubMed]  [PMC]
  36. Zermatten V, Lu X, Castillo NJ, Kellenberger T, Tuia D. Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024;17:6468-6477 [Crossref]
  37. McPherson CJ, Addington-Hall JM. Judging the quality of care at the end of life: can proxies provide reliable information?. Soc Sci Med. 2003;56(1):95-109. [Crossref]  [PubMed]
  38. Quinn TP, Jacobs S, Senadeera M, Le V, Coghlan S. The three ghosts of medical AI: Can the black-box present deliver?. Artif Intell Med. 2022;124:102158. [Crossref]  [PubMed]
  39. Kiseleva A, Kotzinos D, De Hert P. Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations. Front Artif Intell. 2022;5:879603. Published 2022 May 30. [Crossref]  [PubMed]  [PMC]
  40. Akhtar MAK, Kumar M, Nayyar A. Transparency and Accountability in Explainable AI: Best Practices. In: Towards Ethical and Socially Responsible Explainable AI. Studies in Systems, Decision 2024;551:127-164. [Crossref]
  41. Manickam P, Mariappan SA, Murugesan SM, Hansda S, Kaushik A, Shinde R, Thipperudraswamy SP. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors. 2022;12(8):562. [Crossref]  [PubMed]  [PMC]
  42. Feng T. Applications of Artificial Intelligence to Diagnosis of Neurodegenerative Diseases. Stud Health Technol Inform. 2023;308:648-655. [Crossref]
  43. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol. 2022;39(8):120. Published 2022 Jun 15. [Crossref]  [PubMed]  [PMC]
  44. Patel AU, Gu Q, Esper R, Maeser D, Maeser N. The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare. BioMedInformatics. 2024;4(2):1363-1383. [Crossref]
  45. Lescrauwaet L, Wagner H.; Yoon C, Shukla S. Adaptive Legal Frameworks and Economic Dynamics in Emerging Tech-Nologies: Navigating the Intersection for Responsible Innovation. LawEco. 2022;16:202-220. [Crossref]