Medical Artificial Intelligence and the Need for Comprehensive Policymaking

Authors

DOI:

https://doi.org/10.69760/gsrh.010120250018

Keywords:

Medical Artificial Intelligence, Policymaking, Machine Learning, Intelligent Robots, Image Processing, Expert Systems

Abstract

Since the concept of artificial intelligence was proposed in 1956, it has led to numerous technological innovations in medicine and has completely changed the traditional medical practice. The present study mainly describes the application of artificial intelligence in various medical fields from four aspects: machine learning, intelligent robots, image processing, and expert systems. It also discusses the current challenges and future trends in these fields.

In line with the development of globalization, various research institutions around the world have conducted research in the field of application of artificial intelligence in medicine. As a result, medical artificial intelligence has achieved significant progress and its future prospects have revealed its increasing development and the need for comprehensive policymaking in this field.

Author Biographies

  • Soroush Rahmaniboukani, Secretary General at International Science and Technology University, Warsaw, Poland

    [1] Soroush Rahmaniboukani, Secretary General at International Science and Technology University, soroush.rahmani@istu.edu.pl, https://orcid.org/0009-0004-6795-5028

  • Mohammad Qurban Hakimi, Istanbul Kent University, Istanbul, Türkiye

    [2] Mohammad Qurban Hakımı ,Master's degree student in health management, İSTANBUL KENT University, İstanbul, Türkiye, Mail: masihyk2018@gmail.com, https://orcid.org/0009-0005-6121-5069

  • Mohammad Ekram Yawar, Faculty of Law, International Science and Technology University, Warsaw, Poland

    [3]  Asst. Prof. Dr. Mohammad Ekram Yawar, Dean of the Faculty of Law, International Science and Technology University, Warsaw, Poland, ekram.yawar@istu.edu.pl, https://orcid.org/0000-0003-3198-5212

References

Abernethy, A. P., Etheredge, L. M., Ganz, P. A., Wallace, P., German, R. R., Neti, C., . . . Murphy, S. B. (2010). Rapid-learning system for cancer care. Journal of Clinical Oncology, 28, 4268. doi:https://dx.doi.org/10.1200%2FJCO.2010.28.5478

Arrieta, A. B., Dı́az-Rodrı́guez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., . . . others. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. doi:https://doi.org/10.1016/j.inffus.2019.12.012

Bargar, W. L., Bauer, A., & Börner, M. (1998). Primary and Revision Total Hip Replacement Using the Robodoc (R) System. Clinical Orthopaedics and Related Research (1976-2007), 354, 82–91.

Beasley, R. A. (2012). Medical robots: current systems and research directions. Journal of Robotics, 2012. doi:https://doi.org/10.1155/2012/401613

Chunxiang, L., Qiang, L., Sanzhong, L., Yuyuan, W., Zhi, W., Jia, C., & Xuhua, Z. (2018). Comparison of blood loss between orthopedic surgical robot and artificial nail in the treatment of femoral neck fracture. Armed Police Medicine, 29, 677. Retrieved from http://journal08.magtechjournal.com/Jwk_wjyx/CN/abstract/article_16393.shtml

Ertl, L., & Christ, F. (2007). Significant improvement of the quality of bystander first aid using an expert system with a mobile multimedia device. Resuscitation, 74, 286–295. doi:https://doi.org/10.1016/j.resuscitation.2007.01.006

Fu, Y., Liu, R., Liu, Y., & Lu, J. (2019). Intertrochanteric fracture visualization and analysis using a map projection technique. Medical & biological engineering & computing, 57, 633–642. doi:https://doi.org/10.1007/s11517-018-1905-1

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51, 1–42. doi:https://doi.org/10.1145/3236009

Guo, S., & Guo, Z. (2018). Research progress of orthopedic surgery robot. Medical Journal of the Chinese People’s Armed Police Force, 29, 69–72.

Hamdi, A., Aboeleneen, A., & Shaban, K. (2021). MARL: Multimodal Attentional Representation Learning for Disease Prediction. arXiv preprint arXiv:2105.00310.

Haskins, G., Kruger, U., & Yan, P. (2020). Deep learning in medical image registration: a survey. Machine Vision and Applications, 31, 1–18. doi:https://doi.org/10.1007/s00138-020-01060-x

Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE transactions on pattern analysis and machine intelligence, 20, 832–844. doi:https://doi.org/10.1109/34.709601

Huang, J., Zhang, Z., & Wang, S. (2016). Efficacy of the Da Vinci surgical system in colorectal surgery comparing with traditional laparoscopic surgery or open surgery: A meta-analysis. International Journal of Advanced Robotic Systems, 13, 1729881416664849. doi:https://doi.org/10.1177/1729881416664849

Kandaswamy, A., & Kumar, A. S. (1997). Recent trends in medicalوexpert systems. IETE Technical Review, 14, 205–210. doi:https://doi.org/10.1080/02564602.1997.11416672

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62, 15–25. doi:https://doi.org/10.1016/j.bushor.2018.08.004

Kong, F.-M., Hayman, J. A., Griffith, K. A., Kalemkerian, G. P., Arenberg, D., Lyons, S., . . . others. (2006). Final toxicity results of a radiation-dose escalation study in patients with non–small-cell lung cancer (NSCLC): Predictors for radiation pneumonitis and fibrosis. International Journal of Radiation Oncology* Biology* Physics, 65, 1075–1086. doi:https://doi.org/10.1016/j.ijrobp.2006.01.051

Kumar, G., & Kalra, R. (2016). A survey on machine learning techniques in health care industry. International Journal of Recent Research Aspects, 3, 128–132.

Liu, F., Zhang, J. R., & Yang, H. (2018). Research progress of medical image recognition based on deep learning. J. Chinese Journal of Biomedical Engineering, 37, 86–94.

Liu, Z., Wang, S., Yang, R., & Ou, X. (2014). A case-control study of risk factors for severe hand-foot-mouth disease in Yuxi, China, 2010–2012. Virologica Sinica, 29, 123–125. doi:https://doi.org/10.1007/s12250-014-3384-3

Maizels, M., & Wolfe, W. J. (2008). An expert system for headache diagnosis: the Computerized Headache Assessment tool (CHAT). Headache: The Journal of Head and Face Pain, 48, 72–78. doi:https://doi.org/10.1111/j.1526-4610.2007.00918.x

Masood, A., Sheng, B., Li, P., Hou, X., Wei, X., Qin, J., & Feng, D. (2018). Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. Journal of biomedical informatics, 79, 117–128. doi:https://doi.org/10.1016/j.jbi.2018.01.005

McGuire, S. (2016). World cancer report 2014. Geneva, Switzerland: World Health Organization, international agency for research on cancer, WHO Press, 2015. Advances in nutrition, 7, 418–419. doi:https://doi.org/10.3945/an.116.012211

Myers, W. (1986). Introduction to expert systems. IEEE Computer Architecture Letters, 1, 100–109. doi:https://doi.ieeecomputersociety.org/10.1109/MEX.1986.5006506

Patel, V. L., Shortliffe, E. H., Stefanelli, M., Szolovits, P., Berthold, M. R., Bellazzi, R., & Abu-Hanna, A. (2009). The coming of age of artificial intelligence in medicine. Artificial intelligence in medicine, 46, 5–17. doi:https://doi.org/10.1016/j.artmed.2008.07.017

Peleg, M., & Combi, C. (2013, February). Artificial intelligence in medicine AIME 2011. Artificial intelligence in medicine, 57, 87-9. doi:10.1016/j.artmed.2013.01.001

Puppe, F. (1997). Introduction to Knowledge Systems. Artificial Intelligence In Medicine, 2, 201–203.

Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3, 210–229. doi:https://doi.org/10.1147/rd.33.0210

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85–117. doi:https://doi.org/10.1016/j.neunet.2014.09.003

Sheikhtaheri, A., Sadoughi, F., & Dehaghi, Z. H. (2014). Developing and using expert systems and neural networks in medicine: a review on benefits and challenges. Journal of medical systems, 38, 1–6. doi:https://doi.org/10.1007/s10916-014-0110-5

Shen, T. L., & Fu, X. L. (2018). Application and prospect of artificial intelligence in cancer diagnosis and treatment. Zhonghua zhong liu za zhi [Chinese journal of oncology], 40, 881–884. doi:https://doi.org/10.3760/cma.j.issn.0253-3766.2018.12.001

Shortliffe, E. H., & Buchanan, B. G. (1975). A model of inexact reasoning in medicine. Mathematical biosciences, 23, 351–379. doi:https://doi.org/10.1016/0025-5564(75)90047-4

Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001).

Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics Association, 8, 527–534. doi:https://doi.org/10.1136/jamia.2001.0080527

Stuart Russell,Peter Norvig .Artificial intelligence: a modern approach .(2002).

Su, M., Miften, M., Whiddon, C., Sun, X., Light, K., & Marks, L. (2005). An artificial neural network for predicting the incidence of radiation pneumonitis. Medical physics, 32, 318–325. doi:https://doi.org/10.1118/1.1835611

Sun, L., Hu, H., Li, M., & others. (2010). A review on continuum robot. Robot, 32, 688–694.

Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q.-H., Ho, M.-T., Vuong, T.-T., . . . others. (2019). Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. Journal of clinical medicine, 8, 360. doi:https://doi.org/10.3390/jcm8030360

Werbos, P. J. (1982). Applications of advances in nonlinear sensitivity analysis. In System modeling and optimization (pp. 762–770). Springer. doi:https://doi.org/10.1007/BFb0006203

Wong, G. L.-H., Ma, A. J., Deng, H., Ching, J. Y.-L., Wong, V. W.-S., Tse, Y.-K., . . . others. (2019). Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding. Alimentary pharmacology & therapeutics, 49, 912–918. doi:https://doi.org/10.1111/apt.15145

Wu, Q., Zhao, Y.-M., Bai, S.-Z., & Li, X. (2019). Application of robotics in stomatology. International journal of computerized dentistry, 22, 251–260.

Wu, X., Tao, Y., Qiu, Q., & Wu, X. (2018). Application of image recognition-based automatic hyphae detection in fungal keratitis. Australasian physical & engineering sciences in medicine, 41, 95–103. doi:https://doi.org/10.1007/s13246-017-0613-8

ZHANG Jiayao, Y. Z. (2019). The development of orthopedics in the era of intelligent medicine. JOURNAL OF CLINICAL SURGERY, 27, 31. doi:10.3969/j.issn.1005-6483.2019.01.008

Zhang, W., Li, H., Cui, L., Li, H., Zhang, X., Fang, S., & Zhang, Q. (2021). Research progress and development trend of surgical robot and surgical instrument arm. The International Journal of Medical Robotics and Computer Assisted Surgery, e2309. doi:https://doi.org/10.1002/rcs.2309

Zhang, Z., Zhou, X., Zhang, X., Wang, L., & Wang, P. (2018). A model based on convolutional neural network for online transaction fraud detection. Security and Communication Networks, 2018. doi:https://doi.org/10.1155/2018/5680264

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Published

2025-03-02

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How to Cite

Rahmaniboukani, S., Qurban Hakimi, M., & Ekram Yawar, M. (2025). Medical Artificial Intelligence and the Need for Comprehensive Policymaking. Global Spectrum of Research and Humanities , 2(2), 60-70. https://doi.org/10.69760/gsrh.010120250018

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