Review
BibTex RIS Cite

Artificial Intelligence in Medicine: Opportunities and Challenges

Year 2024, , 1092 - 1099, 15.09.2024

Tahmineh Darvishmohammadi , Ayşe Özkal , Ahmet Selim Özkal

https://doi.org/10.34248/bsengineering.1499831

Abstract

Currently, artificial intelligence (AI) is used in many fields of medicine such as cardiology, endocrinology, neurology, and particularly gastroenterology where AI increases the quality of images obtained from related imaging techniques. Also, medical diagnosis is greatly affected by AI algorithms and deep learning techniques. AI shows potential for not only monitoring and managing treatment plans but also promises accurate diagnosis and prediction of diseases. This paper aims to review the future opportunities and challenges of AI applications in medicine. The results show a bright future with multiple opportunities in medical diagnosis, radiology, and pathology fields with increasing accuracy, image quality, and decreasing radiation dose. Additionally, AI will facilitate medical research studies which is a great contribution to the medical world. Challenges and ethical limitations will be mostly related to the validity and reliability of data, bias, responsibility issues, risks and unpredictable consequences, and equitable application which need establishing clear guidelines and regulations. This paper suggests a more extended educational program for both healthcare professionals and patients to achieve the best result.

Keywords

Artificial intelligence Medical technologies Medicine Medical diagnosis

References

  • Abajian A, Murali N, Savic LJ, Laage-Gaupp FM, Nezami N, Duncan JS, Schlachter T, Lin M, Geschwind J, Chapiro J. 2018. Predicting treatment response to ıntra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning: an artificial ıntelligence concept. J Vasc Interv Radiol, 29(6): 850-857.
  • Afzal N, Sohn S, Abram S, Scott CG, Chaudhry R, Liu H, Kullo IJ, Arruda‐Olson AM. 2017. Mining peripheral arterial disease cases from narrative clinical notes using natural language processing. J Vasc Surg, 65(6): 1753–1761.
  • Briganti G, Moine OL. 2020. Artificial intelligence in Medicine: Today and tomorrow. Front Med, 7: 27.
  • Campanella G, Hanna MG, Geneslaw Miraflor A, Silva VWK, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. 2019. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med, 25(8): 1301–1309.
  • Canales C, Lee C, Cannesson M. 2020. Science without conscience is but the ruin of the soul: the ethics of big data and artificial intelligence in perioperative medicine. Anesth Analg, 130(5): 1234–1243.
  • Christiansen MP, Garg S K, Brazg R, Bode BW, Bailey TS, Slover RH, Sullivan A, Huang S, Shin J, Lee SW, Kaufman FR. 2017. Accuracy of a Fourth-Generation subcutaneous continuous glucose sensor. Diabetes Technol Ther, 19(8): 446–456.
  • Dorsey ER, Glidden AM, Holloway MR, Birbeck GL, Schwamm L H. 2018. Teleneurology and mobile technologies: the future of neurological care. Nat Rev Neurol, 14(5): 285–297.
  • Filipp FV. 2019. Opportunities for Artificial intelligence in advancing precision Medicine. Current Curr Genet Med Rep, 7: 208–213.
  • Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. 2000. Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc, 7(6): 593–604.
  • Halcox JPJ, Wareham K, Cardew A, Gilmore M, Barry JP, Phillips C, Gravenor MB. 2017. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation. Circ Cardiovasc Interv, 136(19): 1784–1794.
  • Hasani N, Farhadi F, Morris MA, Nikpanah M, Rhamim A, Xu Y, Pariser A, Collins MT, Summers RM, Jones E, Siegel E, Saboury B. 2022. Artificial intelligence in medical imaging and its impact on the rare disease community: threats, challenges and opportunities. PET Clin, 17(1): 13–29.
  • Hoogenboom SA, Bagci U, Wallace MB. 2020. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when? Tech Innov Gastrointest Endosc, 22(2): 42–47.
  • Ichimasa K, Kudo S, Mori Y, Misawa M, Matsudaira S, Kouyama Y, Baba T, Hidaka E, Wakamura K, Hayashi T, Kudo T, Ishigaki T, Yagawa Y, Nakamura H, Takeda K, Haji A, Hamatani, S, Mori K, Ishida F, Miyachi H. 2018. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy, 50(03): 230–240.
  • Kamdar JH, Praba JJ, Georrge JJ. 2020. Artificial intelligence in medical diagnosis: methods, algorithms and applications. Springer, Cham, New Delhi, India, 13, pp: 27–37.
  • Kaul V, Enslin, S, Gross S A. 2020. History of artificial intelligence in medicine. Gastrointest Endosc, 92(4): 807–812.
  • Kim JT. 2018. Application of machine and deep learning algorithms in intelligent clinical decision support systems in healthcare. J Health Med Inform, 9: 5.
  • Kononenko I. 2001. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med, 23(1): 89–109.
  • Kuwaiti AA, Nazer K, Al-Reedy A, Al-Shehri S, Almuhanna A, Subbarayalu AV, Al-Muhanna D, Al-Muhanna F. 2023. A Review of the role of artificial intelligence in healthcare. J Pers Med, 13(6): 951.
  • Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, Prevedello LM, Clark TJ, Geis JR, Itri JN, Hawkins CM. 2018. Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol, 15(2): 350–359.
  • Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello C, and Stephan A. 2023. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med, 6(111).
  • McDougall RJ. 2018. Computer knows best? The need for value-flexibility in medical AI. J Med Ethics, 45, 156 - 160.
  • McKinney SM, Sieniek M, Godbole V, Godwin J, Антропова НВ, Ashrafian H, Back T, Chesus, M, Corrado G, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling‐Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly C, King D, Ledsam J R, Melnick D, Mostofi H, Lily P, Reicher JJ, Romera-Paredes B,Sidebottom R, Suleyman M, Tse D, Young KC, Fauw JD, Shetty S. 2020. International evaluation of an AI system for breast cancer screening. Nature, 577(7788): 89–94.
  • Meskó B, Görög M. 2020. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med, 3: 126.
  • Pesapane F, Codari M, Sardanelli F. 2018. Artificial intelligence in medical imaging: threat or opportunity? Radiologists are again at the forefront of innovation in medicine. Eur Radiol Exp, 2(1): 35.
  • Rajkomar A, Oren E, Chen K, Dai A M, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang D, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte A J, Howell MD, Cui C, Corrado GS, Dean J. 2018. Scalable and accurate deep learning with electronic health records. NPJ Digit Med, 1: 18.
  • Regalia G, Onorati F, Lai M, Caborni C, Picard RW. 2019. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Res, 153: 79–82.
  • Rossi JG, Feldberg B, Krois J, Schwendicke F. 2022. Evaluation of the clinical, technical, and financial aspects of cost-effectiveness analysis of artificial intelligence in medicine: scoping review and framework of analysis. JMIR Med Inform, 10(8): e33703.
  • Ruffle JK, Farmer AD, Aziz Q. 2019. Artificial intelligence-assisted gastroenterology, promises and pitfalls. Am J Gastroenterol, 114(3): 422–428.
  • Sezgin E. 2023. Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers. Digit Health, 2023: 9.
  • Sunarti S, Rahman FF, Naufal M, Risky M, Febriyanto K, Masnina R. 2021. Artificial intelligence in healthcare: opportunities and risk for future. Gac Sanit, 35(1): S67-S70.
  • Sweilam NH, Tharwat AA, Moniem NKA. 2010. Support vector machine for diagnosis cancer disease: A comparative study. Egypt Inform J, 11(2): 81–92.
  • Tursunbayeva A, Renkema M. 2022. Artificial intelligence in health‐care: implications for the job design of healthcare professionals. Asia Pac J Hum Resour, 61(4): 845–887.
  • Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferrán E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S. 2019. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov, 18(6): 463–477.
  • Velagaleti SB, Krishna AV, Lakshmi DS. 2023. Improving performance of clinical and operational workflows in health tech domain using artificial intelligence. IJRASET, 11(6):3929-3932.
  • Yang Y J, Bang C S. 2019. Application of artificial intelligence in gastroenterology. World J Gastroenterol, 25(14): 1666–1683.
  • Zauderer MG, Gucalp A, Epstein AS, Seidman AD, Caroline A, Granovsky S, Fu J, Keesing J, Lewis SM, Co HT, Petri J, Megerian M, Eggebraaten T, Bach PB, Kris MG. 2014. Piloting IBM Watson Oncology within Memorial Sloan Kettering’s regional network. J Clin Oncol, 32: e17653-e17653.
  • Zhang P, Boulos MNK. 2023. Generative AI in medicine and healthcare: promises, opportunities and challenges. Future Internet, 15(9): 286.
  • Zhu M, Xu C, Yu J, Wu Y, Li C, Zhang M, Jin Z, Li, Z. 2013. Differentiation of pancreatic cancer and chronic pancreatitis using Computer-Aided Diagnosis of Endoscopic Ultrasound (EUS) images: a diagnostic test. PLoS One, 8(5): e63820.

Artificial Intelligence in Medicine: Opportunities and Challenges

Year 2024, , 1092 - 1099, 15.09.2024

Tahmineh Darvishmohammadi , Ayşe Özkal , Ahmet Selim Özkal

https://doi.org/10.34248/bsengineering.1499831

Abstract

Currently, artificial intelligence (AI) is used in many fields of medicine such as cardiology, endocrinology, neurology, and particularly gastroenterology where AI increases the quality of images obtained from related imaging techniques. Also, medical diagnosis is greatly affected by AI algorithms and deep learning techniques. AI shows potential for not only monitoring and managing treatment plans but also promises accurate diagnosis and prediction of diseases. This paper aims to review the future opportunities and challenges of AI applications in medicine. The results show a bright future with multiple opportunities in medical diagnosis, radiology, and pathology fields with increasing accuracy, image quality, and decreasing radiation dose. Additionally, AI will facilitate medical research studies which is a great contribution to the medical world. Challenges and ethical limitations will be mostly related to the validity and reliability of data, bias, responsibility issues, risks and unpredictable consequences, and equitable application which need establishing clear guidelines and regulations. This paper suggests a more extended educational program for both healthcare professionals and patients to achieve the best result.

Keywords

Artificial intelligence Medical technologies Medicine Medical diagnosis

References

  • Abajian A, Murali N, Savic LJ, Laage-Gaupp FM, Nezami N, Duncan JS, Schlachter T, Lin M, Geschwind J, Chapiro J. 2018. Predicting treatment response to ıntra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning: an artificial ıntelligence concept. J Vasc Interv Radiol, 29(6): 850-857.
  • Afzal N, Sohn S, Abram S, Scott CG, Chaudhry R, Liu H, Kullo IJ, Arruda‐Olson AM. 2017. Mining peripheral arterial disease cases from narrative clinical notes using natural language processing. J Vasc Surg, 65(6): 1753–1761.
  • Briganti G, Moine OL. 2020. Artificial intelligence in Medicine: Today and tomorrow. Front Med, 7: 27.
  • Campanella G, Hanna MG, Geneslaw Miraflor A, Silva VWK, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. 2019. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med, 25(8): 1301–1309.
  • Canales C, Lee C, Cannesson M. 2020. Science without conscience is but the ruin of the soul: the ethics of big data and artificial intelligence in perioperative medicine. Anesth Analg, 130(5): 1234–1243.
  • Christiansen MP, Garg S K, Brazg R, Bode BW, Bailey TS, Slover RH, Sullivan A, Huang S, Shin J, Lee SW, Kaufman FR. 2017. Accuracy of a Fourth-Generation subcutaneous continuous glucose sensor. Diabetes Technol Ther, 19(8): 446–456.
  • Dorsey ER, Glidden AM, Holloway MR, Birbeck GL, Schwamm L H. 2018. Teleneurology and mobile technologies: the future of neurological care. Nat Rev Neurol, 14(5): 285–297.
  • Filipp FV. 2019. Opportunities for Artificial intelligence in advancing precision Medicine. Current Curr Genet Med Rep, 7: 208–213.
  • Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. 2000. Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc, 7(6): 593–604.
  • Halcox JPJ, Wareham K, Cardew A, Gilmore M, Barry JP, Phillips C, Gravenor MB. 2017. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation. Circ Cardiovasc Interv, 136(19): 1784–1794.
  • Hasani N, Farhadi F, Morris MA, Nikpanah M, Rhamim A, Xu Y, Pariser A, Collins MT, Summers RM, Jones E, Siegel E, Saboury B. 2022. Artificial intelligence in medical imaging and its impact on the rare disease community: threats, challenges and opportunities. PET Clin, 17(1): 13–29.
  • Hoogenboom SA, Bagci U, Wallace MB. 2020. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when? Tech Innov Gastrointest Endosc, 22(2): 42–47.
  • Ichimasa K, Kudo S, Mori Y, Misawa M, Matsudaira S, Kouyama Y, Baba T, Hidaka E, Wakamura K, Hayashi T, Kudo T, Ishigaki T, Yagawa Y, Nakamura H, Takeda K, Haji A, Hamatani, S, Mori K, Ishida F, Miyachi H. 2018. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy, 50(03): 230–240.
  • Kamdar JH, Praba JJ, Georrge JJ. 2020. Artificial intelligence in medical diagnosis: methods, algorithms and applications. Springer, Cham, New Delhi, India, 13, pp: 27–37.
  • Kaul V, Enslin, S, Gross S A. 2020. History of artificial intelligence in medicine. Gastrointest Endosc, 92(4): 807–812.
  • Kim JT. 2018. Application of machine and deep learning algorithms in intelligent clinical decision support systems in healthcare. J Health Med Inform, 9: 5.
  • Kononenko I. 2001. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med, 23(1): 89–109.
  • Kuwaiti AA, Nazer K, Al-Reedy A, Al-Shehri S, Almuhanna A, Subbarayalu AV, Al-Muhanna D, Al-Muhanna F. 2023. A Review of the role of artificial intelligence in healthcare. J Pers Med, 13(6): 951.
  • Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, Prevedello LM, Clark TJ, Geis JR, Itri JN, Hawkins CM. 2018. Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol, 15(2): 350–359.
  • Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello C, and Stephan A. 2023. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med, 6(111).
  • McDougall RJ. 2018. Computer knows best? The need for value-flexibility in medical AI. J Med Ethics, 45, 156 - 160.
  • McKinney SM, Sieniek M, Godbole V, Godwin J, Антропова НВ, Ashrafian H, Back T, Chesus, M, Corrado G, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling‐Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly C, King D, Ledsam J R, Melnick D, Mostofi H, Lily P, Reicher JJ, Romera-Paredes B,Sidebottom R, Suleyman M, Tse D, Young KC, Fauw JD, Shetty S. 2020. International evaluation of an AI system for breast cancer screening. Nature, 577(7788): 89–94.
  • Meskó B, Görög M. 2020. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med, 3: 126.
  • Pesapane F, Codari M, Sardanelli F. 2018. Artificial intelligence in medical imaging: threat or opportunity? Radiologists are again at the forefront of innovation in medicine. Eur Radiol Exp, 2(1): 35.
  • Rajkomar A, Oren E, Chen K, Dai A M, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang D, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte A J, Howell MD, Cui C, Corrado GS, Dean J. 2018. Scalable and accurate deep learning with electronic health records. NPJ Digit Med, 1: 18.
  • Regalia G, Onorati F, Lai M, Caborni C, Picard RW. 2019. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Res, 153: 79–82.
  • Rossi JG, Feldberg B, Krois J, Schwendicke F. 2022. Evaluation of the clinical, technical, and financial aspects of cost-effectiveness analysis of artificial intelligence in medicine: scoping review and framework of analysis. JMIR Med Inform, 10(8): e33703.
  • Ruffle JK, Farmer AD, Aziz Q. 2019. Artificial intelligence-assisted gastroenterology, promises and pitfalls. Am J Gastroenterol, 114(3): 422–428.
  • Sezgin E. 2023. Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers. Digit Health, 2023: 9.
  • Sunarti S, Rahman FF, Naufal M, Risky M, Febriyanto K, Masnina R. 2021. Artificial intelligence in healthcare: opportunities and risk for future. Gac Sanit, 35(1): S67-S70.
  • Sweilam NH, Tharwat AA, Moniem NKA. 2010. Support vector machine for diagnosis cancer disease: A comparative study. Egypt Inform J, 11(2): 81–92.
  • Tursunbayeva A, Renkema M. 2022. Artificial intelligence in health‐care: implications for the job design of healthcare professionals. Asia Pac J Hum Resour, 61(4): 845–887.
  • Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferrán E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S. 2019. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov, 18(6): 463–477.
  • Velagaleti SB, Krishna AV, Lakshmi DS. 2023. Improving performance of clinical and operational workflows in health tech domain using artificial intelligence. IJRASET, 11(6):3929-3932.
  • Yang Y J, Bang C S. 2019. Application of artificial intelligence in gastroenterology. World J Gastroenterol, 25(14): 1666–1683.
  • Zauderer MG, Gucalp A, Epstein AS, Seidman AD, Caroline A, Granovsky S, Fu J, Keesing J, Lewis SM, Co HT, Petri J, Megerian M, Eggebraaten T, Bach PB, Kris MG. 2014. Piloting IBM Watson Oncology within Memorial Sloan Kettering’s regional network. J Clin Oncol, 32: e17653-e17653.
  • Zhang P, Boulos MNK. 2023. Generative AI in medicine and healthcare: promises, opportunities and challenges. Future Internet, 15(9): 286.
  • Zhu M, Xu C, Yu J, Wu Y, Li C, Zhang M, Jin Z, Li, Z. 2013. Differentiation of pancreatic cancer and chronic pancreatitis using Computer-Aided Diagnosis of Endoscopic Ultrasound (EUS) images: a diagnostic test. PLoS One, 8(5): e63820.
There are 38 citations in total.

Details

Primary Language English
Subjects Information Systems (Other), Biomedical Sciences and Technology, Biomedical Imaging, Biomedical Diagnosis
Journal Section Reviews
Authors

Tahmineh Darvishmohammadi 0009-0000-7590-1004

Ayşe Özkal 0000-0003-1294-7106

Ahmet Selim Özkal 0009-0008-9667-7126

Early Pub Date September 13, 2024
Publication Date September 15, 2024
Submission Date June 12, 2024
Acceptance Date September 4, 2024
Published in Issue Year 2024

Cite

APA Darvishmohammadi, T., Özkal, A., & Özkal, A. S. (2024). Artificial Intelligence in Medicine: Opportunities and Challenges. Black Sea Journal of Engineering and Science, 7(5), 1092-1099. https://doi.org/10.34248/bsengineering.1499831
AMA Darvishmohammadi T, Özkal A, Özkal AS. Artificial Intelligence in Medicine: Opportunities and Challenges. BSJ Eng. Sci. September 2024;7(5):1092-1099. doi:10.34248/bsengineering.1499831
Chicago Darvishmohammadi, Tahmineh, Ayşe Özkal, and Ahmet Selim Özkal. “Artificial Intelligence in Medicine: Opportunities and Challenges”. Black Sea Journal of Engineering and Science 7, no. 5 (September 2024): 1092-99. https://doi.org/10.34248/bsengineering.1499831.
EndNote Darvishmohammadi T, Özkal A, Özkal AS (September 1, 2024) Artificial Intelligence in Medicine: Opportunities and Challenges. Black Sea Journal of Engineering and Science 7 5 1092–1099.
IEEE T. Darvishmohammadi, A. Özkal, and A. S. Özkal, “Artificial Intelligence in Medicine: Opportunities and Challenges”, BSJ Eng. Sci., vol. 7, no. 5, pp. 1092–1099, 2024, doi: 10.34248/bsengineering.1499831.
ISNAD Darvishmohammadi, Tahmineh et al. “Artificial Intelligence in Medicine: Opportunities and Challenges”. Black Sea Journal of Engineering and Science 7/5 (September 2024), 1092-1099. https://doi.org/10.34248/bsengineering.1499831.
JAMA Darvishmohammadi T, Özkal A, Özkal AS. Artificial Intelligence in Medicine: Opportunities and Challenges. BSJ Eng. Sci. 2024;7:1092–1099.
MLA Darvishmohammadi, Tahmineh et al. “Artificial Intelligence in Medicine: Opportunities and Challenges”. Black Sea Journal of Engineering and Science, vol. 7, no. 5, 2024, pp. 1092-9, doi:10.34248/bsengineering.1499831.
Vancouver Darvishmohammadi T, Özkal A, Özkal AS. Artificial Intelligence in Medicine: Opportunities and Challenges. BSJ Eng. Sci. 2024;7(5):1092-9.

                                                24890