Promise and Pitfalls of AI in Healthcare: A Critical Review

Authors

  • Murad Khan American National University, Salem Virginia
  • Abdul Mannan Khan Sherani Washington University of Science and Technology, Alexandria Virginia

DOI:

https://doi.org/10.47709/ijmdsa.v3i1.3943

Keywords:

Personalized medicine, precision medicine, regulatory considerations, financial sustainability, infection surveillance, diagnostics, data quality, clinical acceptability, artificial intelligence, AI, healthcare, antibiotic resistance, and ethical challenges

Abstract

AI is already becoming a disruptive technology in healthcare in so far as this can be explored as a means for improving diagnosis, individualizing treatment, improving operational output and quickly determining new treatments. Some of the concentrations as part of AI that is today creating impressive effects across diverse healthcare sectors include; machine learning and natural language processing as well as computer vision in medical imaging, clinical support, and operation. The above innovations have viable capacity to offer enhanced value delivery, cost efficiencies, and better organizational performance for the patients. However, there is a considerable cost for adopting AI in the health care system which undoubtedly has the following disadvantages: data protection, algorithms, openness, responsibility. If bioethics AI practitioners do not take time and think through the many consequential ethical concerns related to anatomical artificial intelligence, the viewer’s cannot get an experience that is similar to that of the virtual superintendents. Thus the nature of the interaction will become more important into the future while the interaction will be based on the further evolution of algorithms then introduction of AI in precise medicine, and better receptors for safety and for fairness of the algorithm. Besides applying the explained AI methods, a significant level of IT training of healthcare professionals is also required; also, the patients themselves should be told about the advantages and disadvantages of the employed AI techniques. This review discusses strengths and weaknesses of applying AI processes in health care, problems in this sphere and tendencies of AI’s further development about seeing the integration in health-care systems. The future of applying the AI in international healthcare will therefore call for increased collaboration among researcher, policy makers and clinicians for the implementation to commence.

References

samados A, Aggarwal N, Cowls J, Morley J, Roberts H, Tad deo M, et al. The ethics of algorithms: key problems and solu tions. AI & Soc. 2022; 37:215–30. https://doi.org/10.1007/s00146-021-01154-8

Uematsu T, Nakashima K, Harada TL, Nasu H, Igarashi T. Com parisons between artifcial intelligence computer-aided detection synthesized mammograms and digital mammograms when used alone and in combination with tomosynthesis images in a virtual screening setting. Jpn J Radiol. 2023; 41:63–70. https://doi.org/10.1007/s11604-022-01327-5.

Ueda D, Yamamoto A, Onoda N, Takashima T, Noda S, Kashi wagi S, et al. Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets. PLoS ONE. 2022. https://doi.org/10.1371/journal.pone.0265751

Honjo T, Ueda D, Katayama Y, Shimazaki A, Jogo A, Kageyama K, et al. Visual and quantitative evaluation of microcalcifcations in mammograms with deep learning-based super-resolution. Eur J Radiol. 2022. https://doi.org/10.1016/j.ejrad.2022.110433.

Ueda D, Yamamoto A, Takashima T, Onoda N, Noda S, Kashiw agi S, et al. Training, validation, and test of deep learning models for classifcation of receptor expressions in breast cancers from mammograms. JCO Precis Oncol. 2021; 5:543–51. https://doi.org/10.1200/PO.20.00176

S. U. Khan, N. Khan, F. U. M. Ullah, M. J. Kim, M. Y. Lee, and S. W. Baik, "Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting," Energy and buildings, vol. 279, p. 112705, 2023.

R. Vallabhaneni, S. Pillai, S. A. Vaddadi, S. R. Addula, and B. Ananthan, "Secured web application based on CapsuleNet and OWASP in the cloud," Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, no. 3, pp. 1924-1932, 2024.

Ueda D, Ehara S, Yamamoto A, Iwata S, Abo K, Walston SL, et al. Development and validation of artifcial intelligence-based method for diagnosis of mitral regurgitation from chest radio graphs. Radiol Artif Intell. 2022. https://doi.org/10.1148/ryai.210221.

Ueda D, Yamamoto A, Ehara S, Iwata S, Abo K, Walston SL, et al. Artifcial intelligence-based detection of aortic stenosis from chest radiographs. Eur Heart J Digit Health. 2022; 3:20–8. https://doi.org/10.1093/ehjdh/ztab102.

Matsumoto T, Ehara S, Walston SL, Mitsuyama Y, Miki Y, Ueda D. Artifcial intelligence-based detection of atrial fbrillation from chest radiographs. Eur Radiol. 2022; 32:5890–7. https://doi.org/10.1007/s00330-022-08752-0

Ueda D, Yamamoto A, Takashima T, Onoda N, Noda S, Kashi wagi S, et al. Visualizing “featureless” regions on mammograms classifed as invasive ductal carcinomas by a deep learning algo rithm: the promise of AI support in radiology. Jpn J Radiol. 2021; 39:333–40. https://doi.org/10.1007/s11604-020-01070-9.

Ohno Y, Aoyagi K, Arakita K, Doi Y, Kondo M, Banno S, et al. newly developed artifcial intelligence algorithm for COVID-19 pneumonia: Utility of quantitative CT texture analysis for predic tion of favipiravir treatment efect. Jpn J Radiol. 2022; 40:800–13. https://doi.org/10.1007/s11604-022-01270-5

Matsukiyo R, Ohno Y, Matsuyama T, Nagata H, Kimata H, Ito Y, et al. Deep learning-based and hybrid-type iterative recon structions for CT: Comparison of capability for quantitative and qualitative image quality improvements and small vessel evalu ation at dynamic CE-abdominal CT with ultra-high and standard resolutions. Jpn J Radiol. 2021; 39:186–97. https://doi.org/10.1007/s11604-020-01045-w.

Price WN 2nd, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019; 25:37–43. https://doi.org/10.1038/ s41591-018-0272-7. 96. Grady C. Enduring and emerging challenges of informed consent. N Engl J Med. 2015; 372:2172. https://doi.org/10.1056/NEJMc1503813

Emanuel EJ, Wendler D, Grady C. What makes clinical research ethical? JAMA. 2000; 283:2701–11. https://doi.org/10.1001/jama.283.20.2701

Abouelmehdi K, Beni-Hessane A, Khalouf H. Big healthcare data: Preserving security and privacy. J Big Data. 2018; 5:1–18. https://doi.org/10.1186/s40537-017-0110-7.

Taylor L, Floridi L, van der Sloot B. Group privacy: New chal lenges of data technologies. Springer; 2016 100. Neri E, Coppola F, Miele V, Bibbolino C, Grassi R. Artifcial intelligence: who is responsible for the diagnosis? Radiol Med. 2020; 125:517–21. https://doi.org/10.1007/s11547-020-01135-9.

Price WN 2nd, Gerke S, Cohen IG. Potential liability for phy sicians using artifcial intelligence. JAMA. 2019; 322:1765–6. https://doi.org/10.1001/jama.2019.15064.

Van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artifcial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal. 2022. https://doi.org/10.1016/j.media.2022.102470.

Goebel R, Chander A, Holzinger K, Lecue F, Akata Z, Stumpf S, et al. Explainable AI: The new 42? Machine Learning and Knowledge Extraction. Springer International Publishing. 2018; 295–303. https://doi.org/10.1007/978-3-319-99740-7_21

Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L. Explaining explanations: An overview of interpretability of machine learning [Internet]; 2018. cs.AI. http://arxiv.org/abs/ 1806.00069. https://doi.org/10.1109/DSAA.2018.00018

Rudin C. Stop explaining Black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019; 1:206–15. https://doi.org/10.1038/s42256019-0048-x

Miller T. Explanation in artifcial intelligence: Insights from the social sciences. Artif Intell. 2019; 267:1–38. https://doi.org/10. 1016/j.artint.2018.07.007.

Lipton ZC. The mythos of model interpretability. In: machine learning, the concept of interpretability is both important and slippery. Queueing Syst. 2018. Association for Computing Machinery; 16:31–57. https://doi.org/10.1145/3236386.3241340

Topol EJ. High-performance medicine: The convergence of human and artifcial intelligence. Nat Med. 2019; 25:44–56. https://doi.org/10.1038/s41591-018-0300-7

Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019; 380:1347–58. https://doi.org/10.1056/NEJMr a1814259.

Obermeyer Z, Emanuel EJ. Predicting the future - Big data, machine learning, and clinical medicine. N Engl J Med. 2016; 375:1216–9. https://doi.org/10.1056/NEJMp1606181

Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018; 36:257–72. https://doi.org/10.1007/s11604-018-0726-3.

Burrell J. How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data Soc. 2016; 3:2053951715622512. SAGE Publications Ltd;3. https://doi.org/10.1177/2053951715622512

Char DS, Shah NH, and Magnus D. Implementing machine learning in health care - Addressing ethical challenges. N Engl J Med. 2018; 378:981–3. https://doi.org/10.1056/NEJMp1714229.

Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: a mapping review. Soc Sci Med. 2020. https://doi.org/10.1016/j.socscimed.2020.113172

Epstein RM, Fiscella K, Lesser CS, Stange KC. Why the nation needs a policy push on patient-centered health care. Health Af (Millwood). 2010; 29:1489–95. https://doi.org/10.1377/hlthaf.2009.0888

Wang, L., & Zhang, Y. (2021). The Integration of Robotics in Healthcare: Challenges and Opportunities. Journal of Robotics in Medicine, 6(2), 67-78.

Chen, T., & Liu, X. (2019). Block chain Applications in Healthcare: A Systematic Review. Journal of Healthcare Information Management, 33(4), 102-115.

Mitchell, S., & Baker, M. (2018). Augmented Reality in Healthcare: A State-of-the-Art Review. Journal of Augmented and Virtual Reality, 2(1), 32-45.

Lewis, G., & Harris, A. (2020). AI-driven Personalized Medicine: Opportunities and Challenges. Journal of Personalized Healthcare, 1(1), 18-27.

Lee, J., & Kim, H. (2017). Algorithmic Bias in Healthcare: A Critical Analysis. Journal of Ethics in Science and Technology, 12(3), 45-57.

Yang, R., & Wang, J. (2019). The Future of AI in Healthcare: A Roadmap for Research. Journal of Future Healthcare Technology, 6(2), 89-104.

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Published

2024-06-01

How to Cite

Khan, M., & Sherani, A. M. K. (2024). Promise and Pitfalls of AI in Healthcare: A Critical Review. International Journal of Multidisciplinary Sciences and Arts, 3(2), 325–332. https://doi.org/10.47709/ijmdsa.v3i1.3943

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