Artificial Intelligence in Smart Healthcare Applications: Benefits, Challenges, and Ethical Considerations
DOI:
https://doi.org/10.63682/jns.v14i32S.8889Keywords:
Artificial Intelligence, Smart Healthcare, Multiple Disease Identification, AI Dietitian, Ethical Dilemmas, Usability Examination, Machine Learning and precision medicineAbstract
Artificial Intelligence (AI) can change healthcare by transferring clever applications to diagnose better, helping with personalized healthcare, and streamlining health outcomes. The presented research develops and tests an AI-based healthcare application that will have two core features: A multi-disease detecting program on the critical organs (diabetes, kidney, heart, liver, breast cancer, malaria, pneumonia, and lungs) and an AI-based dietitian and food recommending system, which will provide some customized nutritional advice. The study will evaluate the validity, reliability, and usefulness of such systems, as well as the ethical and social implications of the same through a mixed-methods design. Multi-disease detection model has accuracy of 92.5 percent with organ specific accuracy spread between 71.17 percent (liver) to 99.17 percent (kidney). The AI dietitian app was found to match the expert recommendations with 98.9 accuracy and delivered health outcomes with 78 percent of the patients. In spite of these improvements, obstacles that include information privacy, the ease of algorithms, and the possibility of biases imply the requirement of effective ethical models. This paper highlights the transformative aspect of AI in the healthcare sector but also affirms that innovative use must be ethical to overcome ethical and practical concerns
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Al Kuwaiti, A., et al. (2023). A Review of the Role of Artificial Intelligence in Healthcare. Journal of Personalized Medicine, 13(6), 951. https://doi.org/10.3390/jpm13060951
Ali, O., et al. (2023). A systematic literature review of artificial intelligence in the healthcare sector. Journal of Innovation & Knowledge, 8(1), 100333. https://doi.org/10.1016/j.jik.2023.100333
Aminizadeh, S., et al. (2024). Opportunities and challenges of artificial intelligence in healthcare. Artificial Intelligence in Medicine, 102779. https://doi.org/10.1016/j.artmed.2024.102779
Aarthy Chellasamy, & Aishwarya Nagarathinam. (2022). An Overview of Augmenting AI Application in Healthcare. Lecture Notes on Data Engineering and Communications Technologies, 397–407. https://doi.org/10.1007/978-981-19-0898-9_31
Askin, S., et al. (2023). Artificial Intelligence Applied to clinical trials. Artificial Intelligence Applied to Clinical Trials: Opportunities and Challenges, 13(2). https://doi.org/10.1007/s12553-023-00738-2
Alowais, S. A., et al. (2023). Revolutionizing healthcare. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04698-z
Chibugo, F., et al. (2024). THE ROLE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE. International Medical Science Research Journal, 4(4), 500–508. https://doi.org/10.51594/imsrj.v4i4.1052
El Kafhali, S., & Lazaar, M. (2021). Artificial Intelligence for Healthcare. Advances in Intelligent Systems and Computing, 141–156. https://doi.org/10.1007/978-3-030-72588-4_10
Gala, D., et al. (2024). The Role of Artificial Intelligence in Improving Patient Outcomes. Healthcare, 12(4), 481. https://doi.org/10.3390/healthcare12040481
Gómez-González, E., & Gómez Gutiérrez, E. (2020). Artificial Intelligence in Medicine and Healthcare. Publications Office of the European Union. https://idus.us.es/handle/11441/105744
Ioannis Vourganas et al. (2022). Accountable, Responsible, Transparent Artificial Intelligence in Ambient Intelligence Systems for Healthcare. 87–111. https://doi.org/10.1007/978-981-16-8150-9_5
Jan, Z., et al. (2023). Artificial intelligence for industry 4.0. Expert Systems with Applications, 216, 119456. https://doi.org/10.1016/j.eswa.2022.119456
Kumar, S., & T.V. Vijay Kumar. (2024). Trends, challenges and opportunities of artificial intelligence in healthcare. International Journal of Electronic Healthcare, 14(1), 27–53. https://doi.org/10.1504/ijeh.2024.140082
M. Kavitha et al. (2022). Systematic View and Impact of Artificial Intelligence in Smart Healthcare Systems. CRC Press EBooks, 25–56. https://doi.org/10.1201/9781003265436-2
Muhammad Mohsin Qureshi, et al. (2023). Advances in Laser Speckle Imaging. Journal of Biophotonics. https://doi.org/10.1002/jbio.202300126
Pagallo, U., & Durante, M. (2022). The Good, the Bad, and the Invisible. J, 5(1), 139–149. https://doi.org/10.3390/j5010011
Pierre-Olivier Côté, et al. (2024). Data cleaning and machine learning. Automated Software Engineering, 31(2). https://doi.org/10.1007/s10515-024-00453-w
Richardson, J. P., et al. (2022). A framework for examining patient attitudes regarding applications of artificial intelligence in healthcare. DIGITAL HEALTH, 8, 205520762210890. https://doi.org/10.1177/20552076221089084
Shalom Akhai. (2024). The Impact of Artificial Intelligence on Healthcare. Journal of Advanced Research in Medical Science & Technology, 11(1&2), 1–6. http://www.medicaljournalshouse.com/index.php/Journal-MedicalSci-MedTechnology/article/view/921
Udegbe, F. C., et al. (2024). AI’S IMPACT ON PERSONALIZED MEDICINE. Engineering Science & Technology Journal, 5(4), 1386–1394. https://doi.org/10.51594/estj.v5i4.1040
Ueda, D., et al. (2023). Fairness of Artificial Intelligence in healthcare. Japanese Journal of Radiology, 42(1). https://doi.org/10.1007/s11604-023-01474-3
Usability Testing. (2022). Google Books. https://books.google.com/books?hl=en&lr=&id=fYlyEAAAQBAJ&oi=fnd&pg=PR1&dq=Usability+Testing&ots=ngqZhLZMD6&sig=68lNK7XoLjTwSfcEWu3aBVGU-4Q
Varnosfaderani, S. M., & Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics. Bioengineering, 11(4), 337. https://doi.org/10.3390/bioengineering11040337
Sanaullah, S., Dayan, I., Hashmi, A. A. Q., Anjum, N., Ejaz, M., Zhaira, I., & Zhaira, D. (2025). Deciphering the relationship between Janus kinase-2 (Jak-2) mutation and thrombocytopenia. Biological and Clinical Sciences Research Journal, 6(1), 97–100. https://doi.org/10.54112/bcsrj.v6i1.1526
Safdar, A., Zhaira, D., Kousar, S., & Javaid, S. (2022). Investigating the presence of fetal trisomy 13, 18, and 21 in Pakistani patients and its computational analysis. International Journal of Endorsing Health Science Research (IJEHSR), 10(1), 78-85. https://doi.org/10.29052/IJEHSR.v10.i1.2022.78-85
Zaidi, D. Z., Shafiq, A., Ishaq, M., Khan, A. U., Zhaira, I., Sadiq, N., Ishtiaq, R. H., Malik, N. U., Ahmed, M., & Zaidi, D. Z. (2024). Hyperprolactinemia: A potential factor in infertility—Investigating the relationship and implications. Journal of Population Therapeutics & Clinical Pharmacology, 31(3). https://doi.org/10.53555/jptcp.v31i3.5265
Zhaira, D., Shafiq, A., Ishaq, M., Khan, A. U., Zhaira, I., Sadiq, N., Ishtiaq, R. H., Malik, N. U., Ahmed, M., & Zaidi, Z. D. (2024). Hyperprolactinemia: A potential factor in infertility—Investigating the relationship and implications. Journal of Population Therapeutics and Clinical Pharmacology. Advance online publication. https://doi.org/10.53555/jptcp.v31i3.5260
Hussain, A., Ishtiaq, R. H., Malik, N. U., Ahmed, B., Naqvi, S., Aslam, Z., Ramzan, A., & Zhaira, D. (2024). Comparison of HbA1c levels in diabetic patients taking oral drugs and insulin. Journal of Population Therapeutics and Clinical Pharmacology, 31(3). https://doi.org/10.53555/jptcp.v31i3.5255
Zhaira, D., Mubarik, H., Anwar, W. K., Zhaira, I., Khalid, M., Muzzamal, H., Hassan, M. S., Abbas, F., & Sadiq, N. (2024). Deciphering the dengue enigma: Unraveling clinical, hematological, and serological signatures in Rawalpindi, Pakistan. Journal of Pakistan Therapeutic and Clinical Pharmacology, 31(3). https://doi.org/10.53555/jptcp.v31i3.5107
Husnain, A., Mehmood, K., Javed, R., Zhaira, I., Zaidi, D. Z., & Ramzan, A. (2024). Association of HCV viral load with hematological and biochemical parameters in hepatitis C patients, Pakistan. Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban) / Journal of Tianjin University Science and Technology, 57(4), 90. https://doi.org/10.5281/zenodo.10947248
Kousar, S., Nafisa, A., Zhaira, D., Zhaira, I., Khalid, M., Khalid, M., Munir, I., Waheed, K., Ramzan, A., Khan, A., & Ahmed, D. (2024). A study on serum prolactin level and its relationship with thyroid stimulating hormone in infertile women in Pakistan. Kurdish Studies, 12(2). https://doi.org/10.53555/ks.v12i2.2879
Zhaira, I., Hamza, D., Kausar, S., Malik, Z., Rashid, S., Saad, N., & Zhaira, D. (2024). A comparative study on biochemical profile among pre and post-sofosbuvir-treated in HCV patients, Pakistan. Journal of Tianjin University Science and Technology, 57(5), 422. https://doi.org/10.5281/zenodo.11344706
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