Cognitive Computing in Respiratory Health: Revolutionizing Medicine Through Artificial Intelligence(AI) And Machine Learning (ML)
Abstract
Human errors in medical practice can lead to misdiagnosis, resulting in inappropriate treatment and serious risks to patient health. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as a valuable tool for reducing such errors, particularly in medical diagnostics. These technologies can rapidly and accurately analyze large volumes of data, providing additional insights that help healthcare professionals to make more accurate decisions. AI excels in providing solid evidence to guide clinical decisions, reducing reliance on subjective judgments. It can analyse complex datasets and identify patterns that has chances to be overlooked by human eyes, thus leading to improved diagnostic accuracy and treatment plans. ML, a component of AI, uses adaptive models that learn from extensive datasets, though these models must be trained on high-quality data to avoid perpetuating errors or biases. In pulmonary medicine, AI and ML have shown considerable potential in diagnosing and treating conditions such as asthma, chronic obstructive pulmonary disease (COPD), and pulmonary fibrosis. These technologies help determine disease staging, forecast exacerbations, and estimate survival rates. By harnessing AI and ML, clinicians can make more precise diagnoses, customize treatments to individual needs, and detect early signs of worsening conditions, ultimately enhancing patient outcomes. Moreover, AI and ML can minimize patient risks by providing a broader and more in-depth analysis of medical data. This review explores how these technologies can process large datasets to deliver insights that surpass human capability, fostering error-free diagnosis and treatment.
Keywords:
Artificial intelligences, AI in Pulmonary Function Tests, COPD Treatment, Machine learning, Respiratory diseases
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References
Bhaskaran, S., & Marappan, R. (2023). Enhanced personalized recommendation system for machine learning public datasets: generalized modeling, simulation, significant results and analysis. International Journal of Information Technology, 15(3), 1583–1595. https://doi.org/10.1007/s41870-023-01165-2
Chen, S., Kuhn, M., Prettner, K., Yu, F., Yang, T., Bärnighausen, T., Bloom, D. E., & Wang, C. (2023). The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020-50: a health-augmented macroeconomic modelling study. The Lancet. Global Health, 11(8), e1183–e1193. https://doi.org/10.1016/S2214-109X(23)00217-6
Das, N., Happaerts, S., Gyselinck, I., Staes, M., Derom, E., Brusselle, G., Burgos, F., Contoli, M., Dinh-Xuan, A. T., Franssen, F. M. E., Gonem, S., Greening, N., Haenebalcke, C., Man, W. D.-C., Moisés, J., Peché, R., Poberezhets, V., Quint, J. K., Steiner, M. C., … Janssens, W. (2023). Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation. The European Respiratory Journal, 61(5), 2201720. https://doi.org/10.1183/13993003.01720-2022
Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural Language Processing. In Imaging Informatics for Healthcare Professionals (pp. 87–99). Springer International Publishing. https://doi.org/10.1007/978-3-031-25928-9_5
Fernandez-Granero, M. A., Sanchez-Morillo, D., Lopez-Gordo, M. A., & Leon, A. (2015). A Machine Learning Approach to Prediction of Exacerbations of Chronic Obstructive Pulmonary Disease. In Artificial Computation in Biology and Medicine (pp. 305–311). Springer International Publishing. https://doi.org/10.1007/978-3-319-18914-7_32
Gonzalez, G., Ash, S. Y., Vegas-Sánchez-Ferrero, G., Onieva Onieva, J., Rahaghi, F. N., Ross, J. C., Díaz, A., San José Estépar, R., Washko, G. R., & Investigators, Copdg. and E. (2018). Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography. American Journal of Respiratory and Critical Care Medicine, 197(2), 193–203. https://doi.org/10.1164/rccm.201705-0860OC
Hardinge, M., Rutter, H., Velardo, C., Shah, S. A., Williams, V., Tarassenko, L., & Farmer, A. (2015). Using a mobile health application to support self-management in chronic obstructive pulmonary disease: a six-month cohort study. BMC Medical Informatics and Decision Making, 15, 46. https://doi.org/10.1186/s12911-015-0171-5
Horst, C., Gholipour, B., Nair, A., & Jacob, J. (2019). Differential diagnoses of fibrosing lung diseases. BJR Open, 1(1), 20190009. https://doi.org/10.1259/bjro.20190009
Khemasuwan, D., Sorensen, J. S., & Colt, H. G. (2020). Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. European Respiratory Review : An Official Journal of the European Respiratory Society, 29(157), 200181. https://doi.org/10.1183/16000617.0181-2020
Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks. IEEE Access, 6, 32328–32338. https://doi.org/10.1109/access.2018.2837692
Kumar, K., Kumar, P., Deb, D., Unguresan, M.-L., & Muresan, V. (2023). Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. Healthcare (Basel, Switzerland), 11(2), 207. https://doi.org/10.3390/healthcare11020207
Liu, N., Xie, F., Siddiqui, F. J., Ho, A. F. W., Chakraborty, B., Nadarajan, G. D., Tan, K. B. K., & Ong, M. E. H. (2022). Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation. JMIR Research Protocols, 11(3), e34201–e34201. https://doi.org/10.2196/34201
Liu, Y., Tang, J., & Sun, Y. (2023). Impact of Interstitial Lung Abnormalities on Disease Expression and Outcomes in COPD or Emphysema: A Systematic Review. International Journal of Chronic Obstructive Pulmonary Disease, 18, 189–206. https://doi.org/10.2147/COPD.S392349
McLean, S., Nurmatov, U., Liu, J. L., Pagliari, C., Car, J., & Sheikh, A. (2011). Telehealthcare for chronic obstructive pulmonary disease. The Cochrane Database of Systematic Reviews, 2011(7), CD007718–CD007718. https://doi.org/10.1002/14651858.CD007718.pub2
Mekov, E., Miravitlles, M., & Petkov, R. (2020). Artificial intelligence and machine learning in respiratory medicine. Expert Review of Respiratory Medicine, 14(6), 559–564. https://doi.org/10.1080/17476348.2020.1743181
Mhlanga, D. (2023). Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review. Energies, 16(2), 745. https://doi.org/10.3390/en16020745
Nakamura, H., Hirai, T., Kurosawa, H., Hamada, K., Matsunaga, K., Shimizu, K., Konno, S., Muro, S., Fukunaga, K., Nakano, Y., Kuwahira, I., & Hanaoka, M. (2024). Current advances in pulmonary functional imaging. Respiratory Investigation, 62(1), 49–65. https://doi.org/10.1016/j.resinv.2023.09.004
Ohno, Y., Ozawa, Y., Nagata, H., Bando, S., Cong, S., Takahashi, T., Oshima, Y., Hamabuchi, N., Matsuyama, T., Ueda, T., Yoshikawa, T., Takenaka, D., & Toyama, H. (2023). Area-Detector Computed Tomography for Pulmonary Functional Imaging. Diagnostics (Basel, Switzerland), 13(15), 2518. https://doi.org/10.3390/diagnostics13152518
Omer, N., Samak, A. H., Taloba, A. I., & Abd El-Aziz, R. M. (2023). A novel optimized probabilistic neural network approach for intrusion detection and categorization. Alexandria Engineering Journal, 72, 351–361. https://doi.org/10.1016/j.aej.2023.03.093
Paladugu, P. S., Ong, J., Nelson, N., Kamran, S. A., Waisberg, E., Zaman, N., Kumar, R., Dias, R. D., Lee, A. G., & Tavakkoli, A. (2023). Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence. Annals of Biomedical Engineering, 51(10), 2130–2142. https://doi.org/10.1007/s10439-023-03304-z
Nwadiugwu, M. C. (2020). Neural networks, artificial intelligence and the computational brain. arXiv preprint arXiv:2101.08635. https://doi.org/10.48550/arXiv.2101.08635
Pv, V., Jeena, S., Sambathkumar, R., & Venkateswaramurthy, N. (2019). Assessment And Educational Interventions In Inhalation Technique Among Asthma And Copd Patients At A Tertiary Care Hospital In Erode (Tamilnadu- India). International Journal of Life Science and Pharma Research. https://doi.org/10.22376/ijpbs/lpr.2019.9.3.p61-69
Qiblawey, Y., Tahir, A., Chowdhury, M. E. H., Khandakar, A., Kiranyaz, S., Rahman, T., Ibtehaz, N., Mahmud, S., Maadeed, S. Al, Musharavati, F., & Ayari, M. A. (2021). Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning. Diagnostics (Basel, Switzerland), 11(5), 893. https://doi.org/10.3390/diagnostics11050893
Rogueda, P., Grinovero, M., Ponti, L., Purkins, G., Croad, O. (2019). Telehealth ready: performance of the Amiko Respiro sense connected technology with Merxin DPIs. Journal Aerosol Med Pulm Drug Delivery,32(2), A26-A26. http://doi.org/10.1089/jamp.2019.ab01.abstracts
Romana, A., Kamath, L., Sarda, A., Muraraiah, S., & Cr, J. (2012). Polypharmacy leading to adverse drug reactions in elderly in a tertiary care hospital. mortality, 6(7).
Sayed, M., Riaño, D., & Villar, J. (2021). Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning. Journal of Clinical Medicine, 10(17), 3824. https://doi.org/10.3390/jcm10173824
Sethu, M., Kotla, B., Russell, D., Madadi, M., Titu, N. A., Coble, J. B., Boring, R. L., Blache, K., Agarwal, V., Yadav, V., & Khojandi, A. (2022). Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review. Nuclear Technology, 209(3), 276–294. https://doi.org/10.1080/00295450.2022.2067461
Somenath Ghosh, D., Sanjeev Kumar Jha, D., Elamin Bushara, M. O., Asit Kumar, D., & Ghate, V. U. (2023). Climate Changes and Cancer. International Journal of Trends in OncoScience. https://doi.org/10.22376/ijtos.2023.1.4.23-31
Stamer, T., Steinhäuser, J., & Flägel, K. (2023). Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review. Journal of Medical Internet Research, 25, e43311–e43311. https://doi.org/10.2196/43311
Topalovic, M., Das, N., Burgel, P.-R., Daenen, M., Derom, E., Haenebalcke, C., Janssen, R., Kerstjens, H. A. M., Liistro, G., Louis, R., Ninane, V., Pison, C., Schlesser, M., Vercauter, P., Vogelmeier, C. F., Wouters, E., Wynants, J., & Janssens, W. (2019). Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. European Respiratory Journal, 53(4), 1801660. https://doi.org/10.1183/13993003.01660-2018
Walsh, S. L. F., Humphries, S. M., Wells, A. U., & Brown, K. K. (2020). Imaging research in fibrotic lung disease; applying deep learning to unsolved problems. The Lancet Respiratory Medicine, 8(11), 1144–1153. https://doi.org/10.1016/s2213-2600(20)30003-5
Walshe, K., & Rundall, T. G. (2001). Evidence-based management: from theory to practice in health care. The Milbank Quarterly, 79(3), 429–V. https://doi.org/10.1111/1468-0009.00214
Zhang, Z. (2020). Big data analysis with artificial intelligence technology based on machine learning algorithm. Journal of Intelligent & Fuzzy Systems, 39(5), 6733–6740. https://doi.org/10.3233/jifs-191265
Chen, S., Kuhn, M., Prettner, K., Yu, F., Yang, T., Bärnighausen, T., Bloom, D. E., & Wang, C. (2023). The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020-50: a health-augmented macroeconomic modelling study. The Lancet. Global Health, 11(8), e1183–e1193. https://doi.org/10.1016/S2214-109X(23)00217-6
Das, N., Happaerts, S., Gyselinck, I., Staes, M., Derom, E., Brusselle, G., Burgos, F., Contoli, M., Dinh-Xuan, A. T., Franssen, F. M. E., Gonem, S., Greening, N., Haenebalcke, C., Man, W. D.-C., Moisés, J., Peché, R., Poberezhets, V., Quint, J. K., Steiner, M. C., … Janssens, W. (2023). Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation. The European Respiratory Journal, 61(5), 2201720. https://doi.org/10.1183/13993003.01720-2022
Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural Language Processing. In Imaging Informatics for Healthcare Professionals (pp. 87–99). Springer International Publishing. https://doi.org/10.1007/978-3-031-25928-9_5
Fernandez-Granero, M. A., Sanchez-Morillo, D., Lopez-Gordo, M. A., & Leon, A. (2015). A Machine Learning Approach to Prediction of Exacerbations of Chronic Obstructive Pulmonary Disease. In Artificial Computation in Biology and Medicine (pp. 305–311). Springer International Publishing. https://doi.org/10.1007/978-3-319-18914-7_32
Gonzalez, G., Ash, S. Y., Vegas-Sánchez-Ferrero, G., Onieva Onieva, J., Rahaghi, F. N., Ross, J. C., Díaz, A., San José Estépar, R., Washko, G. R., & Investigators, Copdg. and E. (2018). Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography. American Journal of Respiratory and Critical Care Medicine, 197(2), 193–203. https://doi.org/10.1164/rccm.201705-0860OC
Hardinge, M., Rutter, H., Velardo, C., Shah, S. A., Williams, V., Tarassenko, L., & Farmer, A. (2015). Using a mobile health application to support self-management in chronic obstructive pulmonary disease: a six-month cohort study. BMC Medical Informatics and Decision Making, 15, 46. https://doi.org/10.1186/s12911-015-0171-5
Horst, C., Gholipour, B., Nair, A., & Jacob, J. (2019). Differential diagnoses of fibrosing lung diseases. BJR Open, 1(1), 20190009. https://doi.org/10.1259/bjro.20190009
Khemasuwan, D., Sorensen, J. S., & Colt, H. G. (2020). Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. European Respiratory Review : An Official Journal of the European Respiratory Society, 29(157), 200181. https://doi.org/10.1183/16000617.0181-2020
Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks. IEEE Access, 6, 32328–32338. https://doi.org/10.1109/access.2018.2837692
Kumar, K., Kumar, P., Deb, D., Unguresan, M.-L., & Muresan, V. (2023). Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. Healthcare (Basel, Switzerland), 11(2), 207. https://doi.org/10.3390/healthcare11020207
Liu, N., Xie, F., Siddiqui, F. J., Ho, A. F. W., Chakraborty, B., Nadarajan, G. D., Tan, K. B. K., & Ong, M. E. H. (2022). Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation. JMIR Research Protocols, 11(3), e34201–e34201. https://doi.org/10.2196/34201
Liu, Y., Tang, J., & Sun, Y. (2023). Impact of Interstitial Lung Abnormalities on Disease Expression and Outcomes in COPD or Emphysema: A Systematic Review. International Journal of Chronic Obstructive Pulmonary Disease, 18, 189–206. https://doi.org/10.2147/COPD.S392349
McLean, S., Nurmatov, U., Liu, J. L., Pagliari, C., Car, J., & Sheikh, A. (2011). Telehealthcare for chronic obstructive pulmonary disease. The Cochrane Database of Systematic Reviews, 2011(7), CD007718–CD007718. https://doi.org/10.1002/14651858.CD007718.pub2
Mekov, E., Miravitlles, M., & Petkov, R. (2020). Artificial intelligence and machine learning in respiratory medicine. Expert Review of Respiratory Medicine, 14(6), 559–564. https://doi.org/10.1080/17476348.2020.1743181
Mhlanga, D. (2023). Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review. Energies, 16(2), 745. https://doi.org/10.3390/en16020745
Nakamura, H., Hirai, T., Kurosawa, H., Hamada, K., Matsunaga, K., Shimizu, K., Konno, S., Muro, S., Fukunaga, K., Nakano, Y., Kuwahira, I., & Hanaoka, M. (2024). Current advances in pulmonary functional imaging. Respiratory Investigation, 62(1), 49–65. https://doi.org/10.1016/j.resinv.2023.09.004
Ohno, Y., Ozawa, Y., Nagata, H., Bando, S., Cong, S., Takahashi, T., Oshima, Y., Hamabuchi, N., Matsuyama, T., Ueda, T., Yoshikawa, T., Takenaka, D., & Toyama, H. (2023). Area-Detector Computed Tomography for Pulmonary Functional Imaging. Diagnostics (Basel, Switzerland), 13(15), 2518. https://doi.org/10.3390/diagnostics13152518
Omer, N., Samak, A. H., Taloba, A. I., & Abd El-Aziz, R. M. (2023). A novel optimized probabilistic neural network approach for intrusion detection and categorization. Alexandria Engineering Journal, 72, 351–361. https://doi.org/10.1016/j.aej.2023.03.093
Paladugu, P. S., Ong, J., Nelson, N., Kamran, S. A., Waisberg, E., Zaman, N., Kumar, R., Dias, R. D., Lee, A. G., & Tavakkoli, A. (2023). Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence. Annals of Biomedical Engineering, 51(10), 2130–2142. https://doi.org/10.1007/s10439-023-03304-z
Nwadiugwu, M. C. (2020). Neural networks, artificial intelligence and the computational brain. arXiv preprint arXiv:2101.08635. https://doi.org/10.48550/arXiv.2101.08635
Pv, V., Jeena, S., Sambathkumar, R., & Venkateswaramurthy, N. (2019). Assessment And Educational Interventions In Inhalation Technique Among Asthma And Copd Patients At A Tertiary Care Hospital In Erode (Tamilnadu- India). International Journal of Life Science and Pharma Research. https://doi.org/10.22376/ijpbs/lpr.2019.9.3.p61-69
Qiblawey, Y., Tahir, A., Chowdhury, M. E. H., Khandakar, A., Kiranyaz, S., Rahman, T., Ibtehaz, N., Mahmud, S., Maadeed, S. Al, Musharavati, F., & Ayari, M. A. (2021). Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning. Diagnostics (Basel, Switzerland), 11(5), 893. https://doi.org/10.3390/diagnostics11050893
Rogueda, P., Grinovero, M., Ponti, L., Purkins, G., Croad, O. (2019). Telehealth ready: performance of the Amiko Respiro sense connected technology with Merxin DPIs. Journal Aerosol Med Pulm Drug Delivery,32(2), A26-A26. http://doi.org/10.1089/jamp.2019.ab01.abstracts
Romana, A., Kamath, L., Sarda, A., Muraraiah, S., & Cr, J. (2012). Polypharmacy leading to adverse drug reactions in elderly in a tertiary care hospital. mortality, 6(7).
Sayed, M., Riaño, D., & Villar, J. (2021). Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning. Journal of Clinical Medicine, 10(17), 3824. https://doi.org/10.3390/jcm10173824
Sethu, M., Kotla, B., Russell, D., Madadi, M., Titu, N. A., Coble, J. B., Boring, R. L., Blache, K., Agarwal, V., Yadav, V., & Khojandi, A. (2022). Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review. Nuclear Technology, 209(3), 276–294. https://doi.org/10.1080/00295450.2022.2067461
Somenath Ghosh, D., Sanjeev Kumar Jha, D., Elamin Bushara, M. O., Asit Kumar, D., & Ghate, V. U. (2023). Climate Changes and Cancer. International Journal of Trends in OncoScience. https://doi.org/10.22376/ijtos.2023.1.4.23-31
Stamer, T., Steinhäuser, J., & Flägel, K. (2023). Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review. Journal of Medical Internet Research, 25, e43311–e43311. https://doi.org/10.2196/43311
Topalovic, M., Das, N., Burgel, P.-R., Daenen, M., Derom, E., Haenebalcke, C., Janssen, R., Kerstjens, H. A. M., Liistro, G., Louis, R., Ninane, V., Pison, C., Schlesser, M., Vercauter, P., Vogelmeier, C. F., Wouters, E., Wynants, J., & Janssens, W. (2019). Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. European Respiratory Journal, 53(4), 1801660. https://doi.org/10.1183/13993003.01660-2018
Walsh, S. L. F., Humphries, S. M., Wells, A. U., & Brown, K. K. (2020). Imaging research in fibrotic lung disease; applying deep learning to unsolved problems. The Lancet Respiratory Medicine, 8(11), 1144–1153. https://doi.org/10.1016/s2213-2600(20)30003-5
Walshe, K., & Rundall, T. G. (2001). Evidence-based management: from theory to practice in health care. The Milbank Quarterly, 79(3), 429–V. https://doi.org/10.1111/1468-0009.00214
Zhang, Z. (2020). Big data analysis with artificial intelligence technology based on machine learning algorithm. Journal of Intelligent & Fuzzy Systems, 39(5), 6733–6740. https://doi.org/10.3233/jifs-191265
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Agilandeswari, V., Basheer, M., Bagchi, S., Menon, G., & Prasanna, P. (2024). Cognitive Computing in Respiratory Health: Revolutionizing Medicine Through Artificial Intelligence(AI) And Machine Learning (ML). International Journal of Advancement in Life Sciences Research, 7(3), 55-64. https://doi.org/https://doi.org/10.31632/ijalsr.2024.v07i03.005
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