Risk Management and Application of Artificial Intelligence in Elderly Patients with Chronic Diseases and Sarcopenia

DOI: https://doi.org/10.62517/jmhs.202405313

Author(s)

Qinqin Chen1,#, Yinhang Wu2,#, Chenyu Li3,#, Xuecan Yang4, Laurent Peyrodie5, Jean-Marie Nianga6, Zefeng Wang1,3,4,5,6,*

Affiliation(s)

1ASIR, Institute - Association of Intelligent Systems and Robotics, Paris, France 2Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang, China 3Huzhou University, Huzhou, Zhejiang, China 4IEIP, Institute of Education and Innovation in Paris, Paris, France 5ICL, Junia, Université Catholique de Lille, LITL, F-59000 Lille, France 6Sino-Congolese Foundation for Development, Brazzaville, Republic of the Congo #These Authors Contributed Equally to This Work. *Corresponding Author.

Abstract

This study investigates AI's incorporation in healthcare, focusing on chronic diseases and sarcopenia in the elderly. It explores AI's potential to enhance risk management and patient outcomes, especially for cardiovascular disease, diabetes, and COPD. The research highlights AI-driven tools' efficacy in early diagnosis, personalized treatment, and continuous health monitoring, improving elderly patients' quality of life. Legal and ethical issues are covered, including bias in AI models, patient data privacy, and regulatory compliance. The study emphasizes the need for interdisciplinary collaboration and empirical validation. The future of artificial intelligence in the healthcare field, with wearable technology and personalized treatment plans, is expected to bring revolutionary patient care. The paper also considers economic implications, advocating for regulatory frameworks and equitable access. AI can transform chronic disease and sarcopenia management through better diagnostics and proactive health management, but ongoing research and collaboration are essential to tackle ethical, legal, and practical challenges, aiming for an efficient, effective, and equitable healthcare system.

Keywords

Artificial Intelligence; Chronic Diseases; Sarcopenia; Healthcare Risk Management; Personalized Medicine

References

[1]Jeong-Kyun Kim, Myung-Nam Bae, K. Lee, Jae-Chul Kim, S. Hong. Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life. Biosensors. 2022. DOI: 10.3390/bios12030167 [2]Yangbing Hu. Robotics and Artificial Intelligence Applications in Remote Data Acquisition, Data Analysis and Disease Early Warning. IEEE. 2022. DOI: 10.1109/EEBDA53927.2022.9745020 [3]K. Kioskli, Spyridon Papastergiou. A Swarm Artificial Intelligence Approach for Effective Treatment of Chronic Conditions. IEEE. 2023. DOI: 10.1109/DRCN57075.2023.10108128 [4]Olga A. Sawicki, Angelina Mueller, R. Klaassen-Mielke, Anastasiya Glushan, F. Gerlach, M. Beyer, M. Wensing, Kateryna Karimova. Strong and sustainable primary healthcare is associated with a lower risk of hospitalization in high risk patients. Scientific Reports. 2021. DOI: 10.1038/s41598-021-83962-y [5]Susan Tran-Nguyen, Stephen Edward Asha. A collaborative pharmacist-led intervention to prevent hospital readmissions among elderly patients discharged from the emergency department: a retrospective cohort study. Scientific Reports. 2024. DOI: 10.1038/s41598-024-64968-8 [6]A. Romanenko, Ю. Л. Кучин, К. Ю. БЄлка, И. А. Токар. Perioperative pain management in elderly patients with a proximal femoral fracture: evidence review. Pain Medicine Journal. 2020. DOI: 10.31636/pmjua.v4i4.2 [7]T. Borg, J. Eisold, Yusuf Miyanjo, E. Pappa. The effect of COVID-19 on surgical management of skin cancers of the head, face and neck in elderly patients. Skin Health and Disease. 2023. DOI: 10.1002/ski2.175 [8]Zhiyang Cong, Mingshu Huo, Xing Jiang, Hongyu Yu. Factors associated with the level of self-management in elderly patients with chronic diseases: a pathway analysis. BMC Geriatrics. 2024. DOI: 10.1186/s12877-024-04956-9 . [9]Li Chen, Yinghua Gong, Liangzhuo Yuan. Health behaviour and its determinants in elderly patients with chronic diseases: evidence from Jiangsu Province, China. BMC Geriatrics. 2022. DOI: 10.1186/s12877-022-03010-w [10]Xiaoming Wang, Cun-yuan Zhang. Chinese expert consensus on the diagnosis and treatment of chronic heart failure in elderly patients (2021). Aging Medicine. 2022. DOI: 10.1002/agm2.12215 [11]Yongli Wang, Xiaodan Li, Dongmei Jia, Bei-lei Lin, B. Fu, Bei Qi, Zhenxiang Zhang. Exploring polypharmacy burden among elderly patients with chronic diseases in Chinese community: a cross-sectional study. BMC Geriatrics. 2021. DOI: 10.1186/s12877-021-02247-1 [12]J. Plaza-Zamora, I. Legaz, E. Osuna, M. Pérez-Cárceles. Medication literacy in elderly patients with chronic diseases: a community pharmacy perspective. Research Square. 2020. DOI: 10.21203/rs.2.20968/v1 [13]Xin Wang, Shihui Wang, Xiaojia Yu, Zhuo Ma, Huaguang Wang, Jing Yang, Lihong Liu. Impact of pharmacist-led medication therapy management in ambulatory elderly patients with chronic diseases. British Journal of Clinical Pharmacology. 2020. DOI: 10.1111/bcp.14709. [14]Shaoliang Tang, Yun Xu, Zhengjun Li, Tongling Yang, David Qian. Does Economic Support Have an Impact on the Health Status of Elderly Patients with Chronic Diseases in China? - Based on CHARLS (2018) Data Research. Frontiers in Public Health. 2021. DOI: 10.3389/fpubh.2021.658830 [15]Zhixu Hu, Hang Qiu, Liya Wang, Minghui Shen. Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission. BMC Medical Informatics and Decision Making. 2022. DOI: 10.1186/s12911-022-01802-z [16]Lanyu Lu, Bowei Liu, F. Yin. Alternative skeletal muscle index for sarcopenia diagnosis in elderly patients with type 2 diabetes mellitus: A pilot study. Frontiers in Endocrinology. 2023. DOI: 10.3389/fendo.2023.1083722 [17]Hiroyuki Inoshita, Daisuke Asaoka, Kei Matsuno, N. Yanagisawa, Yusuke Suzuki, Katsumi Miyauchi. Cross-Sectional Study on the Association between Dietary Patterns and Sarcopenia in Elderly Patients with Chronic Kidney Disease Receiving Conservative Treatment. Nutrients. 2023. DOI: 10.3390/nu15234994 [18]Xintian Cai, Junli Hu, Mengru Wang, Wen Wen, Jingyu Wang, Wenbo Yang, Yujie Dang, Q. Luo, Jing Hong, Nan-fang Li. Association between the sarcopenia index and the risk of stroke in elderly patients with hypertension: a cohort study. Aging. 2023. DOI: 10.18632/aging.204587 [19]Yang Hou, Jia Xiang, Bo Wang, Shoufeng Duan, Rouxuan Song, Wenhu Zhou, Songwen Tan, Binsheng He. Pathogenesis and comprehensive treatment strategies of sarcopenia in elderly patients with type 2 diabetes mellitus. Frontiers in Endocrinology. 2024. DOI: 10.3389/fendo.2023.1263650 [20]G. Ma, Lingling Zou, W. Dai, Z-Z Wang, Y-H Cao. The association between glucose fluctuation with sarcopenia in elderly patients with type 2 diabetes mellitus. European Review for Medical and Pharmacological Sciences. 2023. DOI: 10.26355/eurrev_202303_31557 [21]Satoshi Ida, K. Imataka, Shoki Morii, Keitaro Katsuki, Kazuya Murata. Frequency and Overlap of Cachexia, Malnutrition, and Sarcopenia in Elderly Patients with Diabetes Mellitus: A Study Using AWGC, GLIM, and AWGS2019. Nutrients. 2024. DOI: 10.3390/nu16020236 [22]A. Kamiya, Tsutomu Hayashi, Ryota Sakon, K. Ishizu, T. Wada, Sho Otsuki, Y. Yamagata, H. Katai, T. Yoshikawa. Acceleration of sarcopenia in elderly patients who develop asymptomatic pneumonia shadow within one year after surgery for early gastric cancer. BMC Surgery. 2023. DOI: 10.1186/s12893-023-02096-2 [23]Yogesh Kumar, Apeksha Koul, Ruchika Singla, M. Ijaz. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing. 2022. DOI: 10.1007/s12652-021-03612-z [24]M. Rakhimov, Ravshanjon Akhmadjonov, Shahzod Javliev. Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning. IEEE. 2022. DOI: 10.1109/AICT55583.2022.10013587 [25]Yiqing Xu, Zalizah Awang Long, D. Setyohadi. A Comprehensive Review on the Application of Artificial Intelligence in Chronic Obstructive Pulmonary Disease (COPD) Management. IEEE. 2024. DOI: 10.1109/IMCOM60618.2024.10418416 [26]Yi Xie, Lin Lu, F. Gao, Shuang-jiang He, Hui-juan Zhao, Ying Fang, Jiaming Yang, Y. An, Z. Ye, Zhe Dong. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Journal of Zhejiang University Science B. 2021. DOI: 10.1007/s11596-021-2485-0 [27]Natalie Nathan, Michael Saring, Noam Savion-Gaiger, Kira Radinsky, Alma Peri. Artificial Intelligence in Healthcare: A New Frontier in Predicting and Managing Chronic Diseases. PubMed. 2024. DOI: 10.1097/JPA.0000000000000208 [28]Dileep Kumar Murala, S. K. Panda, Sujata Priyambada Dash. MedMetaverse: Medical Care of Chronic Disease Patients and Managing Data Using Artificial Intelligence, Blockchain, and Wearable Devices State-of-the-Art Methodology. IEEE Access. 2023. DOI: 10.1109/ACCESS.2023.3340791 [29]Jingyi Wen, Dong Liu, Qianni Wu, Lanqin Zhao, Wai Cheng Iao, Haotian Lin. Retinal image-based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives. Visual Informatics. 2023. DOI: 10.1002/VIW.20220070 [30]Amanda Kote. Innovation in Healthcare through Technology and Artificial Intelligence – Case Study: VitalView Blood Pressure Monitoring System. Ingenious Healthcare. 2024. DOI: 10.58944/eqld4771 [31]Diego Lopez-Bernal, David C. Balderas, P. Ponce, Mario Rojas, A. Molina. Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases—A Review. Life. 2023. DOI: 10.3390/life13041031 [32]M. Cingolani, R. Scendoni, P. Fedeli, Fabio Cembrani. Artificial intelligence and digital medicine for integrated home care services in Italy: Opportunities and limits. Frontiers in Public Health. 2023. DOI: 10.3389/fpubh.2022.1095001 [33]Basma Boukenze. Earlier Forecasting of Chronic Kidney Failure: Performance of artificial intelligence in healthcare. IEEE CiSt. 2023. DOI: 10.1109/CiSt56084.2023.10409921 [34]Leelkanth Dewangan, Mahesh V. Shitole, J. P. Patra, Jalaluddin Khan, Gopesh Kumar Dawda, Prasanta Kumar Parida. Machine Learning-Based Risk Prediction for Coronary Heart Disease Using Clinical Data. IEEE. 2023. DOI: 10.1109/ICAIIHI57871.2023.10488969 [35]Ranieri Guerra. Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes. Italian Journal of Medicine. 2024. DOI: 10.4081/itjm.2024.1721 [36]Julia M. Puaschunder. The Future of Artificial Intelligence in International Healthcare: An Index. SSRN. 2020. DOI: 10.2139/ssrn.3623530 [37]Rejath Jose, Faiz Syed, Anvin Thomas, Milan Toma. Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions. Applied Sciences. 2024. DOI: 10.3390/app14052132 [38]Israa Abu-elezz, Asmaa Hassan, B. Jaber, Mohamad Sharique, Alaa A. Abd-alrazaq, Mowafa J Househ, T. Alam, Zubair Shah. Contribution of Artificial Intelligence in Pregnancy: A Scoping Review. IOS Press. 2022. DOI: 10.3233/shti210927 [39]X. Huo, H. Krumholz, X. Bai, E. Spatz, Q. Ding, P. Horak, Weigang Zhao, Q. Gong, Haibo Zhang, Xiaofang Yan, Ying Sun, Jiamin Liu, Xuekun Wu, Wenchi Guan, Xiuling Wang, Jing Li, Xi Li, J. Spertus, F. Masoudi, Xin Zheng. Effects of Mobile Text Messaging on Glycemic Control in Patients with Coronary Heart Disease and Diabetes Mellitus. Circulation: Cardiovascular Quality and Outcomes. 2019. DOI: 10.1161/CIRCOUTCOMES.119.005805 [40]S. Elghamrawy, A. Darwish, A. Hassanien. Monitoring COVID-19 Disease Using Big Data and Artificial Intelligence-Driven Tools. Springer. 2020. DOI: 10.1007/978-3-030-63307-3_10 [41]A. S. Yadav, Anupam Swami, Navin Ahlawat, Priyanka Agarwal. A Comprehensive Review of Advancements in Bladder Cancer Screening: Harnessing Mathematics and Artificial Intelligence for Early Detection and Personalized Management. GeNex. 2024. DOI: 10.55248/gengpi.5.0624.1547 [42]Wan D. Bae, Shayma Alkobaisi, Matthew Horak, Choon-Sik Park, Sungroul Kim, Joel Davidson. Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models. Life. 2022. DOI: 10.3390/life12101631 [43]K. Drabiak, Skylar Kyzer, Valerie Nemov, I. E. El Naqa. AI and ML ethics, Law, Diversity, and Global Impact. British Journal of Radiology. 2023. DOI: 10.1259/bjr.20220934 [44]Aloysius Ochasi, A. J. Mahamadou, Russ B. Altman. Justice in Healthcare Artificial Intelligence in Africa. arXiv. 2024. DOI: 10.48550/arXiv.2406.10653 [45]Onyekachukwu R. Okonji, Kamol Yunusov, Bonnie Gordon. Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations. TechRxiv. 2024. DOI: 10.36227/techrxiv.171527587.75649430/v1 [46]Mansi Dhingra. Bioethical Considerations of Artificial Intelligence in Healthcare Management. International Journal of Science and Research. 2023. DOI: 10.21275/sr23709144913 [47]Hisham E Hasan, Deema Jaber, O. Khabour, K. Alzoubi. Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study. BMC Medical Ethics. 2024. DOI: 10.1186/s12910-024-01062-8 [48]Krati Dubey, Chaitali B. Kasar, Guravaiah Thulluri, Anuradha Yesugad, Mehaboob Karishma, Dipanjali Padhi. Leveraging Big Data for Effective Healthcare Management with Artificial Intelligence. IEEE. 2023. DOI: 10.1109/ICAIIHI57871.2023.10489731 [49]Iqra Basharat, Subhan Shahid. AI-enabled chatbots healthcare systems: an ethical perspective on trust and reliability. Journal of Health Organization and Management. 2024. DOI: 10.1108/jhom-10-2023-0302 [50]M. Rincón, R. Martínez-Tomás, J. M. Ferrández. Assistive intelligence for the elderly. Expert Systems. 2020. DOI: 10.1111/exsy.12535 [51]Thierry Moulin, J. Dobson, J. Hixson, Theodoros N. Arvanitis. Digital Health: The journal's pioneering journey 6 years on. Digital Health. 2021. DOI: 10.1177/20552076211034034 [52]Ritika R. Chaturvedi, Wendy PhD. Consumer digital health and person generated health data – opportunities and challenges for sleep disparities research and clinical practice. Sleep. 2022. DOI: 10.1093/sleep/zsac079.056 [53]Wasim Alamgir, A. Mohyuddin. Healthcare Analytics: Applications and Challenges. Life and Science. 2022. DOI: 10.37185/lns.1.1.263 [54]Zhijun Yan, Roberta Bernardi, Ni Huang, Younghoon Chang. Guest editorial: The bright side and the dark side of digital health. Internet Research. 2021. DOI: 10.1108/intr-12-2021-682.