Artificial Intelligence for Remote Patient Monitoring: Advancements, Applications, and Challenges


  • Mehrdad Farrokhi
  • Fatemeh Taheri
  • Amir Moeini
  • Masoud Farrokhi
  • Mousavi Zadeh Sayed Alireza
  • Maryam Farahmandsadr
  • Ehsan Bahrami Hezaveh
  • Ali Davoodi
  • Sepideh Niknejad
  • Mahmonir Bayanati
  • Barzan Soleimani
  • Saeedeh Shirdel
  • Mohammad Hamidi Madani
  • Fatemeh Pourali
  • Yasser Asghari Vostacolaee
  • Seyedmohammadmahan Mir Nasiri
  • Farzaneh Alvandi
  • Pegah Moharrami Yeganeh
  • Fateme Nozari
  • Fatemeh Malek
  • Saman Rabiei
  • Seyed Pooriya Moshashaei
  • Seyed Hasan Khatami shal
  • Ashkan Azizi
  • Mohammad Mehdi Shadravan
  • Mahyar Noorbakhsh
  • Habib Azimi
  • Ehsan Fayyazishishavan
  • Maryam Amini Rankouhi
  • Ghazal Daftari
  • Elahe Abdi Bastami
  • Zohreh Ranjbar
  • Ziba Abbasian
  • Abdolreza Rouientan
  • Mohadese Ahmadzade
  • Halimberdy Gharajeh
  • Rahil GhorbaniNia
  • Reza Fathazam
  • Marjan Dehdilani
  • Mehrdad Mohammadian
  • Fataneh Bakhshi
  • Atieh Sadeghniiat-Haghighi
  • Nasim Nouri
  • Parya Safarkhanlou
  • Kourosh Shahraki
  • Mohammad Khosousi Sani
  • Roya Khorram
  • Somayeh Doosti
  • Fatemeh Rostamian Motlagh
  • Roozbeh Roohinezhad
  • Setareh Hedayati Emami
  • Fatemeh Kazemi
  • Ali Karami-Nejad
  • Ramila Abedi Azar
  • Zahrasadat Rezaei
  • Babak Goodarzy
  • Paniz Sabeghi
  • Behzad Garousi
  • Mostafa Yahyazadeh Andevari


Artificial Intelligence , Remote Patient Monitoring, Applications, Challenges


Artificial Intelligence (AI) has emerged as a transformative force in the healthcare sector, particularly in the realm of remote patient monitoring (RPM). RPM involves the collection, analysis, and interpretation of patient data outside of traditional clinical settings, allowing healthcare providers to monitor patients' health remotely. Advancements in AI have significantly enhanced RPM by enabling more accurate and timely monitoring, diagnosis, and intervention, thereby improving patient outcomes and reducing healthcare costs. One of the key applications of AI in RPM is predictive analytics, where algorithms analyze patient data to identify patterns and predict potential health issues before they escalate. This proactive approach allows healthcare providers to intervene early, preventing complications and hospitalizations. AI-powered wearables and sensors collect continuous data on vital signs, activity levels, and other health metrics, providing a comprehensive view of patients' health status in real-time. Machine learning algorithms analyze this data to detect anomalies and trends, alerting healthcare providers to any deviations from normal parameters. Furthermore, AI facilitates personalized medicine by tailoring treatment plans to individual patients based on their unique characteristics and medical history. By integrating AI-driven decision support systems into RPM platforms, healthcare providers can make more informed clinical decisions, optimize resource allocation, and improve the efficiency of healthcare delivery. In conclusion, AI holds immense potential to revolutionize remote patient monitoring by enabling more personalized, proactive, and efficient healthcare delivery. Addressing the challenges associated with its implementation will be crucial in realizing the full benefits of AI in RPM and improving patient care outcomes.


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Artificial Intelligence for Remote Patient Monitoring: Advancements, Applications, and Challenges




How to Cite

Farrokhi , M., Taheri, F., Moeini, A., Farrokhi, M., Sayed Alireza , M. Z., Farahmandsadr, M., Bahrami Hezaveh, E., Davoodi, A., Niknejad, S., Bayanati, M., Soleimani, B., Shirdel, S., Hamidi Madani, M., Pourali, F., Asghari Vostacolaee, Y., Mir Nasiri, S., Alvandi, F., Moharrami Yeganeh, P., Nozari, F., Malek, F., Rabiei, S., Moshashaei , S. P., Khatami shal, S. H., Azizi, A., Shadravan, M. M., Noorbakhsh, M., Azimi, H., Fayyazishishavan, E., Amini Rankouhi, M., Daftari, G., Abdi Bastami, E., Ranjbar, Z., Abbasian, Z., Rouientan, A., Ahmadzade, M., Gharajeh, H., GhorbaniNia, R., Fathazam, R., Dehdilani, M., Mohammadian, M., Bakhshi, F., Sadeghniiat-Haghighi, A., Nouri, N., Safarkhanlou, P., Shahraki, K., Khosousi Sani, M., Khorram, R., Doosti, S., Rostamian Motlagh, F., Roohinezhad, R., Hedayati Emami, S., Kazemi, F., Karami-Nejad, A., Abedi Azar, R., Rezaei, Z., Goodarzy, B., Sabeghi, P., Garousi, B., & Yahyazadeh Andevari, M. (2024). Artificial Intelligence for Remote Patient Monitoring: Advancements, Applications, and Challenges. Kindle, 4(1), 1–261. Retrieved from



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