Human and AI: Collaborative Medicine in the Age of Technology


  • Mehrdad Farrokhi
  • Fatemeh Taheri
  • Masoud Farrokhi
  • Nikoo Emtiazi
  • Mostafa Talebi
  • Abdullatif Akbari
  • Amirhossein Esfahani
  • Shahab Fatemi
  • Hadi Assadian
  • Behnaz Dalvandi
  • Salman Delavar
  • Atousa Moghadam Fard
  • Javaneh Atighi
  • Faraz Madanchi
  • Amirali Azimi
  • Fatemeh-sadat Tabatabaei
  • Reyhane Nematollahi
  • Aram Farhoudian
  • Zeinab Mansouri
  • Ava Zand
  • Habib Azimi
  • Lida Zare Lahijan
  • Arash Rahimi
  • Saeideh Khaleghpanah
  • Hamed Ghorbani
  • Ali Karami-Nejad
  • Zeinab Fotouhi Ashin
  • Ramila Abedi Azar
  • Atieh Sadeghniiat-Haghighi
  • Mehrdad Maghbouli
  • Shiva Karimian
  • Azin Khorramdel
  • Mansoureh Taghizadeh
  • Elham Shabani
  • Zahra Yazdani
  • Ariyan Nazemi
  • Hossein Boustani Hezarani
  • Nasrin Etesamifard
  • Behzad Garousi
  • Mahzad Yousefi
  • Hournaz Hassanzadeh


Human , Artificial Intelligence , Collaborative Medicine , Technology


In the era of rapidly advancing technology, the collaboration between humans and artificial intelligence (AI) is reshaping the landscape of medicine, ushering in an era of collaborative medicine. This partnership harnesses the unique strengths of both humans and AI to revolutionize healthcare delivery, diagnosis, treatment, and patient care. At the heart of this collaboration is the recognition that while AI possesses unparalleled computational power and can analyze vast amounts of data with lightning speed, it lacks the empathy, intuition, and contextual understanding inherent in human cognition. Conversely, humans bring to the table their deep understanding of the complexities of disease, their ability to interpret nuanced patient responses, and their capacity for empathy and bedside manner. In the realm of diagnostics, AI algorithms can sift through massive datasets of medical images, lab results, and patient records to identify patterns and anomalies that might escape human detection. By analyzing these patterns, AI can assist clinicians in making more accurate and timely diagnoses, thereby improving patient outcomes and reducing the risk of misdiagnosis. Moreover, AI-driven predictive analytics can help identify patients at high risk of developing certain conditions or experiencing adverse events, allowing healthcare providers to intervene proactively and prevent potential health crises. This predictive capability can be particularly valuable in chronic disease management, where early intervention can significantly improve long-term outcomes. In treatment planning, AI-powered decision support systems can analyze vast databases of clinical guidelines, research studies, and patient data to recommend personalized treatment regimens tailored to each patient's unique profile. These recommendations can help clinicians navigate the complexities of modern medicine, ensuring that patients receive the most effective and appropriate care.


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Farrokhi M, Khouzani SJ, Farrokhi M, Jalayeri H, Faranoush P, Babaei M, et al. Artificial Intelligence and Deep Learning for Screening and Risk Assessment of Cancers. Kindle. 2024;4(1):1-140.

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Farrokhi M, Taheri F, Farrokhi M, Moeini A, Tooyserkani SH, Shahali A, et al. Anti-Aging Strategies to Prevent Diseases: Promoting Longevity and Optimal Health. Kindle. 2024;4(1):1-194.

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Rezaei M, Rahmani E, Khouzani SJ, Rahmannia M, Ghadirzadeh E, Bashghareh P, et al. Role of artificial intelligence in the diagnosis and treatment of diseases. Kindle. 2023;3(1):1-160.

Rezaei M, Saei S, Khouzani SJ, Rostami ME, Rahmannia M, Manzelat AMR, et al. Emerging Technologies in Medicine: Artificial Intelligence, Robotics, and Medical Automation. Kindle. 2023;3(1):1-184.

Rezaei T, Khouzani PJ, Khouzani SJ, Fard AM, Rashidi S, Ghazalgoo A, et al. Integrating Artificial Intelligence into Telemedicine: Revolutionizing Healthcare Delivery. Kindle. 2023;3(1):1-161.

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Taheri F, Farrokhi M, Fard AM, Rahmani E, Soltani R, Shamsedanesh S, et al. Role of Micronutrients and Nutrition in Prevention and Treatment of Cancers. Kindle. 2023;3(1):1-102.

Taheri F, Rahmani E, Fard SS, Rezaei M, Ayati A, Farhoudian A, et al. Aging Process and Related Diseases. Kindle. 2023;3(1):1-117.

Talaie R, Fard SS, Forouhi M, Fard AM, Fard TM, Dadashzadehasl N, et al. Applications, Limitations, and Guidelines for the Use of Telemedicine in Medical Departments. Kindle. 2022;2(1):1-118.

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Mehrzad V, Mehdizadeh M, Parkhideh S, Rafiee A, Jamshidian N. The Effects of Premedication with Desipramine on Bone Marrow Mobilization for Autologous Bone Marrow Transplantation in Multiple Myeloma. International Journal of Cancer Management. 2023;16(1).

Human and AI: Collaborative Medicine in the Age of Technology




How to Cite

Farrokhi, M., Taheri, F., Farrokhi, M., Emtiazi, N., Talebi, M., Akbari, A., Esfahani, A., Fatemi, S., Assadian, H., Dalvandi, B., Delavar, S., Moghadam Fard, A., Atighi, J., Madanchi, F., Azimi, A., Tabatabaei, F.- sadat, Nematollahi, R., Farhoudian, A., Mansouri, Z., Zand, A., Azimi, H., Zare Lahijan, L., Rahimi, A., Khaleghpanah, S., Ghorbani, H., Karami-Nejad, A., Fotouhi Ashin, Z., Abedi Azar, R., Sadeghniiat-Haghighi, A., Maghbouli, M., Karimian, S., Khorramdel, A., Taghizadeh, M., Shabani, E., Yazdani, Z., Nazemi, A., Boustani Hezarani, H., Etesamifard, N., Garousi, B., Yousefi, M., & Hassanzadeh, H. (2024). Human and AI: Collaborative Medicine in the Age of Technology. Kindle, 4(1), 1–160. Retrieved from



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