Telemedicine and Artificial Intelligence

Enis Taha ÖZKANa , Onur İNAMb

aGazi University Graduate School of Health Sciences, Department of Biophysics, Ankara, Türkiye
bGazi University Faculty of Medicine, Department of Biophysics, Ankara, Türkiye

ABSTRACT
Telemedicine and artificial intelligence (AI) integration have become a major and distinct area in healthcare, especially during the COVID-19 pandemic, due to increased patient usage. AI enhances diagnostics, preventive care, and remote treatment. Telemedicine, bolstered by AI, is increasingly essential in providing quality care, especially during the COVID-19 pandemic, which significantly increased its adoption. Machine learning, deep learning, and AI’s subsets are instrumental in improving diagnostics, risk prediction, clinical applications, and patient care. The application of AI in telemedicine varies in many medical fields, like pain management and dementia patient care. This situation demonstrates the technology’s flexibility and convenience for patients with access barriers. Challenges such as data security, privacy, and the need for interoperability in connected healthcare services could also be addressed via AI, and AI could be posted as a solution. Future directions emphasize the role of 6G technology in enriching AI and telemedicine. The potential of AI and telemedicine, efficient healthcare for the global population’s diverse needs while ensuring cost-effectiveness and data privacy could be achieved. Overall, the integration of AI and telemedicine can cause transformative advancements in modern healthcare practices.
Keywords: Telemedicine; artificial intelligence; deep learning; machine learning

Referanslar

  1. Hazratifard M, Gebali F, Mamun M. Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial. Sensors (Basel). 2022;22(19):7655. [Crossref]  [PubMed]  [PMC]
  2. Kiburg KV, Turner A, He M. Telemedicine and delivery of ophthalmic care in rural and remote communities: Drawing from Australian experience. Clin Exp Ophthalmol. 2022;50(7):793-800. [Crossref]  [PubMed]  [PMC]
  3. Pap IA, Oniga S. A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence. Int J Environ Res Public Health. 2022;19(18): 11413. [Crossref]  [PubMed]  [PMC]
  4. Cechner RL, Carter JR. Storage and Retrieval of SNOP-coded Pathologic Diagnoses Using Offsite Computing and Optical Character Recognizing Systems. Am J Clin Pathol. 1976;65(5):654-61. [Crossref]  [PubMed]
  5. Hackl WO, Neururer SB, Pfeifer B. Telemedicine Research from Big Bang to 2022. Stud Health Technol Inform. 2023;301:220-4. [Crossref]  [PubMed]
  6. Qi J, Wu C, Yang L, Ni C, Liu Y. Artificial intelligence (AI) for home support interventions in dementia: a scoping review protocol. BMJ Open. 2022;12(9): e062604. [Crossref]  [PubMed]  [PMC]
  7. Hsiao V, Chandereng T, Huebner JA, Kunstman DT, Flood GE, Tevaarwerk AJ, et al. Telemedicine Use across Medical Specialties and Diagnoses. Appl Clin Inform. 2023;14(1):172-84. [Crossref]  [PubMed]  [PMC]
  8. Drake C, Lian T, Cameron B, Medynskaya K, Bosworth HB, Shah K. Understanding Telemedicine's "New Normal": Variations in Telemedicine Use by Specialty Line and Patient Demographics. Telemed J E Health. 2022;28(1): 51-9. [Crossref]  [PubMed]  [PMC]
  9. Perez J, Niburski K, Stoopler M, Ingelmo P. Telehealth and chronic pain management from rapid adaptation to long-term implementation in pain medicine: A narrative review. Pain Rep. 2021;6(1):e912. [Crossref]  [PubMed]  [PMC]
  10. Cascella M, Coluccia S, Grizzuti M, Romano MC, Esposito G, Crispo A, et al. Satisfaction with Telemedicine for Cancer Pain Management: A Model of Care and Cross-Sectional Patient Satisfaction Study. Curr Oncol. 2022;29(8):5566-78. [Crossref]  [PubMed]  [PMC]
  11. Cascella M, Schiavo D, Grizzuti M, Romano MC, Coluccia S, Bimonte S, et al. Implementation of a Hybrid Care Model for Telemedicine-based Cancer Pain Management at the Cancer Center of Naples, Italy: A Cohort Study. In Vivo. 2023;37(1):385-92. [Crossref]  [PubMed]  [PMC]
  12. Cascella M, Scarpati G, Bignami EG, Cuomo A, Vittori A, Di Gennaro P, et al. Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management. J Anesth Analg Crit Care. 2023;3(1):19. [Crossref]  [PubMed]  [PMC]
  13. Moyle W, Arnautovska U, Ownsworth T, Jones C. Potential of telepresence robots to enhance social connectedness in older adults with dementia: an integrative review of feasibility. Int Psychogeriatr. 2017;29(12):1951-64. [Crossref]  [PubMed]  [PMC]
  14. König A, Francis LE, Joshi J, Robillard JM, Hoey J. Qualitative study of affective identities in dementia patients for the design of cognitive assistive technologies. J Rehabil Assist Technol Eng. 2017;4:2055668316685038. [Crossref]  [PubMed]  [PMC]
  15. Arthanat S, Begum M, Gu T, LaRoche DP, Xu D, Zhang N. Caregiver perspectives on a smart home-based socially assistive robot for individuals with Alzheimer's disease and related dementia. Disabil Rehabil Assist Technol. 2020;15(7):789-98. [Crossref]  [PubMed]
  16. Tamura T, Yonemitsu S, Itoh A, Oikawa D, Kawakami A, Higashi Y, et al. Is an entertainment robot useful in the care of elderly people with severe dementia? J Gerontol A Biol Sci Med Sci. 2004;59(1):83-5. [Crossref]  [PubMed]
  17. Moyle W, Jones C, Sung B. Telepresence robots: Encouraging interactive communication between family carers and people with dementia. Australas J Ageing. 2020;39(1):e127-e33. [Crossref]  [PubMed]
  18. Sodhro AH, Zahid N. AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications. Sensors (Basel). 2021;21(23):8039. [Crossref]  [PubMed]  [PMC]
  19. Nayak S, Patgiri R. 6G Communication Technology: A Vision on Intelligent Healthcare. 2020. [Crossref]
  20. Sodhro AH, Pirbhulal S, Luo Z, Muhammad K, Zahid NZ. Toward 6G Architecture for Energy-Efficient Communication in IoT-Enabled Smart Automation Systems. IEEE Internet of Things Journal. 2021;8(7):5141-8. [Crossref]
  21. Padhi PK, Charrua-Santos F. 6G Enabled Tactile Internet and Cognitive Internet of Healthcare Everything: Towards a Theoretical Framework. Applied System Innovation. 2021;4(3):66. [Crossref]
  22. Zong B, Fan C, Wang X, Duan X, Wang B, Wang J. 6G Technologies: Key Drivers, Core Requirements, System Architectures, and Enabling Technologies. IEEE Vehicular Technology Magazine. 2019;14(3):18-27. [Crossref]
  23. Zhang L, Liang YC, Niyato D. 6G Visions: Mobile ultra-broadband, super internet-of-things, and artificial intelligence. China Communications. 2019;16:1-14. [Crossref]
  24. Sodhro A, Pirbhulal S, Albuquerque VHC. Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications. IEEE Transactions on Industrial Informatics. 2019;15(7): 4235-43. [Crossref]
  25. Zhang S, Xiang C, Xu S. 6G: Connecting Everything by 1000 Times Price Reduction. IEEE Open Journal of Vehicular Technology. 2020;1:107-15. [Crossref]
  26. Zhang Y, Di B, Wang P, Lin J, Song L. HetMEC: Heterogeneous Multi-Layer Mobile Edge Computing in the 6 G Era. IEEE Transactions on Vehicular Technology. 2020;69(4):4388-400. [Crossref]
  27. Muzammal M, Talat R, Sodhro AH, Pirbhulal S. A Multi-sensor Data Fusion Enabled Ensemble Approach for Medical Data from Body Sensor Networks. Information Fusion. 2020;53(2020):155-64. [Crossref]
  28. Sodhro A, Luo S, Gul Hassan S, Muzamal M, Rodrigues J, Albuquerque VHC. Artificial Intelligence based QoS optimization for multimedia communication in IoV systems. Future Generation Computer Systems. 2019;95. [Crossref]