Evaluating Artificial Intelligence in Medical Communication for Enhancing Doctor-Patient Interactions and Outcomes
DOI:
https://doi.org/10.56294/mw2024493Keywords:
Artificial Intelligence, Doctor-Patient Communication, Patient Outcomes, Healthcare Chatbots, Medical AI AssistantsAbstract
Medical communication using artificial intelligence (AI) might improve relationships between physicians and patients and provide better outcomes on health. It looks at how consumers and healthcare professionals may interact more easily thanks to AI-powered solutions such virtual assistants and robots. AI technologies may provide physicians accurate, current information, assist in clinical decision-making, reduce their mental load so they may concentrate on more challenging aspects of treatment. Moreover made accessible seven days a week, twenty-four hours a day are artificial intelligence technologies. In this sense, individuals may always acquire customised to their medical requirements answers and assistance. This paper investigates how effectively artificial intelligence technologies could raise patient engagement in medical exchanges, confidence, and pleasure. Examining many artificial intelligence systems used in healthcare reveals how they enable individuals to interact better by addressing shared issues such language and mental stress, and how they assist to solve the shortage of healthcare professionals. Particularly with regard to safeguarding data privacy and maintaining the human touch in healthcare, we also discuss the moral questions and challenges raised by artificial intelligence usage. Particularly how quickly and precisely AI can provide medical advice, assist to reduce the risk of misunderstandings, and enhance treatment commitment, researchers are also investigating how AI influences patient outcomes. The results of the research show that although artificial intelligence is a terrific tool for enhancing doctor-patient communication, it must be utilised wisely so as not to replace human understanding. Better, more knowledgeable, and more compassionate treatment for patients in the future is what we expect to see by supporting AI systems and healthcare professionals to cooperate. This will raise their confidence in the medical system and enhance their general state of wellness.
References
Dave, M.; Patel, N. Artificial intelligence in healthcare and education. Br. Dent. J. 2023, 234, 761–764.
Brambilla, A.; Sun, T.-Z.; Elshazly, W.; Ghazy, A.; Barach, P.; Lindahl, G.; Capolongo, S. Flexibility during the COVID-19 Pandemic Response: Healthcare Facility Assessment Tools for Resilient Evaluation. Int. J. Environ. Res. Public Health 2021, 18, 11478.
Prakash, S.; Balaji, J.N.; Joshi, A.; Surapaneni, K.M. Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare-A Scoping Review of Reviews. J. Pers. Med. 2022, 12, 1914.
Dinesh Chandrakar, N. K. Saikhedkar . (2016). Design of Ammonia Water Vapour Absorption Air Conditioning System for a Car by Waste Heat Recovery from Engine Exhaust gas. Advance Physics Letter, 3(2), 24-29
Pisapia, A.; Banfi, G.; Tomaiuolo, R. The novelties of the regulation on health technology assessment, a key achievement for the European union health policies. Clin. Chem. Lab. Med. CCLM 2022, 60, 1160–1163.
Wang, C.; Zhang, J.; Lassi, N.; Zhang, X. Privacy Protection in Using Artificial Intelligence for Healthcare: Chinese Regulation in Comparative Perspective. Healthcare 2022, 10, 1878.
Townsend, B.A.; Sihlahla, I.; Naidoo, M.; Naidoo, S.; Donnelly, D.-L.; Thaldar, D.W. Mapping the regulatory landscape of AI in healthcare in Africa. Front. Pharmacol. 2023, 14, 1214422.
Marengo, A.; Pagano, A. Investigating the Factors Influencing the Adoption of Blockchain Technology across Different Countries and Industries: A Systematic Literature Review. Electronics 2023, 12, 3006.
Moldt, J.-A.; Festl-Wietek, T.; Madany Mamlouk, A.; Nieselt, K.; Fuhl, W.; Herrmann-Werner, A. Chatbots for future docs: Exploring medical students’ attitudes and knowledge towards artificial intelligence and medical chatbots. Med. Educ. Online 2023, 28, 2182659.
Bartels, R.; Dudink, J.; Haitjema, S.; Oberski, D.; van ‘t Veen, A. A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care. Front. Digit. Health 2022, 4, 942588.
Shams, R.A.; Zowghi, D.; Bano, M. AI and the quest for diversity and inclusion: A systematic literature review. AI Ethics 2023.
Feng, J.; Phillips, R.V.; Malenica, I.; Bishara, A.; Hubbard, A.E.; Celi, L.A.; Pirracchio, R. Clinical artificial intelligence quality improvement: Towards continual monitoring and updating of AI algorithms in healthcare. npj Digit. Med. 2022, 5, 66.
Boonstra, A.; Laven, M. Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review. BMC Health Serv. Res. 2022, 22, 669.
Lorenzon, M.; Spina, E.; Franco, F.T.D.; Giovannini, I.; Vita, S.D.; Zabotti, A. Salivary Gland Ultrasound in Primary Sjögren’s Syndrome: Current and Future Perspectives. Open Access Rheumatol. Res. Rev. 2022, 14, 147–160.
Hogg, H.D.J.; Al-Zubaidy, M.; Talks, J.; Denniston, A.K.; Kelly, C.J.; Malawana, J.; Papoutsi, C.; Teare, M.D.; Keane, P.A.; Beyer, F.R.; et al. Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence. J. Med. Internet Res. 2023, 25, 39742.
Miller, G.J. Stakeholder roles in artificial intelligence projects. Proj. Leadersh. Soc. 2022, 3, 100068.
Kordi, M.; Dehghan, M.J.; Shayesteh, A.A.; Azizi, A. The impact of artificial intelligence algorithms on management of patients with irritable bowel syndrome: A systematic review. Inform. Med. Unlocked 2022, 29, 100891.
Alcocer Alkureishi, M.; Lenti, G.; Choo, Z.-Y.; Castaneda, J.; Weyer, G.; Oyler, J.; Lee, W.W. Teaching Telemedicine: The Next Frontier for Medical Educators. JMIR Med. Educ. 2021, 7, e29099.
Murri, R.; Masciocchi, C.; Lenkowicz, J.; Fantoni, M.; Damiani, A.; Marchetti, A.; Sergi, P.D.A.; Arcuri, G.; Cesario, A.; Patarnello, S.; et al. A real-time integrated framework to support clinical decision making for COVID-19 patients. Comput. Methods Programs Biomed. 2022, 217, 106655.
Published
Issue
Section
License
Copyright (c) 2024 Frederick Sidney Correa, Rohini, Shashikant Patil, Satya Ranjan Das, Kamineni Sairam, Jagtej Singh (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
