Gotowe bibliografie tematyczne / Healthcare AI

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Gotowa bibliografia na temat „Healthcare AI”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Healthcare AI"

Chinta, Sribala Vidyadhari, Zichong Wang, Avash Palikhe, et al. "AI-driven healthcare: Fairness in AI healthcare: A survey." PLOS Digital Health 4, no. 5 (2025): e0000864. https://doi.org/10.1371/journal.pdig.0000864.

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Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These b

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Mahadev Mastud. "AI in HealthCare." International Journal of Scientific Research in Science and Technology 12, no. 1 (2025): 34–36. https://doi.org/10.32628/ijsrst25121152.

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Artificial Intelligence (AI) is revolutionizing the healthcare industry by enhancing diagnostics, streamlining workflows, and enabling more personalized patient care. By leveraging powerful algorithms and vast amounts of data, AI can identify patterns, predict outcomes, and assist healthcare professionals in making informed decisions. This introduction explores the transformative role of AI in healthcare, highlighting its applications, benefits, challenges, and potential for future advancements. From improving diagnostic accuracy to optimizing administrative processes, AI is reshaping the way

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J Dr. Uma Devi A, Naveen Raj K, Mugilan M, Supriya Latha Ananthan, Neha S N, and Sutharsan T. "AI in Healthcare." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1100–1111. https://doi.org/10.47392/irjaem.2025.0180.

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Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostics, treatment planning, and patient care. This article provides an overview of the use of AI in healthcare by means of Electronic Health Records (EHRs), AI symptom checkers, drug discovery, predictive health analysis, and telemedicine. AI has significantly increased efficiency, reduced medical errors, and offered personalized treatments. But present AI-enabled healthcare systems lack some features such as the lack of physical examination, cybersecurity, poor internet connectivity, and medical miscommunication via

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Satpute, Ms Puja D. "Healthcare Chatbot Using AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5846–51. http://dx.doi.org/10.22214/ijraset.2024.61249.

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Abstract: Medical chatbots mark a major advancement in patient care and dissemination of medical information. These AIpowered virtual assistants provide a variety of services, from providing medical information, facilitating appointments, and monitoring symptoms to providing psychological support and making it easier to find the nearest medical centre. It provides users with 24/7 access by easily advertising and pre-registering, improving user experience and making the healthcare process more efficient. Although medical chatbots have many advantages, it is important to know that their role is

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Talati, Dhruvitkumar. "AI in healthcare domain." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 3 (2023): 256–62. http://dx.doi.org/10.60087/jklst.vol2.n3.p253.

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Artificial Intelligence (AI) has emerged as a transformative force in the healthcare domain, revolutionizing various aspects of medical research, diagnostics, treatment, and patient care. This paper provides an overview of recent developments and applications of AI in healthcare, highlighting its potential to enhance efficiency, accuracy, and accessibility in medical practices. The integration of machine learning algorithms, natural language processing, and computer vision techniques has enabled AI systems to analyze vast amounts of medical data, support clinical decision-making, and personali

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Saiteja, Kobbaji. "Healthcare Chatbot Using AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49884.

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Abstract - In today's era, India faces a major challenge in providing affordable and accessible healthcare, especially in rural areas where transport and quality facilities are limited. To address this, we developed an AI-powered Healthcare Chatbot using Python. This system helps users get instant responses to health-related queries and locate nearby doctors, clinics, and hospitals using the Google Places API—crucial during emergencies. The project has two modules: User and Admin. Users can register, log in, manage profiles, chat with the bot, and access healthcare facility information. Admins

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Anand, Laxman Mhatre. "AI automation in healthcare." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 12, no. 2 (2024): 1–4. https://doi.org/10.5281/zenodo.14208968.

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As the demand for healthcare services surges, the industry is becoming increasingly vulnerable to issues such as patient engagement challenges, data overload, and diagnosis and prescription hurdles. AI automation is a novel technology that promises to address these challenges. This document discusses some of the challenges in healthcare and how AI automation mitigates these issues.

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Vishal, Ananda Kuwar, Mahesh Asawa Parth, Ravindra Patil Ganesh, Nandkumar Pawar Vedant, Jain Preeti, and Yedurkar Amit. "AI Driven Healthcare Solution." TIJER - INTERNATIONAL RESEARCH JOURNALS 12, no. 2 (2025): a117—a121. https://doi.org/10.5281/zenodo.15209814.

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Artificial Intelligence (AI) is transforming the healthcare sector by providing innovative, data-centric solutions to various medical challenges. This project aims to develop spe- cialized AI agents tailored for healthcare applications, focusing on creating systems such as Ortho AI for orthopedic-related queries and Dermatology AI for skin-related issues. These AI solutions provide patients with accurate, personalized healthcare advice, supporting them in obtaining quicker, more reliable responses. Additionally, they assist healthcare professionals by enhancing diagnosis and treatment recommen

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Pagidoju, Vijaybhasker. "RELIABLE AI SYSTEMS IN HEALTHCARE (AI MEETS SRE)." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY 16, no. 2 (2025): 37–58. https://doi.org/10.34218/ijcet_16_02_003.

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Rozprawy doktorskie na temat "Healthcare AI"

Andersson, Oscar, and Tim Andersson. "AI applications on healthcare data." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44752.

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The purpose of this research is to get a better understanding of how different machine learning algorithms work with different amounts of data corruption. This is important since data corruption is an overbearing issue within data collection and thus, in extension, any work that relies on the collected data. The questions we were looking at were: What feature is the most important? How significant is the correlation of features? What algorithms should be used given the data available? And, How much noise (inaccurate or unhelpful captured data) is acceptable? The study is structured to introdu

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PANIGUTTI, Cecilia. "eXplainable AI for trustworthy healthcare applications." Doctoral thesis, Scuola Normale Superiore, 2022. https://hdl.handle.net/11384/125202.

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Acknowledging that AI will inevitably become a central element of clinical practice, this thesis investigates the role of eXplainable AI (XAI) techniques in developing trustworthy AI applications in healthcare. The first part of this thesis focuses on the societal, ethical, and legal aspects of the use of AI in healthcare. It first compares the different approaches to AI ethics worldwide and then focuses on the practical implications of the European ethical and legal guidelines for AI applications in healthcare. The second part of the thesis explores how XAI techniques can help meet thr

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Jönsson, Josef. "AI acceptance and attitudes : People’s perception of healthcare and commercial AI applications." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176507.

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The relevance of AI is ever increasing. The goal of the wide implementation is usually either to boost task efficiency or for public comfort. To fuel this progression, more personal data is being used and Artificial intelligence inhabits the role of the human expert, in many different applications. This study investigated the attitudes and rates of acceptance to said AI applications and if they differed in relation to each other. Additionally, this study explored if general positive and negative attitude towards AI influence AI acceptance. Applications studied came from two different domains,

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Årnfelt, Theodor. "Risk-benefit perception of AI use : Public perception of AI in healthcare and commercial domains." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177750.

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AI applications are today implemented in a wide range of settings with the goal of achieving greater efficiency. However, these implementations can not be guaranteed to be free of risks. This study investigated how people perceive these risks and benefits, and whether there were any notable differences to be found between the domains in which they appear, in this case e-commerce/marketing and healthcare. Additionally, the relationship between the perceptions and individual positive and negative attitudes towards AI were examined by utilizing an affect heuristic framework. The findings showed t

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State, Christopher. "Designing conversational AI for digital healthcare in regards to accessibility." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279589.

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The area of Conversational AI in healthcare is gaining traction, especially in this rapid and global spread of COVID-19. An increased number of digital services have emerged in order to tackle this pandemic. Conversational AI is an example found in this study that can be used to quickly get information about symptoms and help the individual in their self-assessment. Users can quickly and easily can get in touch with healthcare professionals in ways that work for the individual, who no longer has to rely solely on phone calls. In this study, a proof-of-concept is designed and evaluated together

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Jonson, Maja, and Prajwal Modani. "Diagnosing Doctors and AI : What the introduction of AI can tell us about the professional role of physicians." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448747.

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Artificial Intelligence (AI) is being implemented in various industries and shows promise within healthcare. AI has potential to perform tasks that a professional would traditionally do, and it thus looks to be a disruptive technology. Professional workers, like physicians, are likely to resist such implementations to protect the high status of their profession. As professional workers and healthcare hold significant roles in society, we found that research on how they are affected by AI is needed. This study takes place in the beginning of the introduction in Swedish hospitals. To gain insigh

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Mendes, David, M. J. Lopes, Artur Romão, and Irene Pimenta Rodrigues. "Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare." Bachelor's thesis, IGI Global, 2016. http://hdl.handle.net/10174/19286.

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The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for

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Olsson, Catrin, and Sara Ekström. "Utmaningar med AI för patientcentrerad och jämlik vård : En studie om varför hälso- och sjukvården möter utmaningar vid implementering av AI i syfte att stödja patientcentrerad och jämlik vård." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44280.

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Efter en kartläggning som Socialstyrelsen utfört över hälso- och sjukvårdens nyttjande avArtificiell Intelligens (AI), har det konstaterats att det finns utrymme och potential för ett mereffektivt användande av tekniken. Men trots att potential och fördelar med AI presenteras,möter branschen motgångar och utmaningar vid införandet av tekniken. I rapportenpresenteras några av de utmaningar som kan uppstå vid arbetet med att införa AI-teknik inomhälso- och sjukvården samt varför de förväntade effekterna inte uppnås. Studien har utgåttfrån en kvalitativ forskningsansats i syfte att genomföra empi

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Jonsson, Hanna, and Luyolo Mazomba. "Revenue Generation in Data-driven Healthcare : An exploratory study of how big data solutions can be integrated into the Swedish healthcare system." Thesis, Umeå universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161384.

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Abstract The purpose of this study is to investigate how big data solutions in the Swedish healthcare system can generate a revenue. As technology continues to evolve, the use of big data is beginning to transform processes in many different industries, making them more efficient and effective. The opportunities presented by big data have been researched to a large extent in commercial fields, however, research in the use of big data in healthcare is scarce and this is particularly true in the case of Sweden. Furthermore, there is a lack in research that explores the interface between big data

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Aldin, Andreas, and Victor Larsson. "Attityder, byråkrati och meningsskapande i samband med AI i sjukvården : En kvalitativ undersökning." Thesis, Uppsala universitet, Institutionen för informatik och media, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446400.

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This thesis explores attitudes towards Artificial Intelligence within the Swedish Public healthcare system. The goal of the study was to attain a deeper understanding of how hospitals and healthcare officials perceive and think about the technology and what it means to them in their professional life. Our primary data was collected from ten interviews conducted with decision-makers at Karolinska University Hospital in Stockholm and Akademiska University Hospital in Uppsala. The material was analysed through a discourse analysis, through which four themes were identified, each of which describe

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Książki na temat "Healthcare AI"

Shaban-Nejad, Arash, Martin Michalowski, and Simone Bianco, eds. Multimodal AI in Healthcare. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-14771-5.

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Raval, Mehul S., Mohendra Roy, Tolga Kaya, and Rupal Kapdi. Explainable AI in Healthcare. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003333425.

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Vyas, Sonali, Akanksha Upadhyaya, Deepshikha Bhargava, and Vinod Kumar Shukla. Edge-AI in Healthcare. CRC Press, 2023. http://dx.doi.org/10.1201/9781003244592.

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Adirim, Terry, ed. Digital Health, AI and Generative AI in Healthcare. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83526-1.

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Suri, Abhinav. Practical AI for Healthcare Professionals. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7780-5.

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Rai, Bipin Kumar, Gautam Kumar, and Vipin Balyan, eds. AI and Blockchain in Healthcare. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0377-1.

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Panesar, Arjun. Machine Learning and AI for Healthcare. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3799-1.

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Shaban-Nejad, Arash, Martin Michalowski, and David L. Buckeridge, eds. Explainable AI in Healthcare and Medicine. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53352-6.

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Panesar, Arjun. Machine Learning and AI for Healthcare. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6537-6.

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Jain, Mahima, Sanjay Dhir, and Shuchi Sinha. AI Adoption in Healthcare Industry 4.0. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4667-8.

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Części książek na temat "Healthcare AI"

Farhan, Maruf. "Empowering Healthcare." In Blockchain and AI. CRC Press, 2024. http://dx.doi.org/10.1201/9781003162018-2.

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Singh, Bhupinder, and Christian Kaunert. "AI in Healthcare." In AI for Humanitarianism. CRC Press, 2025. https://doi.org/10.1201/9781003479109-7.

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Ghatge, Dipali, and K. Rajeswari. "AI-Based Healthcare." In Applied Artificial Intelligence. CRC Press, 2023. http://dx.doi.org/10.1201/9781003324430-3.

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Narejo, Ghous Bakhsh. "AI in Healthcare." In Privacy Vulnerabilities and Data Security Challenges in the IoT. CRC Press, 2020. http://dx.doi.org/10.1201/9780429322969-5.

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Singh, Ashay, and Ankur Singh Bist. "AI and Healthcare." In Data Driven Decision Making Using Analytics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003199403-9.

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Tan, Yee-Fan, and Pei-Sze Tan. "AI for Healthcare." In The Smart Life Revolution. CRC Press, 2025. https://doi.org/10.1201/9781003509196-7.

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Beger, Jan. "AI in Healthcare." In Innovations in Healthcare and Outcome Measurement. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77302-0_4.

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Sakly, Houneida, Ramzi Guetari, Naoufel Kraiem, and Mourad Said. "AI in Healthcare." In Scalable Artificial Intelligence for Healthcare. CRC Press, 2025. https://doi.org/10.1201/9781003480594-1.

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Singh, Harjit, Manita Arora, and Avneet Singh. "AI in Healthcare." In Revolutionizing Healthcare Services. CRC Press, 2025. https://doi.org/10.1201/9781003531272-13.

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Lawry, Tom. "Adopting AI Requires Adapting." In Hacking Healthcare. Productivity Press, 2022. http://dx.doi.org/10.4324/9781003286103-17.

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Streszczenia konferencji na temat "Healthcare AI"

Apriyanto, Noor Pratama, Nurwahyu Alamsyah, Oki Wijaya, Erika Loniza, and Yekti Satriyandari. "Roles of AI in Healthcare." In 2024 International Conference on Information Technology and Computing (ICITCOM). IEEE, 2024. https://doi.org/10.1109/icitcom62788.2024.10762060.

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Barr, Joseph R., Fabio Persia, Daniela D'Auria, and Jon C. Haass. "Toward Personalizing Healthcare With AI." In 2024 Conference on AI, Science, Engineering, and Technology (AIxSET). IEEE, 2024. https://doi.org/10.1109/aixset62544.2024.00036.

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Shilaskar, Swati, Prathamesh Somkuwar, Vrushaket Shivpuje, and Shravani Mhetre. "WellnessWizz – Conversation AI for Healthcare." In 2024 International Conference on Computer, Electronics, Electrical Engineering & their Applications (IC2E3). IEEE, 2024. https://doi.org/10.1109/ic2e362166.2024.10826860.

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Lal, Mily, S. Neduncheliyan, Akanksha Goel, Poi Tamrakar, Saurabh Saoji, and Manisha Bhende. "Healthcare Interaction Through Conversational AI." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859494.

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Pagar, Atharva. "CureCall: AI-Powered Healthcare Chatbot." In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2025. https://doi.org/10.1109/icssas66150.2025.11081097.

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Rathore, Sushmit Singh, Rohit Ghosh, Sayantam Giri, et al. "Integrating Intelligent Chatbots in Healthcare: AI Based Healthcare Chatbot." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10990617.

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Pal, Sachin, Tanushka, Kanishka Jha, and Deepika Sharma. "Blockchain-Enhanced AI Diagnostics in Healthcare." In 2024 International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN). IEEE, 2024. https://doi.org/10.1109/emergin63207.2024.10961743.

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Mamalakis, Michail, Héloïse de Vareilles, Graham K. Murray, Pietro Lio, and John Suckling. "The Explanation Necessity for Healthcare AI." In 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion). IEEE, 2025. https://doi.org/10.1109/citrexcompanion65208.2025.10981502.

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Rao, B. Devananda, Gadde Madhukar, Bankula Nithin Reddy, Sriyan Kumar Voni, and N. Snehith Venkata Kumar. "Comprehensive AI-Powered Healthcare Management System." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984436.

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Barr, Joseph R., Fabio Persia, Daniela D'Auria, and Jon C. Haass. "Personalizing Healthcare with Data & AI." In 2025 19th International Conference on Semantic Computing (ICSC). IEEE, 2025. https://doi.org/10.1109/icsc64641.2025.00047.

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Raporty organizacyjne na temat "Healthcare AI"

Smeaton, John, and Lorna Christie. AI and healthcare. Parliamentary Office of Science and Technology, 2021. http://dx.doi.org/10.58248/pn637.

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There are various applications of Artificial Intelligence (AI) in healthcare, such as helping clinicians to make decisions, monitoring patient health, and automating routine administrative tasks. This POSTnote gives an overview of these uses, and their potential impacts on the cost and quality of healthcare, and on the workforce. It summarises the challenges to wider adoption of AI in healthcare, including those relating to safety, privacy, data-sharing, trust, accountability and health inequalities. It also outlines some of the regulations relevant to AI, and how these may change. As healthca

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Fathana, Rezka. A roadmap for Indonesia’s AI-driven healthcare. East Asian Bureau of Economic Research, 2023. http://dx.doi.org/10.59425/eabc.1691488850.

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Fathana, Rezka. A roadmap for Indonesia’s AI-driven healthcare. East Asian Bureau of Economic Research, 2023. http://dx.doi.org/10.59425/eabc.1691575250.

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Gardiner, Hannah, and Natasha Mutebi. AI and Mental Healthcare - opportunities and delivery considerations. Parliamentary Office of Science and Technology, 2025. https://doi.org/10.58248/pn737.

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Gardiner, Hannah, and Natasha Mutebi. AI and Mental Healthcare - ethical and regulatory considerations. Parliamentary Office of Science and Technology, 2025. https://doi.org/10.58248/pn738.

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Haupt, Sue, Bronwyn Graham, and Jane Hirst. Can AI fight sex and gender bias in healthcare? Edited by Grace Jennings-Edquist. Monash University, 2024. http://dx.doi.org/10.54377/6079-ad33.

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes

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Strauss, Ilan, Isobel Moure, Tim O’Reilly, and Sruly Rosenblat. The State of AI Governance Research: AI Safety and Reliability in Real World Commercial Deployment. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4112.d.2025.

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Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (Jan- uary 2020 - March 2025), we compare research outputs of leading AI companies (An- thropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that cor- porate AI research increasingly concentrates on pre-deployment areas — model align- ment and testing & evaluation — while attention to deployment-stage issues, such as model bias, has waned, as commercial imperatives and existential risks have come into focus. We fi

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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-

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Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new real

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