Integrating Artificial Intelligence with Revolutionization of Modern Therapies and Innovations
DOI:
https://doi.org/10.31580/pjmls.v7i2.3077Keywords:
Artificial Intelligence, Clinical outcomes, Diagnostics, Healthcare innovations, Medical therapies, Robotics, Surgical interventions, RehabilitationAbstract
Background: The integration of artificial intelligence (AI) and robotics in healthcare is transforming medical therapies and innovations. These technologies are enhancing diagnostic accuracy, surgical precision, and rehabilitative care, thereby addressing significant challenges in modern medicine. Notable advancements include AI algorithms for disease detection, robotic surgical systems, and AI-driven rehabilitation devices.
Objective: This study aims to explore the roles of AI and robotics in health therapies and innovations, focusing on current applications, benefits, challenges, and future prospects. The aim is to offer a qualitative review of these technologies and to outline the patterns of their application in health care to reveal the potential trends for further research and development.
Methodology: Thus, a comprehensive review of the literature from 2019 to 2024 was conducted using PRISMA guidelines involving both peer-reviewed journals conference proceedings and industry reports. A digital search was conducted on Pubmed, IEEE Explore and Google Scholar. The review examined the roles of AI and robotic devices in diagnosis, operation, and therapy; the assessment of the development in technology and evaluation of clinical effectiveness, as well as the consideration of the issues in putting the application into practice.
Results: Some examples of such tools are Google’s DeepMind and IBM’s Watson; these have enhanced the rates of diagnostic precision and speed. For example, AI algorithms can diagnose diabetic retinopathy and early-stage cancers better than the conventional diagnosis system. Robotic surgical technologies that include the Da Vinci Surgical System and Medtronic’s Mazor X have improved the level of dexterity in the operating room and decreased recovery period as well as complexities. These systems are most useful in procedures that involve minor invasions into the patient’s body. Exoskeletons for rehabilitation have been transformed through the integration of AI like EksoGT while robotics of prosthetics including Ottobock’s C-Brace have undergone a positive change. These devices include personal therapy programs and expand pt mobility and quality of life; causing vast improvement in stroke or spinal cord injury patients.
Conclusion: AI and robotics are critical in enhancing health therapies and innovations mainly because they have considerable impact on diagnostics, on operations, and on rehabilitation processes. Despite these difficulties, there is evidence that research and development will continue to progress and subsequently, help increase the spread of these approaches with further benefits to healthcare.
References
Topol E. Deep medicine: how artificial intelligence can make healthcare human again: Hachette UK; 2019.
Yousefirizi F, Decazes P, Amyar A, Ruan S, Saboury B, Rahmim AJPc. AI-based detection, classification and prediction/prognosis in medical imaging: towards radiophenomics. PET Clin.2022;17(1):183-212.
Kim CW, Kim CH, Baik SHJJoGS. Outcomes of robotic-assisted colorectal surgery compared with laparoscopic and open surgery: a systematic review. J Gastrointest Surg. 2014;18(4):816-30.
Chen B, Ma H, Qin L-Y, Gao F, Chan K-M, Law S-W. Recent developments and challenges of lower extremity exoskeletons. J Orthop Translat. 2016;5:26-37.
Reinkensmeyer DJ, Emken JL, Cramer SCJARBE. Robotics, motor learning, and neurologic recovery. Annu Rev Biomed Eng. 2004;6(1):497-525.
Davenport T, Kalakota RJFhj. The potential for artificial intelligence in healthcare. Future healthcare journal, 2019;6(2):94-8.
Morley J, Machado CC, Burr C, Cowls J, Joshi I, Taddeo M. The ethics of AI in health care: a mapping review. Soc Sci Med. 2020;260:113172.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.
Kruse CS, Marquez G, Nelson D, Palomares OJAci. The use of health information exchange to augment patient handoff in long-term care: a systematic review. Appl Clin Inform. 2018;9(04):752-71.
Reddy S, Fox J, Purohit MPJJotRSoM. Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019;112(1):22-8.
Hamet P, Tremblay JJm. Artificial intelligence in medicine. Metabolism. 2017;69:S36-S40.
Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200-6.
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-10.
Song AH, Jaume G, Williamson DF, Lu MY, Vaidya A, Miller TR, et al. Artificial intelligence for digital and computational pathology. Nature Reviews Bioengineering2023;1(12):930-49.
Sellke N, Gordon C, Lawandy I, Gorvitovskaia AY, Scrimgeour LA, Fingleton JG, et al. Impaired coronary contraction to phenylephrine after cardioplegic arrest in diabetic patients. J Surg Res. 2018;230:80-6.
Mahmud E, Naghi J, Ang L, Harrison J, Behnamfar O, Pourdjabbar A. Demonstration of the safety and feasibility of robotically assisted percutaneous coronary intervention in complex coronary lesions: results of the CORA-PCI Study (Complex Robotically Assisted Percutaneous Coronary Intervention). JACC Cardiovasc Interv.2017;10(13):1320-7.
Chen G, Chan CK, Guo Z, Yu HJCRiBE. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Crit Rev Biomed Eng.2013;41(4-5).
Esquenazi A, Talaty M, Packel A, Saulino MJAjopm, rehabilitation. The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil.2012;91(11):911-21.
Shen D, Wu G, Suk H-IJArobe. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19(1):221-48.
Adler ED, Voors AA, Klein L, Macheret F, Braun OO, Urey MA. Improving risk prediction in heart failure using machine learning. Eur J Heart Fail. 2020;22(1):139-47.
Lang L, Perez AJM. Binding ensembles of p53-MDM2 peptide inhibitors by combining bayesian inference and atomistic simulations. Molecules.2021;26(1):198.
Barra VJJoIM, Learning Ti, Teaching. Convergence of man and machine artificial intelligence in human life. Human Behavior and Emerging Technologies. 2023;3(4).
Miotto R, Wang F, Wang S, Jiang X, Dudley JTJBib. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform.2018;19(6):1236-46.
Beam AL, Kohane ISJJ. Big data and machine learning in health care. JAMA. 2018;319(13):1317-8.
Mirbabaie M, Stieglitz S, Frick NRJH, Technology. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health and Technology 2021;11(4):693-731.
Salim M, Wåhlin E, Dembrower K, Azavedo E, Foukakis T, Liu Y, et al. External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA Oncol.2020;6(10):1581-8.
Saghiri MA, Vakhnovetsky J, Nadershahi NJJoDE. Scoping review of artificial intelligence and immersive digital tools in dental education. J Dent Educ.2022;86(6):736-50.
Shah N, Arshad A, Mazer MB, Carroll CL, Shein SL, Remy KEJPr. The use of machine learning and artificial intelligence within pediatric critical care. Pediatr Res. 2023;93(2):405-12.
Narin A, Kaya C, Pamuk ZJPA, Applications. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Anal Appl. 2021;24:1207-20.
Rajpurkar P, Chen E, Banerjee O, Topol EJJNm. AI in health and medicine. Nat Med. 2022;28(1):31-8.
Yuan M, Yin W, Tao Z, Tan W, Hu YJPo. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PloS one,2020;15(3):e0230548.
Winkler JK, Blum A, Kommoss K, Enk A, Toberer F, Rosenberger A, et al. Assessment of diagnostic performance of dermatologists cooperating with a convolutional neural network in a prospective clinical study: human with machine. JAMA Dermatol.2023;159(6):621-7.
Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, et al. AI in medical imaging informatics: current challenges and future directions. IEEE journal of biomedical and health informatics, 2020;24(7):1837-57.
Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122-31. e9.
Park HJ, Shin Y, Park J, Kim H, Lee IS, Seo D-W, et al. Development and validation of a deep learning system for segmentation of abdominal muscle and fat on computed tomography. Korean J Radiol.2020;21(1):88-100.
Zhang Y, Tiwari P, Rong L, Chen R, AlNajem NA, Hossain MSJAToMC, Communications, et al. Affective interaction: Attentive representation learning for multi-modal sentiment classification. ACM Transactions on Multimedia Computing, Communications and Applications. 2022;18(3s):1-23.
Holzinger A, Langs G, Denk H, Zatloukal K, Müller HJWIRDM, Discovery K. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;9(4):e1312.
Topol EJJNm. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature.2020;577(7788):89-94.
Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KIJES. Impact of artificial intelligence on dental education: A review and guide for curriculum update. Education Sciences. 2023;13(2):150.
Li Y, Zhang T, Yang Y, Gao YJJoIMR. Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects. The Journal of international medical research, 2020;48(9):0300060520945141.
Aboughazala LMJA-AUJoVR, Studies. Automated detection of COVID-19 coronavirus cases using deep neural networks with X-ray images. Computers in biology and medicine,2020;2(1):1-12.
Ngiam KY, Khor WJTLO. Big data and machine learning algorithms for health-care delivery. The Lancet. Oncology, 2019;20(5):e262-e73.
Wu J, Deng W, Li S, Yang XJC, Sciences ML. Advances in research on ACE2 as a receptor for 2019-nCoV Cellular and molecular life sciences. 2021;78:531-44.
Ragab H, Barberis E, Bos H, Giuffrida C, editors. Rage against the machine clear: A systematic analysis of machine clears and their implications for transient execution attacks. 30th USENIX Security Symposium (USENIX Security 21); 2021.
Chen Y-W, Jain LCJP, Applications, Springer: Berlin/Heidelberg G. Deep learning in healthcare. Intelligent Systems Reference Library. 2020.
Yadav A, Vishwakarma DKJAToMC, Communications, Applications. A deep multi-level attentive network for multimodal sentiment analysis. 2023;19(1):1-19.
Pedro AR, Dias MB, Laranjo L, Cunha AS, Cordeiro JVJPo. Artificial intelligence in medicine: A comprehensive survey of medical doctor’s perspectives in Portugal. PloS one, 2023;18(9):e0290613.
Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TAJPr. Artificial intelligence-based clinical decision support in pediatrics. Pediatric research, 2023;93(2):334-41.
Feng F, Jiang Y, Yuan M, Shen J, Yin H, Geng D, et al. Association of radiologic findings with mortality in patients with avian influenza H7N9 pneumonia. PloS one,2014;9(4):e93885.
Wang C, Ma J, Zhang S, Shao J, Wang Y, Zhou H-Y. Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases. NPJ digital medicine, 2022;5(1):124.
Ting DSW, Cheung CY-L, Lim G, Tan GSW, Quang ND, Gan A. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA, 2017;318(22):2211-23.
Alamro RA, Chisholm AE, Williams AM, Carpenter MG, Lam TJJoN, Rehabilitation. Overground walking with a robotic exoskeleton elicits trunk muscle activity in people with high-thoracic motor-complete spinal cord injury. Journal of neuroengineering and rehabilitation, 2018;15:1-11.
Dollar AM, Herr HJITor. Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art. IEEE Transactions on Robotics.2008;24(1):144-58.
Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi NJPo. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PloS one. 2017;12(4):e0174944.
Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine, 2019;25(8):1301-9.
Reddy S, Rogers W, Makinen V-P, Coiera E, Brown P, Wenzel M. Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ health & care informatics, 2021;28(1).
Holmes J, Sacchi L, Bellazzi RJARCSE. Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England,2004;86:334-8.
Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar K. Impact of Artificial Intelligence on Dental Education: A Review and Guide for Curriculum Update. Educ. Sci. 2023, 13, 150. 2023.
Tran BX, Vu GT, Ha GH, Vuong Q-H, Ho M-T, Vuong T-T. Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. Journal of clinical medicine, 2019;8(3):360.
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