Impact of artificial intelligence on healthcare
DOI:
https://doi.org/10.18203/2349-3933.ijam20232839Keywords:
Artificial intelligence, Psychiatry, Cancer research, Cardiology, Dermatology, Ophthalmology, Surgery, GastroenterologyAbstract
Artificial intelligence (AI) is revolutionizing various medical practices, making them more affordable, efficient, and faster. Its uses range from diagnosis, management, monitoring, and outcome forecasting to individualized care. AI technology in psychotherapy can help conditions such as dementia, autism spectrum disorder, and schizophrenia, and due to its image processing, segmentation, and reconstruction capabilities, AI has found applications in a wide range of fields, including the diagnosis of cancer, the treatment of skin lesions, the prediction of metastasis of malignancies, the staging of lung nodules, the identification of COVID-19, and the classification of thyroid tissue. In addition to histopathology images, imaging techniques such as CT, MRI, mammography, fundus imaging, and even photographs can be used to diagnose patients. In this study, we tried to address the current status and future scope of AI to bring substantial upliftment to health care. It is anticipated that human intelligence and AI will coexist in the field of medicine in the future. Modern smart devices collect a huge amount of data that can be used for disease prevention, health promotion, monitoring, and diagnosis in medicine. AI will improve as long as we train them. With the development of sophisticated machinery, robotics, and virtual reality, the healthcare industry is likely to undergo revolutionary changes. AI has performance on par with that of human experts, with the added benefits of scalability and automation. Before becoming fully autonomous in nature, AI systems might need tight supervision due to their lack of training, limited knowledge, and limited flexibility in clinical settings.
References
Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021;11(4):900-915.
Ting DSW, Cheung CY, Lim G, Gavin SWT, Nguyen D, Alfred G et al. 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.
Gulshan V, Peng L, Coram M, Stumpe MC, Derek W, Arunachalam N et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316(22):2402-10.
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-10.
Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-75.
Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668-79.
Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A et al. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017;88(6):581-6.
Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5):e253-61.
Abbasgholizadeh Rahimi S, Légaré F, Sharma G, Archambault P, Zomahoun HTV, Chandavong S et al. Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal. J Med Internet Res. 2021;23(9):e29839.
Bower P, Gilbody S. Stepped care in psychological therapies: access, effectiveness and efficiency: Narrative literature review. Br J Psychiatr. 2005;186(1):11-7.
Antonucci LA, Raio A, Pergola G, Gelao B, Papalino M, Rampino A et al. Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition. BMC Psychol. 2021;9(1):47.
Luxton DD. Recommendations for the ethical use and design of artificial intelligent care providers. Artif Intell. Med. 2014;62(1):1-10.
Shatte A, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol. Med. 2019;49(9):1426-48.
Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn. Neurodyn. 2021;15(2):239-52.
Sachan D. Self-help robots drive blues away. Lancet Psychiatry. 2018;5(7):547.
Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment. Health. 2017;4(2)
Bain EE, Shafner L, Walling DP, Othman AA, Chuang-Stein C, Hinkle J et al. Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia. JMIR mHealth uHealth. 2017;5(2):22.
Du Sert OP, Potvin S, Lipp O, Dellazizzo L, Laurelli M, Breton R et al. Virtual reality therapy for refractory auditory verbal hallucinations in schizophrenia: a pilot clinical trial. Schizophr. Res. 2018;197:176-81.
Pennisi P, Tonacci A, Tartarisco G, Billeci L, Ruta L, Gangemi S et al. Autism and social robotics: a systematic review. Autism Res Off J Int Soc Autism Res. 2016;9(2):165-83.
Wada K, Shibata T. Living with seal robots-its sociopsychological and physiological influences on the elderly at a care house. IEEE Trans. Robot. 2007;23(5):972-80.
Tielman ML, Neerincx MA, Van Meggelen M, Franken I, Brinkman WP. How should a virtual agent present psychoeducation? Influence of verbal and textual presentation on adherence. Technol. Health Care Off J Eur Soc Eng Med. 2017;25(6):1081-96.
Prochaska JJ, Vogel EA, Chieng A, Kendra M, Baiocchi M, Pajarito S et al. A therapeutic relational agent for reducing problematic substance use (Woebot): development and usability study. J Med Internet Res. 2021;23(3)
Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, Bertrand D, et al. Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell 2018;173:305-20.
Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D et al.Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Mef. 2018;24:1559-67.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115-18.
Jiang Y, Liang X, Wang W, Chen C, Yuan Q, Zhang X et al. Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning. JAMA Netw Open. 2021;4:e2032269.
Wang X, Yang W, Weinreb J, Han J, Li Q, Kong X et al. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep 2017;7:15415.
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;577:89-94.
Zhou Q, Zhou Z, Chen C, Fan G, Chen G, Heng H et al. Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images. Comput Biol Med. 2019;107:47-57.
Gayvert KM, Madhukar NS, Elemento O. A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials. Cell Chem Biol. 2016;23(10):1294-301.
Cao CL, Li QL, Tong J, Shi LN, Li WX, Xu Y et al. Artificial intelligence in thyroid ultrasound. Front Oncol. 2023;13:1060702.
Baruah D, Runge L, Jones RH, Collins HR, Kabakus IM, McBee MP. COVID-19 Diagnosis on Chest Radiograph Using Artificial Intelligence. Cureus. 2022;14(11):e31897.
Chiu HY, Chao HS, Chen YM. Application of Artificial Intelligence in Lung Cancer. Cancers (Basel). 2022;14(6):1370.
Afifi A, Toshiya N. Unsupervised detection of liver lesions in CT images. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015;2411-4.
Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018;20:65-71.
Jamart K, Xiong Z, Maso Talou GD, Stiles MK, Zhao J. Mini review: deep learning for atrial segmentation from late gadolinium-enhanced MRIs. Front Cardiovasc Med. 2020;7:86.
Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25:65-9.
Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019;381:1909-17.
Rogers AJ, Selvalingam A, Alhusseini MI, Krummen DE, Corrado C, Abuzaid F et al. Machine learned cellular phenotypes in cardiomyopathy predict sudden death. Circ Res. 2021;128:172-84.
Levy AE, Biswas M, Weber R, Tarakji K, Chung M, Noseworthy Paet al. Applications of machine learning in decision analysis for dose management for dofetilide. PLoS One. 2019;14:e0227324.
Shade JK, Ali RL, Basile D, Popescu D, Akhtar T, Marine J et al. Preprocedure application of machine learning and mechanistic simulations predicts likelihood of paroxysmal atrial fibrillation recurrence following pulmonary vein isolation. Circ Arrhythm Electrophysiol. 2020;13:e008213.
Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57:5200-6.
Ting DSW, Cheung CYL, Lim G, Tan GSW, Quang ND, Gan A et al. 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;318:2211-23.
Wu X, Huang Y, Liu Z, Lai W, Long E, Zhang K et al. Universal artificial intelligence platform for collaborative management of cataracts. Br J Ophthalmol. 2019;103:1553-60.
Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 2018;136:803-10.
Ohsugi H, Tabuchi H, Enno H, Ishitobi N. Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment. Sci Rep. 2017;7:9425.
Masood S, Fang R, Li P, Li H, Sheng B, Mathavan A et al. Automatic choroid layer segmentation from optical coherence tomography images using deep learning. Sci Rep. 2019;9:3058.
Liu TYA, Correa ZM. Deep learning applications in ocular oncology. In: Grzybowski A, editor. Artificial intelligence in ophthalmology. Cham: Springer. 2021;235-8.
Van der Heijden AA, Abramoff MD, Verbraak F, van Hecke MV, Liem A, Nijpels G. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol. 2018;96:63-8.
Babenko B, Mitani A, Traynis I, Kitade N, Singh P, Maa AY et al. Detection of signs of disease in external photographs of the eyes via deep learning. Nat Biomed Eng. 2022;1-14.
Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57:5200-6.
Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K et al. Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep. 2019;9:10750.
Li F, Wang Y, Xu T, Dong L, Yan L, Jiang M, et al. Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye (Lond). 2021;36:1433-41.
Ryu G, Lee K, Park D, Park SH, Sagong M. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography. Sci Rep. 2021;11:23024.
Christopher M, Belghith A, Bowd C, Proudfoot JA, Goldbaum MH, Weinreb RN et al. Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs. Sci Rep. 2018;8:1-13.
Yoon J, Han J, Park JI, Hwang JS, Han JM, Sohn J et al. Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy. Sci Rep. 2020;10:18852.
Cascinelli N, Ferrario M, Tonelli T, Leo E. A possible new tool for clinical diagnosis of melanoma: The computer. J Am Acad Dermatol. 1987;16:361-7.
Sennaar K. Machine Learning for Dermatology-5 Current Applications. 2019. Available at: http://emerj.com/ai-sector-overview/machine-learning-dermatology-applications. Accessed on 25 June, 2023.
Thissen M, Udrea A, Hacking M, Von Braunmuehi T, Ruzicka T. mHealth app for risk assessment of pigmented and non pigmented skin lesions-A study on sensitivity and specificity in detecting malignancy. Telemed J E Health. 2017;23:948-54.
Correa da Rosa J, Kim J, Tian S, Tomalin LE, Krueger JG, SuárezFariñas M. Shrinking the psoriasis assessment gap: Early gene-expression profiling accurately predicts response to long-term treatment. J Invest Dermatol. 2017;137:305-12.
Übeylı ED, Güler I. Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Comput Biol Med. 2005;35:421-33.
De Guzman LC, Maglaque RP, Torres VM, Zapido SP, Cordel MO. Design and evaluation of a multi-model, multi-level artificial neural network for eczema skin lesion detection In: 2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS). 2015;42-7.
Min S, Kong HJ, Yoon C, Kim HC, Suh DH. Development and evaluation of an automatic acne lesion detection program using digital image processing. Skin Res Technol. 2013;19:e423-32.
Stafford IS, Kellermann M, Mossotto E, Beattie RM, MacArthur BD, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digital Med. 2020;3:30.
Nayak R, Kumar P, Galigekere RR. Towards a comprehensive assessment of wound-composition using color-image processing. In Proceedings of the IEEE International Conference on Image Processing (ICIP '09). 2009;4185-8.
Manohar Dhane D, Maity M, Mungle T, Bar C, Achar A, Kolekar M et al. Fuzzy spectral clustering for automated delineation of chronic wound region using digital images. Comput Biol Med. 2017;89:551-60.
Alderden J, Pepper GA, Wilson A, Whitney JD, Richardson S, Butcher R et al. Predicting pressure injury in critical care patients: A machine-learning model. Am J Crit Care. 2018;27:461-8.
Lim HW, Park S, Noh S, Lee DH, Yoon C, Koh W et al. A study on the development of a robot-assisted automatic laser hair removal system. Photomed Laser Surg. 2014;32:633-41.
Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C et al. Deep learning outperformed 136 of 157 dermatologist in a head to head dermoscopic melanoma image classification task. Eur J Cancer. 2019;113:47-54.
Deo RC. Machine learning in medicine. Circulation. 2015;132:1920-30.
Chang M, Canseco JA, Nicholson KJ, Patel N, Vaccaro AR. The Role of Machine Learning in Spine Surgery: The Future Is Now. Front Surg. 2020;7:54.
Panesar S, Cagle Y, Chander D, Morey J, Fernandez-Miranda J, Kliot M. Artificial Intelligence and the Future of Surgical Robotics. Ann Surg. 2019;270:223-6.
Rasouli JJ, Shao J, Neifert S, Gibbs WN, Habboub G, Steinmetz MP et al. Artificial Intelligence and Robotics in Spine Surgery. Glob Spine J. 2021;11:556-64.
Panesar SS, Ashkan K. Surgery in space. Br J Surg. 2018;105:1234-43.
Klang E, Barash Y, Margalit RY, Soffer S, Shimon O, Albshesh A et al. Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy. Gastrointest Endosc. 2020;91:606-13.e2.
Barash Y, Azaria L, Soffer S, Margalit Yehuda R, Shlomi O, Ben-Horin S et al. Ulcer severity grading in video capsule images of patients with Crohn’s disease: An ordinal neural network solution. Gastrointest Endosc. 2021;93:187-92.
Stidham RW, Liu W, Bishu S, Michael DR, Peter DRH, Ji Z et al. Performance of a Deep Learning Model vs Human Reviewers in Grading Endoscopic Disease Severity of Patients With Ulcerative Colitis. JAMA Netw Open. 2019;2(5):e193963.
Syed S, Lubaina E, Aman S, Saurav S, Marium K, Kamran K et al. Artificial Intelligence-Based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection. J Pediatr Gastroenterol Nutr. 2021;72(6):833-41.
Lamash Y, Kurugol S, Freiman M, Jeannette MP-R, Michael JC, Athos B et al. Curved planar reformatting and convolutional neural network-based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI J Magn Reson Imaging. 2019;49:1565-76.
Giger ML. Machine learning in medical imaging. J Am Coll Radiol. 2018;15:512-20.
Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern. Med. 2018;48:e13-4.
Doraiswamy PM, Blease C, Bodner K. Artificial intelligence and the future of psychiatry: insights from a global physician survey. Artif Intell Med. 2020;102.
Coppola F, Faggioni L, Regge D, Giovagnoni A, Golfieri R, Bibbolino C et al. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey. La Radiologia Medica. 2021;126(1):63-71.
QSarwar S, Dent A, Faust K, Richer M, Djuric U, Van Ommeren R et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med. 2019;2(1):1-7.
Staartjes VE, Volokitin A, Regli L, Konukoglu E, Serra C. Machine vision for real-time intraoperative anatomic guidance: a proof-of-concept study in endoscopic pituitary surgery. Operat Neurosurg. 2021;21(4):242-7.