Abstract
Computational intelligence or machine intelligence is generally described as the notion of automated and intelligent machines supporting or replacing human labour. AI has attracted growing research interest and has become increasingly adopted in modelling and solving real-world problems. AI in medicine has attracted increasing attention with significant potential for its adoption, particularly as the gap between increasing expectations of high-quality healthcare, and natural limitations of human physicians in mastering increasingly complex domain knowledge grows. With the assistance of AI, the organisation, retrieval and utilisation of appropriate medical knowledge needed by the practitioner in dealing with complex cases may become much easier. AI may provide appropriate diagnostic, prognostic and therapeutic decisions, and meet requirements for the emerging 4P principles of medicine: predictive, preventive, personalised, and participatory.
AI is likely to improve physician efficiency or accuracy, and clinicians will be essential to provide AI with the expert domain knowledge and data necessary for AI training. Further development and deployment of these technologies should also consider acceptance and satisfaction of patients.
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References
Mintz Y, Brodie R (2019) Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 28:73–81
McCarthy J, Minsky M, Rochester N et al (2006) A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955 [J]. AI Magazine 27(4):12–14. https://doi.org/10.1609/aimag.v27i4.1904
Schwartz WB (1970) Medicine and the computer: the promise and problems of change. Use and Impact of Computers in Clinical Medicine. Springer, New York, pp 321–335
Alonso SG, de la Torre DÃez I, ZapiraÃn BG (2019) Predictive, personalized, preventive and participatory (4P) medicine applied to telemedicine and eHealth in the literature. J Med Syst 43.5:140
Lindsay RK, Buchanan BG, Feigenbaum EA, Lederberg J (1980) Applications of artificial intelligence for organic chemistry: the DENDRAL Project. McGraw-Hill, New York
Freiherr G (1980) The seeds of artificial intelligence: SUMEX-AIM (1980). U.S. G.P.O; DHEW publication no. (NIH) 80–2071. U.S. Dept. of Health, Education, and Welfare, Public Health Service, National Institutes of Health, Washington, D.C
Miller RA, Pople HE, Myers JD (1982) Internist-1: an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 307(8):468–476
Weiss SM, Kulikowski CA, Amarel S, Safir A (1978) A model-based method for computer-aided medical decision making. Artif Intell 11:145–172
Shortliffe EH (1976) Computer-based medical consultations: MYCIN. Elsevier, New York
History of artificial intelligence. http://en.wikipedia.org/wiki/History_of_artificial_intelligence. Accessed 1 June 2008
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks (PDF). Communications of the ACM 60(6):84–90. https://doi.org/10.1145/3065386. ISSN 0001-0782
Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69:S36–S40
Alpaydin E (2020) Introduction to machine learning. MIT Press
Theofilatos K et al (2015) Predicting protein complexes from weighted protein–protein interaction graphs with a novel unsupervised methodology: evolutionary enhanced Markov clustering. Artif Intell Med 63(3):181–189
Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT press
Silverman BG et al (2015) A systems approach to healthcare: agent-based modeling, community mental health, and population well-being. Artif Intell Med 63(2):61–71
Vallor S (2011) Carebots and caregivers: sustaining the ethical ideal of care in the twenty-first century. Philos Technol 24(3):251–268
Larson JA, Johnson MH, Bhayani SB (2014) Application of surgical safety standards to robotic surgery: five principles of ethics for nonmaleficence. J Am Coll Surg 218(2):290–293
Sung GT, Gill IS (2001) Robotic laparoscopic surgery: a comparison of the da Vinci and Zeus systems. Urology 58(6):893–898
Knight BA et al (2015) Comparing expert reported outcomes to national surgical quality improvement program risk calculator-predicted outcomes: do reporting standards differ? J Endourol 29(9):1091–1099
Esteva A et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542.7639:115–118
Teare P et al (2017) Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J Digit Imaging 30(4):499–505
Bar A et al (2017) Compression fractures detection on CT. Medical Imaging 2017: Computer-Aided Diagnosis. Vol. 10134. International Society for Optics and Photonics
Li R et al (2014) Deep learning based imaging data completion for improved brain disease diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham
Laukamp KR et al (2019) Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI. Eur Radiol 29(1):124–132
Rosenkrantz AB, Hughes DR, Duszak R Jr (2016) The US radiologist workforce: an analysis of temporal and geographic variation by using large national datasets. Radiology 279(1):175–184
Aerts HJWL et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5(1):1–9
Wu W et al (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 6:71
Huynh E et al (2017) Associations of radiomic data extracted from static and respiratory-gated CT scans with disease recurrence in lung cancer patients treated with SBRT. PloS one 12.1:e0169172
Parmar C et al (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087
O’Connor JPB et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14(3):169–186
MaxQ-AI. Available from: https://maxq.ai
DA permits marketing of clinical decision support software for alerting providers of a potential stroke in patients [press release]. United States Food and Drug Administration, February 2018
Jha S, Topol EJ (2016) Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316(22):2353–2354
https://www.who.int/news-room/fact-sheets/detail/cancer. 12 Sept 2018
National Lung Screening Trial Research Team (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365(5):395–409
Ardila D et al (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25(6):954–961
Coudray N et al (2018) Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med 24(10):1559–1567
Lehman CD et al (2017) National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology 283(1):49–58
McKinney SM et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577(7788):89–94
Bejnordi BE et al (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22):2199–2210
Rogers HW et al (2015) Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US population, 2012. JAMA Dermatol 151(10):1081–1086
Stern RS (2010) Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol 146(3):279–282
Esteva A et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542.7639:115–118
Wang P et al (2018) Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng 2(10):741–748
Yamauchi A et al (2018) Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 1:3
Kanesaka T et al (2018) Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging. Gastrointest Endosc 87(5):1339–1344
Cuocolo R et al (2019) Current applications of big data and machine learning in cardiology. J Geriatr Cardiol 16.8:601
Seah JCY et al (2019) Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology 290(2):514–522
Madani A et al (2018) Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med 1(1):1–8
Cano-Espinosa C et al (2018) Automated Agatston score computation in non-ECG gated CT scans using deep learning. Medical Imaging 2018: Image Processing. Vol. 10574. International Society for Optics and Photonics
Ngo TA, Lu Z, Carneiro G (2017) Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal 35:159–171
Kwon J-m et al (2018) An algorithm based on deep learning for predicting in-hospital cardiac arrest. J Am Heart Assoc 7.13:e008678
Alaa AM et al (2019) Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PloS One 14.5:e0213653
Gulshan V et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410
Lee CS, Baughman DM, Lee AY (2017) Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmol Retina 1(4):322–327
Goudra BG, Singh PM, Chandrasekhara V (2014) SEDASYS®, airway, oxygenation, and ventilation: anticipating and managing the challenges. Dig Dis Sci 59(5):920–927
Soroush H, Arney D, Goldman J (2016) Toward a safe and secure medical Internet of Things. IIC J Innov 2(1):4–18
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Liu, C., Tan, Z., He, M. (2022). Overview of Artificial Intelligence in Medicine. In: Raz, M., Nguyen, T.C., Loh, E. (eds) Artificial Intelligence in Medicine. Springer, Singapore. https://doi.org/10.1007/978-981-19-1223-8_2
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DOI: https://doi.org/10.1007/978-981-19-1223-8_2
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