Application of artificial intelligence in the field of medicine and neurology_West China Medical Journal

Authors：

HUANG Huan ,  ZHAO Gang

Department of Neurology, Xijing Hospital, Air Force Medical University, Xi’an, Shaanxi 710032, P. R. China;

Corresponding author：

ZHAO Gang, Email: zhaogang@fmmu.edu.cn

Keywords：

Artificial intelligence; Expert system; Machine learning; Medicine; Neurology

DOI：

10.7507/1002-0179.201805154

Video：

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This review describes the concept of artificial intelligence, introduces the working mechanism and the main structure of medical expert system, as well as the development history of medical expert system at home and abroad and its applications in the medical field. The concept of machine learning, commonly used algorithms and its clinical applications in medical diagnosis are briefly described. It mainly introduces the application of artificial intelligence in neurology. The advantages and disadvantages of artificial intelligence system in medical field are analyzed. Finally, the future of artificial intelligence in the medical field is forecasted.

Citation： HUANG Huan, ZHAO Gang. Application of artificial intelligence in the field of medicine and neurology. West China Medical Journal, 2018, 33(6): 639-643. doi: 10.7507/1002-0179.201805154 Copy

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2.	Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med, 2018, 131(2): 129-133.
3.	Esfandiari N, Babavalian MR, Moghadam A, et al. Knowledge discovery in medicine: current issue and future trend. Expert Syst Appl, 2014, 41(9): 4434-4463.
4.	Das S, Chowdhury SR, Saha H. Accuracy enhancement in a fuzzy expert decision making system through appropriate determination of membership functions and its application in a medical diagnostic decision making system. J Med Syst, 2012, 36(3): 1607-1620.
5.	Sotos JC. MYCIN and NEOMYCIN: two approaches to generating explanations in rule-based expert systems. Aviat Space Environ Med, 1990, 61(10): 950-954.
6.	Goggin LS, Eikelboom RH, Atlas MD. Clinical decision support systems and computer-aided diagnosis in otology. Otolaryngol Head Neck Surg, 2007, 136(4 Suppl): S21-S26.
7.	Gavilan C, Gallego J, Gavilan J. ‘Carrusel’: an expert system for vestibular diagnosis. Acta Otolaryngol, 1990, 110(3/4): 161-167.
8.	Dong CL, Wang YJ, Zhang Q, et al. The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo. Comput Methods Programs Biomed, 2014, 113(1): 162-174.
9.	马逸, 陈斌. 专家系统在医学上的研究现状及进展. 中国医疗设备, 2008(6): 42-44, 78.
10.	宗相卿, 代林田. 微机应用关幼波肝病诊疗程序 196 例分析. 辽宁中医杂志, 1992(6): 26-27.
11.	易涛, 魏蛟龙. 心血管药物治疗专家系统的设计. 中国药房, 2003(7): 22-23.
12.	卢培佩. 计算机辅助职业病诊断专家系统的研究. 长沙: 中南大学, 2011.
13.	王心涛, 齐效君, 方向明, 等. 中枢神经系统 CT 影像诊断专家系统的设计与实现. 中国医学创新, 2014(34): 12-15.
14.	Salas-Gonzalez D, Gorriz JM, Ramirez J, et al. Computer-aided diagnosis of Alzheimer’s disease using support vector machines and classification trees. Phys Med Biol, 2010, 55(10): 2807-2817.
15.	Chen X, Wang ZJ, Sy C, et al. Computer-aided diagnosis expert system for cerebrovascular diseases. Neurol Res, 2014, 36(5, SI): 468-474.
16.	Kentala EL, Laurikkala JP, Viikki K, et al. Experiences of otoneurological expert system for vertigo. Scand Audiol Suppl, 2001(52): 90-91.
17.	Heckerman DE, Nathwani BN. Toward normative expert systems: Part Ⅱ. Probability-based representations for efficient knowledge acquisition and inference. Methods Inf Med, 1992, 31(2): 106-116.
18.	Oropesa I, Sanchez-Gonzalez P, Chmarra MK, et al. Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis. Surg Endosc, 2014, 28(2): 657-670.
19.	Lin RH, Chuang CL. A hybrid diagnosis model for determining the types of the liver disease. Comput Biol Med, 2010, 40(7): 665-670.
20.	Chuang CL. Case-based reasoning support for liver disease diagnosis. Artif Intell Med, 2011, 53(1): 15-23.
21.	Sterling M, Huang DT, Ghoraani B. Developing a new computer-aided clinical decision support system for prediction of successful postcardioversion patients with persistent atrial fibrillation. Comput Math Methods Med, 2015: 527815.
22.	刘光熠, 赵迎宾, Ellenius J, 等. 一种灵活的基于临床指南的临床决策支持系统. 计算机应用与软件, 2011, 28(6): 189-191, 238.
23.	杜敏, 罗建伟. 基于大数据的医院决策支持系统构建研究. 中国数字医学, 2014(12): 73-75.
24.	倪家远, 汤亚玲. 基于决策树和面向对象技术的糖尿病诊断专家系统设计. 苏州科技学院学报: 自然科学版, 2016(1): 70-74.
25.	赵凌云. 面向服务的消费者行为分析及推荐模型研究. 济南: 山东师范大学, 2014.
26.	Sidky H, Whitmer JK. Learning free energy landscapes using artificial neural networks. J Chem Phys, 2018, 148(10): 104111.
27.	Fabris F, Doherty A, Palmer D, et al. A new approach for interpreting random forest models and its application to the biology of ageing. Bioinformatics, 2018. doi: 10.1093/bioinformatics/bty087.
28.	Korhani Kangi A, Bahrampour A. Predicting the survival of gastric cancer patients using artificial and bayesian neural networks. Asian Pac J Cancer Prev, 2018, 19(2): 487-490.
29.	Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer, 1997, 79(4): 857-862.
30.	Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115.
31.	Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 2016, 316(22): 2402-2410.
32.	Acharya RU, Yu WW, Zhu KY, et al. Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques. J Med Syst, 2010, 34(4): 619-628.
33.	Sharma G, Friedenberg DA, Annetta NA, et al. Using an artificial neural bypass to restore cortical control of rhythmic movements in a human with quadriplegia. Sci Rep, 2016, 6: 33807.
34.	Bouton CE, Shaikhouni A, Annetta NV, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature, 2016, 533(762): 247-250.
35.	Krittanawong C, Zhang HJ, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol, 2017, 69(21): 2657-2664.
36.	Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging, 2018, 3(3): 223-230.
37.	Abedi V, Goyal N, Tsivgoulis GA, et al. Novel screening tool for stroke using artificial neural network. Stroke, 2017, 48(6): 1678-1681.
38.	Hazlett HC, Gu H, Munsell BC, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature, 2017, 542(7641): 348-351.
39.	Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease. Brain Imaging Behav, 2016, 10(3): 799-817.
40.	Edwards DF, Hollingsworth H, Zazulia AR, et al. Artificial neural networks improve the prediction of mortality in intracerebral hemorrhage. Neurology, 1999, 53(2): 351-357.
41.	Huertas-Fernandez I, Garcia-Gomez FJ, Garcia-Solis D, et al. Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson’s disease using [(123)I]FP-CIT SPECT. Eur J Nucl Med Mol Imaging, 2015, 42(1): 112-119.
42.	Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol, 2017, 2(4): 230-243.
43.	Jha S, Topol EJ. Adapting to artificial intelligence radiologists and pathologists as information specialists. JAMA, 2016, 316(22): 2353-2354.
44.	Lee CS, Nagy PG, Weaver SJ, et al. Cognitive and system factors contributing to diagnostic errors in radiology. AJR Am J Roentgenol, 2013, 201(3): 611-617.

1. Lawrence DR, Palacios-Gonzalez C, Harris J. Artificial intelligence. Camb Q Healthc Ethics, 2016, 25(2): 250-261.
2. Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med, 2018, 131(2): 129-133.
3. Esfandiari N, Babavalian MR, Moghadam A, et al. Knowledge discovery in medicine: current issue and future trend. Expert Syst Appl, 2014, 41(9): 4434-4463.
4. Das S, Chowdhury SR, Saha H. Accuracy enhancement in a fuzzy expert decision making system through appropriate determination of membership functions and its application in a medical diagnostic decision making system. J Med Syst, 2012, 36(3): 1607-1620.
5. Sotos JC. MYCIN and NEOMYCIN: two approaches to generating explanations in rule-based expert systems. Aviat Space Environ Med, 1990, 61(10): 950-954.
6. Goggin LS, Eikelboom RH, Atlas MD. Clinical decision support systems and computer-aided diagnosis in otology. Otolaryngol Head Neck Surg, 2007, 136(4 Suppl): S21-S26.
7. Gavilan C, Gallego J, Gavilan J. ‘Carrusel’: an expert system for vestibular diagnosis. Acta Otolaryngol, 1990, 110(3/4): 161-167.
8. Dong CL, Wang YJ, Zhang Q, et al. The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo. Comput Methods Programs Biomed, 2014, 113(1): 162-174.
9. 马逸, 陈斌. 专家系统在医学上的研究现状及进展. 中国医疗设备, 2008(6): 42-44, 78.
10. 宗相卿, 代林田. 微机应用关幼波肝病诊疗程序 196 例分析. 辽宁中医杂志, 1992(6): 26-27.
11. 易涛, 魏蛟龙. 心血管药物治疗专家系统的设计. 中国药房, 2003(7): 22-23.
12. 卢培佩. 计算机辅助职业病诊断专家系统的研究. 长沙: 中南大学, 2011.
13. 王心涛, 齐效君, 方向明, 等. 中枢神经系统 CT 影像诊断专家系统的设计与实现. 中国医学创新, 2014(34): 12-15.
14. Salas-Gonzalez D, Gorriz JM, Ramirez J, et al. Computer-aided diagnosis of Alzheimer’s disease using support vector machines and classification trees. Phys Med Biol, 2010, 55(10): 2807-2817.
15. Chen X, Wang ZJ, Sy C, et al. Computer-aided diagnosis expert system for cerebrovascular diseases. Neurol Res, 2014, 36(5, SI): 468-474.
16. Kentala EL, Laurikkala JP, Viikki K, et al. Experiences of otoneurological expert system for vertigo. Scand Audiol Suppl, 2001(52): 90-91.
17. Heckerman DE, Nathwani BN. Toward normative expert systems: Part Ⅱ. Probability-based representations for efficient knowledge acquisition and inference. Methods Inf Med, 1992, 31(2): 106-116.
18. Oropesa I, Sanchez-Gonzalez P, Chmarra MK, et al. Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis. Surg Endosc, 2014, 28(2): 657-670.
19. Lin RH, Chuang CL. A hybrid diagnosis model for determining the types of the liver disease. Comput Biol Med, 2010, 40(7): 665-670.
20. Chuang CL. Case-based reasoning support for liver disease diagnosis. Artif Intell Med, 2011, 53(1): 15-23.
21. Sterling M, Huang DT, Ghoraani B. Developing a new computer-aided clinical decision support system for prediction of successful postcardioversion patients with persistent atrial fibrillation. Comput Math Methods Med, 2015: 527815.
22. 刘光熠, 赵迎宾, Ellenius J, 等. 一种灵活的基于临床指南的临床决策支持系统. 计算机应用与软件, 2011, 28(6): 189-191, 238.
23. 杜敏, 罗建伟. 基于大数据的医院决策支持系统构建研究. 中国数字医学, 2014(12): 73-75.
24. 倪家远, 汤亚玲. 基于决策树和面向对象技术的糖尿病诊断专家系统设计. 苏州科技学院学报: 自然科学版, 2016(1): 70-74.
25. 赵凌云. 面向服务的消费者行为分析及推荐模型研究. 济南: 山东师范大学, 2014.
26. Sidky H, Whitmer JK. Learning free energy landscapes using artificial neural networks. J Chem Phys, 2018, 148(10): 104111.
27. Fabris F, Doherty A, Palmer D, et al. A new approach for interpreting random forest models and its application to the biology of ageing. Bioinformatics, 2018. doi: 10.1093/bioinformatics/bty087.
28. Korhani Kangi A, Bahrampour A. Predicting the survival of gastric cancer patients using artificial and bayesian neural networks. Asian Pac J Cancer Prev, 2018, 19(2): 487-490.
29. Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer, 1997, 79(4): 857-862.
30. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115.
31. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 2016, 316(22): 2402-2410.
32. Acharya RU, Yu WW, Zhu KY, et al. Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques. J Med Syst, 2010, 34(4): 619-628.
33. Sharma G, Friedenberg DA, Annetta NA, et al. Using an artificial neural bypass to restore cortical control of rhythmic movements in a human with quadriplegia. Sci Rep, 2016, 6: 33807.
34. Bouton CE, Shaikhouni A, Annetta NV, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature, 2016, 533(762): 247-250.
35. Krittanawong C, Zhang HJ, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol, 2017, 69(21): 2657-2664.
36. Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging, 2018, 3(3): 223-230.
37. Abedi V, Goyal N, Tsivgoulis GA, et al. Novel screening tool for stroke using artificial neural network. Stroke, 2017, 48(6): 1678-1681.
38. Hazlett HC, Gu H, Munsell BC, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature, 2017, 542(7641): 348-351.
39. Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease. Brain Imaging Behav, 2016, 10(3): 799-817.
40. Edwards DF, Hollingsworth H, Zazulia AR, et al. Artificial neural networks improve the prediction of mortality in intracerebral hemorrhage. Neurology, 1999, 53(2): 351-357.
41. Huertas-Fernandez I, Garcia-Gomez FJ, Garcia-Solis D, et al. Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson’s disease using [(123)I]FP-CIT SPECT. Eur J Nucl Med Mol Imaging, 2015, 42(1): 112-119.
42. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol, 2017, 2(4): 230-243.
43. Jha S, Topol EJ. Adapting to artificial intelligence radiologists and pathologists as information specialists. JAMA, 2016, 316(22): 2353-2354.
44. Lee CS, Nagy PG, Weaver SJ, et al. Cognitive and system factors contributing to diagnostic errors in radiology. AJR Am J Roentgenol, 2013, 201(3): 611-617.

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