Review on Artificial Intelligence and Applications in Healthcare

Ravish Huchegowda, MBBS, MD, Srinivas Huchegowda, MBBS, Jyothi R. Jain, Mphil, PhD, Manoj Parthasarathy, MBBS Student, Tharika Shraddha, MBBS, Nagalakshmi C. Sathyanarayanshetty, MBBS, MD, Bharat V. Poojary, BE, Farhan Zameer, MSc, PhD, Chetan H Gowda, BE, MTech, PhD, Naveen H. Gowda, BE, MTech, Huskur N. Venkatesh, MSc, PhD

DOI: https://doi.org/10.17140/noj-6-e010 · Published 2019-01-19 · Licence: https://creativecommons.org/licenses/by/4.0 · © 2019 The Author(s)

Also available as PDF (figures and tables appear in the PDF).

Keywords: Artificial intelligence; Deep learning; Healthcare; Diagnostics; Imaging; Artificial intelligence; Deep learning; Healthcare; Diagnostics; Imaging

Conclusion

Artificial intelligence is going to make huge difference in our day to day lives. AI is influencing the way we live and how we interact with the world, and there is much more to come in the years to follow with more advancement.

Bridging the link between current nano-sciences and AI, it can boost research in various disciplines and provide a new generation of information and communication technologies that shows a huge scope. Major changes in the education curriculum of medical professionals need to take place. But the rising cost of healthcare may prove to be an independent driving force to develop these technologies; meanwhile, health information technology not only improves the quality of care but also reduces its cost significantly. AI has the potentiality to reduce the cost of healthcare markedly and in future, this may translate into creation of promotional policies to accelerate investment in AI by Springer International Publishing. 2018.

8. Gubbi J, Buyya R, Marusic S, Palaniswami M. Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems. 2013; 29: 1645-1660. doi: 10.1016/j. future.2013.01.010

9. Salman M, Wahab Ahmed A, Ahmad Khan O, et al. Artificial intelligence in bio-medical domain: An overview of AI based innovations in medical. International Journal of Advanced Computer Science and Applications. 2017; 8: 319-327.

10. Turban E, Leidner D, McLean E, Wetherbe J. Information th Technology for Management, 6th ed. New Jersey, USA: John Wiley & Sons; 2008.

11. Torkamani A, Andersen KG, Steinhubl SR, Topol EJ. Highdefinition medicine. Cell. 2017; 170: 828-843. doi: 10.1016/j.

cell.2017.08.007

12. Mitra S, Shankar BU. Integrating radio imaging with gene expressions toward a personalized management of cancer. IEEE Transactions on Human-Machine Systems. 2014; 44: 664-677. doi: 10.1109/THMS.2014.2325744

13. Gustafson DH, McTavish FM, Chih MY, et al. A smartphone application to support recovery from alcoholism: A randomized clinical trial. JAMA psychiatry. 2014; 71: 566-572. doi: 10.1001/ jamapsychiatry.2013.4642

14. Diana F. Artificial Intelligence Intersects with Nanotechnology. Web site: https://medium.com/@frankdiana/artificialintelligence-intersects-with-nanotechnology-a674204daa31. 2016.

15. Jain LC, Kandel A, Teodorescu HN. Fuzzy and Neuro-Fuzzy Systems in Medicine. Florida, USA: CRC Press. 2017.

16. Neill DB. Using artificial intelligence to improve hospital inpatient care. IEEE Intelligent Systems. 2013; 1: 92-95. doi: 10.1109/ MIS.2013.51

17. Krishnaiah V, Narsimha DG, Chandra DN. Diagnosis of lung cancer prediction system using data mining classification techniques. International Journal of Computer Science and Information Technologies. 2013; 4: 39-45.

18. Müller H, Michoux N, Bandon D, Geissbuhler A. A review of content-based image retrieval systems in medical applicationsclinical benefits and future directions. Int J Med Inform. 2004; 73: 1-23. doi: 10.1016/j.ijmedinf.2003.11.024

19. Vas P. Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-Neural, and Genetic-Algorithm- Based Techniques. England, UK: Oxford University Press. 1999.

20. Jang JS. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics. 1993; 23: 665-685. doi: 10.1109/21.256541

21. Hudson DL, Cohen ME. Use of Intelligent Agents in the Diagnosis of Cardiac Disorders. Paper presented at: Computers in Cardiology. 22-25 September 2002; Memphis, TN, USA. 633-636. doi: 10.1109/CIC.2002.1166852

22. Beaver BV. Feline Behavior-E-Book. Philadelphia, USA: Saunders Press: 2003.

23. Turner AP, Magan N. Electronic noses and disease diagnostics. Nat Rev Microbiol. 2004; 2: 161-166. doi: 10.1038/nrmicro823

References

1. ModiS. Artificial intelligence and neurology. J Biomed Syst Emerg Technol. 2016; 3: c) Heart disease classification: Artificial neural network has substantiated its ability by working on the classification of heart
2. KatamreddySRiordanDDoodyP. Artificial Calf Weaning disease.21 In this technique for the classification of stroke, the input Strategies and the Role of Machine Learning: A Review. Papaer of sensor is given to the system that uses forward feed network presented at: Irish Signals and Systems Conference (ISSC). 20-21 with the rule of back propagation way. June 2017; Killarney, Ireland. doi: 10.1109/ISSC.2017.7983634 d) Decision support system to diagnose nodules: Through the10.1109/issc.2017.7983634 https://doi.org/10.1109/issc.2017.7983634
3. ZackovaE. Intelligence explosion quest for humankind. Beyond concept of ANN, the new proposed system is decision support Artificial Intelligence. 2015; 9: 31-43. doi: 10.1007/978-3-319-09668system (DSS). A decision support system diagnoses nodules into 1_3 benign and malignant or identifies its severity by analyzing the collected data.610.1007/978-3-319-09668system https://doi.org/10.1007/978-3-319-09668system
4. DashD. Autonomy and Artificial Intelligence: The Future Ingredient of Area Denial Strategy in Land Warfare. New Delhi, India: KW Regardless of its ability to see and listen, AI also smells. Publishers Pvt Ltd. 2018. Humans aren’t particularly aware of the richness of information that can be transmitted through the air and can be perceived by a
5. Dembski W, Kushiner J, (eds). Signs of Intelligence: Understanding highly sensitive olfactory system.22 AI brought change into that by Intelligent Design. Ada, Michigan, USA: Brazos Press. 2001. introducing machines in the laboratory which detects very small amounts of substances in the air. Those machines are called gas-
6. KallioJ. Support Vector machine and deep learning in medical chromatography mass-spectrometers or gas chromatography–applications. University of Tampere. 2017. Web site. http://tampub. mass spectrometry (GC-MS), which analyses the air to discover uta.fi/bitstream/handle/10024/101911/GRADU-1504009242. thousands of different molecules known as volatile organic pdf?sequence=1&isAllowed=y compounds. AI system helps to reveal the illness by smelling human breathe substances.23
7. GubbiJBuyyaRMarusicSPalaniswamiM. Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems. 2013; 29: 1645-1660. doi: 10.1016/j. future.2013.01.01010.1016/j.future.2013.01.010 https://doi.org/10.1016/j.future.2013.01.010
8. Salman M, Wahab Ahmed A, Ahmad Khan O, et al. Artificial intelligence in bio-medical domain: An overview of AI based innovations in medical. International Journal of Advanced Computer Science and Applications. 2017; 8: 319-327
9. TurbanELeidnerDMcLeanEWetherbeJ. Information th Technology for Management, 6th ed. New Jersey, USA: John Wiley & Sons; 2008
10. TorkamaniAAndersenKGSteinhublSRTopolEJ. Highdefinition medicine. Cell. 2017; 170: 828-843. doi: 10.1016/j. cell.2017.08.00710.1016/j.cell.2017.08.007 https://doi.org/10.1016/j.cell.2017.08.007
11. MitraSShankarBU. Integrating radio imaging with gene expressions toward a personalized management of cancer. IEEE Transactions on Human-Machine Systems. 2014; 44: 664-677. doi: 10.1109/THMS.2014.232574410.1109/thms.2014.2325744 https://doi.org/10.1109/thms.2014.2325744
12. GustafsonDHMcTavishFMChihMY. A smartphone application to support recovery from alcoholism: A randomized clinical trial. JAMA psychiatry. 2014; 71: 566-572. doi: 10.1001/ jamapsychiatry.2013.4642
13. DianaF. Artificial Intelligence Intersects with Nanotechnology. Web site: https://medium.com/@frankdiana/artificialintelligence-intersects-with-nanotechnology-a674204daa31. 2016
14. JainLCKandelATeodorescuHN. Fuzzy and Neuro-Fuzzy Systems in Medicine. Florida, USA: CRC Press. 2017
15. NeillDB. Using artificial intelligence to improve hospital inpatient care. IEEE Intelligent Systems. 2013; 1: 92-95. doi: 10.1109/ MIS.2013.51
16. KrishnaiahVNarsimhaDGChandraDN. Diagnosis of lung cancer prediction system using data mining classification techniques. International Journal of Computer Science and Information Technologies. 2013; 4: 39-45
17. MüllerHMichouxNBandonDGeissbuhlerA. A review of content-based image retrieval systems in medical applicationsclinical benefits and future directions. Int J Med Inform. 2004; 73: 1-23. doi: 10.1016/j.ijmedinf.2003.11.02410.1016/j.ijmedinf.2003.11.024 https://doi.org/10.1016/j.ijmedinf.2003.11.024
18. VasP. Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-Neural, and Genetic-Algorithm-Based Techniques. England, UK: Oxford University Press. 1999
19. JangJS. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics. 1993; 23: 665-685. doi: 10.1109/21.25654110.1109/21.256541 https://doi.org/10.1109/21.256541
20. HudsonDLCohenME. Use of Intelligent Agents in the Diagnosis of Cardiac Disorders. Paper presented at: Computers in Cardiology. 22-25 September 2002; Memphis, TN, USA. 633-636. doi: 10.1109/CIC.2002.116685210.1109/cic.2002.1166852 https://doi.org/10.1109/cic.2002.1166852
21. BeaverBV. Feline Behavior-E-Book. Philadelphia, USA: Saunders Press: 2003
22. TurnerAPMaganN. Electronic noses and disease diagnostics. Nat Rev Microbiol. 2004; 2: 161-166. doi: 10.1038/nrmicro82310.1038/nrmicro823 https://doi.org/10.1038/nrmicro823