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SCARY DARK SIDE OF ARTIFICIAL INTELLIGENCE: A PERILOUS CONTRIVANCE TO MANKIND
Corresponding Author(s) : Gautam Kumar
Humanities & Social Sciences Reviews,
Vol. 7 No. 5 (2019): September
Abstract
Purpose of Study: The purpose of the study is to investigate the dark side of artificial intelligence followed by the question of whether AI is programmed to do something destructive or AI is programmed to do something beneficial?
Methodology: A study of different biased Super AI is carried out to find the dark side of AI. In this paper SRL (system review of literature approach methodology is used and the data is collected from the different projects of MIT’s media lab named “Norman AIâ€, “Shelley†and AI-generated algorithm COMPAS.
Main Finding: The study carried out the result if AI is trained in a biased way it will create havoc to mankind.
Implications/Applications: The article can help in developing super-AIs which can benefit the society in a controlled way without having any negative aspects.
Novelty/originality of the study: Our findings ensure that biased AI has a negative impact on society.
Keywords
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- A. Mellit, SA Kalogirou, (2008). Progress in energy and combustion science, 2008 – Elsevier, Volume 34, Issue 5, Pages 574-632. https://doi.org/10.1016/j.pecs.2008.01.001 DOI: https://doi.org/10.1016/j.pecs.2008.01.001
- A Space Odyssey (film). (2001). Retrieved from https://en.wikipedia.org/wiki/2001:_A_Space_ Odyssey_(film).
- A. Graves, A. Mohamed, G. Hinton, (2013). Speech recognition with deep recurrent neural networks, in ICASSP2013, pp.1-5. https://doi.org/10.1109/ICASSP.2013.6638947 DOI: https://doi.org/10.1109/ICASSP.2013.6638947
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- A. Ratnaparkhi, E. Pilli, R. Joshi, (2016). Survey of scaling platforms for deep neural networks, In Proc of International Conference on Emerging Trends in Communication Technologies, pp.1-6. https://doi.org/10.1109/ETCT.2016.7882969 DOI: https://doi.org/10.1109/ETCT.2016.7882969
- Avengers: Age of Ultron. (2019). Retrieved from https://en.wikipedia.org/wiki/ Avengers :_Age_ of_Ultron
- B. Catanzaro, (2013). Deep learning with COTS HPC systems, In Proc of the 30th International Conference on Machine Learning, pp.1337-1345.
- B. Wiederhold, G. Riva, M. Wiederhold, (2015). Virtual reality in healthcare: medical simulation and experiential interface,â€Annual Review of Cyber Therapy and Telemedicine, vol.13, 239 pages.
- Becker, A., Bar-Yehuda, R. and Geiger, D.,(2000) Randomised algorithms for the loop cutset problem, Journal of Artificial Intelligence Research, Vol. 12, pp.219-234. https://doi.org/10.1613/jair.638 DOI: https://doi.org/10.1613/jair.638
- Bhattacharyya, C. and Keerthi, S. S.(2001), Mean field methods for a special class of belief networks, Journal of Artificial Intelligence Research, Vol. 15, pp.91-114. https://doi.org/10.1613/jair.734 DOI: https://doi.org/10.1613/jair.734
- Bicentennial Man (film). (2019, October 30). Retrieved from https://en.wikipedia.org/wiki /Bicentennial_Man_(film).
- Bill Hibbard, (2001). Super-Intelligent machines, ACM SIGGRAPH Computer Graphics, Volume 35, Issue 1, page 11-13. https://doi.org/10.1145/377025.377033 DOI: https://doi.org/10.1145/377025.377033
- Chawla, N. V., Bowyer, K. W., Hall, L. O. and Kegelmeyer, W. P.( 2002), SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, Vol. 16, pp.321-357. https://doi.org/10.1613/jair.953 DOI: https://doi.org/10.1613/jair.953
- Chen, X. and Van Beek, P.(2001), Conflict-directed backjumping revisited, Journal of Artificial Intelligence Research, Vol. 14, pp.53-81. https://doi.org/10.1613/jair.788 DOI: https://doi.org/10.1613/jair.788
- Donald A.Norman, (1991). Approaches to the study of intelligence, Elsevier, Volume 47, Issues 1–3, January, Pages 327-346. https://doi.org/10.1016/0004-3702(91)90058-R DOI: https://doi.org/10.1016/0004-3702(91)90058-R
- F. Pasquale, (2015). Howard University Press, Cambridge, London, England. Pp 15-18.
- Fabio Massimo Zanzotto, (2019). Viewpoint: Human-in-the-loop Artificial Intelligence, Journal of Artificial Intelligence Research 64 (2019) 243-25. https://doi.org/10.1613/jair.1.11345 DOI: https://doi.org/10.1613/jair.1.11345
- G. Bartsch, A. Mitra, S. Mitra, A. Almal, K. Steven, D. Skinner, D. Fry, P. Lenehan, W. Worzel, R. Cote, (2016). Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder, The Journal of Urology,vol.195, pp.493-498. https://doi.org/10.1016/j.juro.2015.09.090 DOI: https://doi.org/10.1016/j.juro.2015.09.090
- G. Lacey, G. Taylor, S. Areibi, (2016). Deep learning on FPGAs: past, present, and future, pp.1-8, arXiv: 1602.04283.
- Good, I. J. , Franz L. Alt; Morris Rubinoff (eds.), (1965). Academic Press, pp. 31–88, doi:10.1016/S0065-2458(08)60418-0, ISBN 9780120121069. https://doi.org/10.1016/S0065-2458(08)60418-0 DOI: https://doi.org/10.1016/S0065-2458(08)60418-0
- H. Bourlard, M. Morgan, (1994). Connnectionist speech recognition: a hybrid approach, Kluwer Academic Publishers. https://doi.org/10.1007/978-1-4615-3210-1 DOI: https://doi.org/10.1007/978-1-4615-3210-1
- H. Mei, M. Bansal, M. Walter, (2016). What to talk about and how? Selective generation using LSTMs with coarse-to-fine alignment, In NAACL-HLT, pp.1-11. https://doi.org/10.18653/v1/N16-1086 DOI: https://doi.org/10.18653/v1/N16-1086
- Hong, J.(2001), Goal recognition through goal graph analysis, Journal of Artificial Intelligence Research, Vol. 15, pp.1-30. https://doi.org/10.1613/jair.830 DOI: https://doi.org/10.1613/jair.830
- I. Sutskever, O. Vinyals, Q. Le, (2010). Sequence to sequence learning with neural networks, In Advances in Neural Information Processing Systems, pp.3104-3112, 2014.
- J. F. Bonnefon, A. Shariff, and I. Rahwan, (2016). The social dilemma of autonomous vehicles. Science, 352, 1573–1576. https://doi.org/10.1126/science.aaf2654 DOI: https://doi.org/10.1126/science.aaf2654
- John McCarthy, Marvin L. Minsky,Nathaniel Rochester, and Claude E. Shannon, (2006), Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, AI Magazine Volume 27 Number 4, pg 12-14.
- Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath, (2017), A Brief Survey of Deep Reinforcement Learning, ieee signal processing magazine, special issue on deep learning for image understanding (arxiv extended version), pg 1-16. https://doi.org/10.1109/MSP.2017.2743240 DOI: https://doi.org/10.1109/MSP.2017.2743240
- Larson, J., Angwin, J., Kirchner, L., & Mattu, S. (2019, March 9). How We Analyzed the COMPAS Recidivism Algorithm. Retrieved from https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm.
- Lawrence E. Widman, Kenneth A. Loparo, Norman R. Nielsen, (1989). Artificial intelligence, simulation & modelling, John Wiley & Sons, Inc. New York, NY, USA, ISBN:0-471-60599-9
- M. Luong, Q. Le, I. Sutskever, O. Vinyals, L. Kaiser, (2016). Multitask sequence to sequence learning,†In Proc ICLR, pp.1-10.
- Peng Y. and Zhang X.(2007), Integrative data mining in systems biology: from text to network mining, Artificial Intelligence in Medicine, Vol. 41, No. 2, pp.83-86. https://doi.org/10.1016/j.artmed.2007.08.001 DOI: https://doi.org/10.1016/j.artmed.2007.08.001
- Project Overview ' Norman. (n.d.). Retrieved from https://www.media.mit.edu/projects/norman/overview.
- Project Overview ' Shelley: Human-AI Collaborated Horror Stories. (n.d.). Retrieved from https://www.media.mit.edu /projects/shelley/overview.
- R. Raina, A. Madhavan, A. Ng, (2009). Large-scale deep unsupervised learning using graphics processors, In Proc of 26th Annual International Conference on Machine Learning, pp.873-880. https://doi.org/10.1145/1553374.1553486 DOI: https://doi.org/10.1145/1553374.1553486
- Rajeev Gupta, (2011). Multi-Agent Approach towards Face Recognition System, International Journal of Computing and Corporate Research (IJCCR), ISSN (Online) – 2249 – 054 X.
- Revell, T. (2017, May 31). AI will be able to beat us at everything by 2060, say experts. Retrieved from https://www.newscientist.com/article/2133188-ai-will-be-able-to-beat-us-at-everything-by-2060-say-experts/.
- S.M. Furnell, M.J Warrner, (1999), Computer hacking and cyber terrorism: the real threats in the new millennium?, Computer & Security, Volume 18, Issue 1, Pages 28-34, DOI: https://doi.org/10.1016/S0167-4048(99)80006-6 DOI: https://doi.org/10.1016/S0167-4048(99)80006-6
- Savia A. Coutinho, (2006). The Relationship between the Need for Cognition, Metacognition, and Intellectual Task Performance, Educational Research and Reviews Vol. 1 (5), pp. 162-164, August 2006, ISSN 1990-3839 © 2006 Academic Journals.
- Singer, J., Gent, I. P. and Smaill, A.(2000), Backbone fragility and the local search cost peak, Journal of Artificial Intelligence Research, Vol. 12, pp.235-270. https://doi.org/10.1613/jair.711 DOI: https://doi.org/10.1613/jair.711
- Singh, G., Dubey, O. P., & Kumar, G. (2018). A SOLUTION TO SELECTIVE FORWARD ATTACK IN WIRELESS SENSOR NETWORK. International Journal of Students’ Research in Technology & Management, 6(2), 25-30. https://doi.org/10.18510/ijsrtm.2018.625 DOI: https://doi.org/10.18510/ijsrtm.2018.625
- Siri, https://en.wikipedia.org/wiki/Siri
- Stone, P., Littman, M.L., Singh, S., Kearns, M.(2001), ATTAC-2000: An adaptive autonomous bidding agent, Journal of Artificial Intelligence Research, Vol. 15, pp. 189-206. https://doi.org/10.1613/jair.865 DOI: https://doi.org/10.1613/jair.865
- T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, S. Khudanpur, (2007). Recurrent neural network based language model,†In Proc of Interspeech10, pp.1045-1048.
- Virender Singh, Rajeev Gupta, (2019) Novel Framework of Semantic Based Image Reterival by Convoluted Features with Non-Linear Mapping in Cyberspace, International Journal of Recent Technology and Engineering, ISSN 2277-3878, Vol-8, Issue-1C2, pp-939-942.
- W. Lin, S. Lin, T. Yang, (2017). Integrated business prestige and artificial intelligence for corporate decision making in dynamic environments, Cybernetics and Systems. https://doi.org/10.1080/01969722.2017.1284533 DOI: https://doi.org/10.1080/01969722.2017.1284533
- Wang S., Wang Y., Du W., Sun F.(2007), Wang X., Zhou C. and Liang Y., A multi-approaches-guided genetic algorithm with application to operon prediction, Artificial Intelligence in Medicine, Vol. 41, No. 2, pp.151-159. https://doi.org/10.1016/j.artmed.2007.07.010 DOI: https://doi.org/10.1016/j.artmed.2007.07.010
- Yunwen WU, Xueyi Ai, (2008), Face Detection in Color Images Using AdaBoost Algorithm Based on Skin Color Information, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), https://doi.org/10.1109/WKDD.2008.148 DOI: https://doi.org/10.1109/WKDD.2008.148
- Zhou X., Liu B., Wu Z. and Feng Y.(2007), Integrative mining of traditional Chines medicine literature and MEDLINE for functional gene networks, Artificial Intelligence in Medicine, Vol. 41, No. 2, pp.87-104. https://doi.org/10.1016/j.artmed.2007.07.007 DOI: https://doi.org/10.1016/j.artmed.2007.07.007
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A Space Odyssey (film). (2001). Retrieved from https://en.wikipedia.org/wiki/2001:_A_Space_ Odyssey_(film).
A. Graves, A. Mohamed, G. Hinton, (2013). Speech recognition with deep recurrent neural networks, in ICASSP2013, pp.1-5. https://doi.org/10.1109/ICASSP.2013.6638947 DOI: https://doi.org/10.1109/ICASSP.2013.6638947
A. Mnih, G. Hinton, (2007). “Three new graphical models for statistical language modelling,†In Proc of ICML07, pp.641-648. https://doi.org/10.1145/1273496.1273577 DOI: https://doi.org/10.1145/1273496.1273577
A. Ratnaparkhi, E. Pilli, R. Joshi, (2016). Survey of scaling platforms for deep neural networks, In Proc of International Conference on Emerging Trends in Communication Technologies, pp.1-6. https://doi.org/10.1109/ETCT.2016.7882969 DOI: https://doi.org/10.1109/ETCT.2016.7882969
Avengers: Age of Ultron. (2019). Retrieved from https://en.wikipedia.org/wiki/ Avengers :_Age_ of_Ultron
B. Catanzaro, (2013). Deep learning with COTS HPC systems, In Proc of the 30th International Conference on Machine Learning, pp.1337-1345.
B. Wiederhold, G. Riva, M. Wiederhold, (2015). Virtual reality in healthcare: medical simulation and experiential interface,â€Annual Review of Cyber Therapy and Telemedicine, vol.13, 239 pages.
Becker, A., Bar-Yehuda, R. and Geiger, D.,(2000) Randomised algorithms for the loop cutset problem, Journal of Artificial Intelligence Research, Vol. 12, pp.219-234. https://doi.org/10.1613/jair.638 DOI: https://doi.org/10.1613/jair.638
Bhattacharyya, C. and Keerthi, S. S.(2001), Mean field methods for a special class of belief networks, Journal of Artificial Intelligence Research, Vol. 15, pp.91-114. https://doi.org/10.1613/jair.734 DOI: https://doi.org/10.1613/jair.734
Bicentennial Man (film). (2019, October 30). Retrieved from https://en.wikipedia.org/wiki /Bicentennial_Man_(film).
Bill Hibbard, (2001). Super-Intelligent machines, ACM SIGGRAPH Computer Graphics, Volume 35, Issue 1, page 11-13. https://doi.org/10.1145/377025.377033 DOI: https://doi.org/10.1145/377025.377033
Chawla, N. V., Bowyer, K. W., Hall, L. O. and Kegelmeyer, W. P.( 2002), SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, Vol. 16, pp.321-357. https://doi.org/10.1613/jair.953 DOI: https://doi.org/10.1613/jair.953
Chen, X. and Van Beek, P.(2001), Conflict-directed backjumping revisited, Journal of Artificial Intelligence Research, Vol. 14, pp.53-81. https://doi.org/10.1613/jair.788 DOI: https://doi.org/10.1613/jair.788
Donald A.Norman, (1991). Approaches to the study of intelligence, Elsevier, Volume 47, Issues 1–3, January, Pages 327-346. https://doi.org/10.1016/0004-3702(91)90058-R DOI: https://doi.org/10.1016/0004-3702(91)90058-R
F. Pasquale, (2015). Howard University Press, Cambridge, London, England. Pp 15-18.
Fabio Massimo Zanzotto, (2019). Viewpoint: Human-in-the-loop Artificial Intelligence, Journal of Artificial Intelligence Research 64 (2019) 243-25. https://doi.org/10.1613/jair.1.11345 DOI: https://doi.org/10.1613/jair.1.11345
G. Bartsch, A. Mitra, S. Mitra, A. Almal, K. Steven, D. Skinner, D. Fry, P. Lenehan, W. Worzel, R. Cote, (2016). Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder, The Journal of Urology,vol.195, pp.493-498. https://doi.org/10.1016/j.juro.2015.09.090 DOI: https://doi.org/10.1016/j.juro.2015.09.090
G. Lacey, G. Taylor, S. Areibi, (2016). Deep learning on FPGAs: past, present, and future, pp.1-8, arXiv: 1602.04283.
Good, I. J. , Franz L. Alt; Morris Rubinoff (eds.), (1965). Academic Press, pp. 31–88, doi:10.1016/S0065-2458(08)60418-0, ISBN 9780120121069. https://doi.org/10.1016/S0065-2458(08)60418-0 DOI: https://doi.org/10.1016/S0065-2458(08)60418-0
H. Bourlard, M. Morgan, (1994). Connnectionist speech recognition: a hybrid approach, Kluwer Academic Publishers. https://doi.org/10.1007/978-1-4615-3210-1 DOI: https://doi.org/10.1007/978-1-4615-3210-1
H. Mei, M. Bansal, M. Walter, (2016). What to talk about and how? Selective generation using LSTMs with coarse-to-fine alignment, In NAACL-HLT, pp.1-11. https://doi.org/10.18653/v1/N16-1086 DOI: https://doi.org/10.18653/v1/N16-1086
Hong, J.(2001), Goal recognition through goal graph analysis, Journal of Artificial Intelligence Research, Vol. 15, pp.1-30. https://doi.org/10.1613/jair.830 DOI: https://doi.org/10.1613/jair.830
I. Sutskever, O. Vinyals, Q. Le, (2010). Sequence to sequence learning with neural networks, In Advances in Neural Information Processing Systems, pp.3104-3112, 2014.
J. F. Bonnefon, A. Shariff, and I. Rahwan, (2016). The social dilemma of autonomous vehicles. Science, 352, 1573–1576. https://doi.org/10.1126/science.aaf2654 DOI: https://doi.org/10.1126/science.aaf2654
John McCarthy, Marvin L. Minsky,Nathaniel Rochester, and Claude E. Shannon, (2006), Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, AI Magazine Volume 27 Number 4, pg 12-14.
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath, (2017), A Brief Survey of Deep Reinforcement Learning, ieee signal processing magazine, special issue on deep learning for image understanding (arxiv extended version), pg 1-16. https://doi.org/10.1109/MSP.2017.2743240 DOI: https://doi.org/10.1109/MSP.2017.2743240
Larson, J., Angwin, J., Kirchner, L., & Mattu, S. (2019, March 9). How We Analyzed the COMPAS Recidivism Algorithm. Retrieved from https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm.
Lawrence E. Widman, Kenneth A. Loparo, Norman R. Nielsen, (1989). Artificial intelligence, simulation & modelling, John Wiley & Sons, Inc. New York, NY, USA, ISBN:0-471-60599-9
M. Luong, Q. Le, I. Sutskever, O. Vinyals, L. Kaiser, (2016). Multitask sequence to sequence learning,†In Proc ICLR, pp.1-10.
Peng Y. and Zhang X.(2007), Integrative data mining in systems biology: from text to network mining, Artificial Intelligence in Medicine, Vol. 41, No. 2, pp.83-86. https://doi.org/10.1016/j.artmed.2007.08.001 DOI: https://doi.org/10.1016/j.artmed.2007.08.001
Project Overview ' Norman. (n.d.). Retrieved from https://www.media.mit.edu/projects/norman/overview.
Project Overview ' Shelley: Human-AI Collaborated Horror Stories. (n.d.). Retrieved from https://www.media.mit.edu /projects/shelley/overview.
R. Raina, A. Madhavan, A. Ng, (2009). Large-scale deep unsupervised learning using graphics processors, In Proc of 26th Annual International Conference on Machine Learning, pp.873-880. https://doi.org/10.1145/1553374.1553486 DOI: https://doi.org/10.1145/1553374.1553486
Rajeev Gupta, (2011). Multi-Agent Approach towards Face Recognition System, International Journal of Computing and Corporate Research (IJCCR), ISSN (Online) – 2249 – 054 X.
Revell, T. (2017, May 31). AI will be able to beat us at everything by 2060, say experts. Retrieved from https://www.newscientist.com/article/2133188-ai-will-be-able-to-beat-us-at-everything-by-2060-say-experts/.
S.M. Furnell, M.J Warrner, (1999), Computer hacking and cyber terrorism: the real threats in the new millennium?, Computer & Security, Volume 18, Issue 1, Pages 28-34, DOI: https://doi.org/10.1016/S0167-4048(99)80006-6 DOI: https://doi.org/10.1016/S0167-4048(99)80006-6
Savia A. Coutinho, (2006). The Relationship between the Need for Cognition, Metacognition, and Intellectual Task Performance, Educational Research and Reviews Vol. 1 (5), pp. 162-164, August 2006, ISSN 1990-3839 © 2006 Academic Journals.
Singer, J., Gent, I. P. and Smaill, A.(2000), Backbone fragility and the local search cost peak, Journal of Artificial Intelligence Research, Vol. 12, pp.235-270. https://doi.org/10.1613/jair.711 DOI: https://doi.org/10.1613/jair.711
Singh, G., Dubey, O. P., & Kumar, G. (2018). A SOLUTION TO SELECTIVE FORWARD ATTACK IN WIRELESS SENSOR NETWORK. International Journal of Students’ Research in Technology & Management, 6(2), 25-30. https://doi.org/10.18510/ijsrtm.2018.625 DOI: https://doi.org/10.18510/ijsrtm.2018.625
Siri, https://en.wikipedia.org/wiki/Siri
Stone, P., Littman, M.L., Singh, S., Kearns, M.(2001), ATTAC-2000: An adaptive autonomous bidding agent, Journal of Artificial Intelligence Research, Vol. 15, pp. 189-206. https://doi.org/10.1613/jair.865 DOI: https://doi.org/10.1613/jair.865
T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, S. Khudanpur, (2007). Recurrent neural network based language model,†In Proc of Interspeech10, pp.1045-1048.
Virender Singh, Rajeev Gupta, (2019) Novel Framework of Semantic Based Image Reterival by Convoluted Features with Non-Linear Mapping in Cyberspace, International Journal of Recent Technology and Engineering, ISSN 2277-3878, Vol-8, Issue-1C2, pp-939-942.
W. Lin, S. Lin, T. Yang, (2017). Integrated business prestige and artificial intelligence for corporate decision making in dynamic environments, Cybernetics and Systems. https://doi.org/10.1080/01969722.2017.1284533 DOI: https://doi.org/10.1080/01969722.2017.1284533
Wang S., Wang Y., Du W., Sun F.(2007), Wang X., Zhou C. and Liang Y., A multi-approaches-guided genetic algorithm with application to operon prediction, Artificial Intelligence in Medicine, Vol. 41, No. 2, pp.151-159. https://doi.org/10.1016/j.artmed.2007.07.010 DOI: https://doi.org/10.1016/j.artmed.2007.07.010
Yunwen WU, Xueyi Ai, (2008), Face Detection in Color Images Using AdaBoost Algorithm Based on Skin Color Information, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), https://doi.org/10.1109/WKDD.2008.148 DOI: https://doi.org/10.1109/WKDD.2008.148
Zhou X., Liu B., Wu Z. and Feng Y.(2007), Integrative mining of traditional Chines medicine literature and MEDLINE for functional gene networks, Artificial Intelligence in Medicine, Vol. 41, No. 2, pp.87-104. https://doi.org/10.1016/j.artmed.2007.07.007 DOI: https://doi.org/10.1016/j.artmed.2007.07.007