Artificial Intelligence in the Investigation of Medical Crimes: Perspectives and Challenges
DOI:
https://doi.org/10.54481/Keywords:
Artificial Intelligence, investigation, medical crimes, inadequate care, ethical aspects, legal normsAbstract
The article examines the application of artificial intelligence (AI) technologies in the investigation of crimes related to inadequate medical care. It analyzes the advantages and limitations of using AI, including enhancing the accuracy and speed of investigations, and discusses ethical and legal aspects. Practical examples are provided, and recommendations for integrating AI into legal and medical practice are proposed.
References
1 World Health Organization. Patient Safety. WHO, 2019.
2 Ibidem.
3 Slawomirski, Lucian, Ane Auraaen, and Niek Klazinga. In: The Economics of Patient Safety. OECD Health Working Papers, no. 96, 2017.
4 Vincent, Charles, Graham Neale, and Maria Woloshynowych. Adverse Events in British Hospitals: Preliminary Retrospective Record Review. . In: BMJ, vol. 322, no. 7285, 2001, pp. 517-519. https://doi.org/10.1136/bmj.322.7285.517
5 O'Connor, E., Coates, H. M., Yardley, I. E., and Wu, A. W. Disclosure of Patient Safety Incidents: A Comprehensive Review. In: International Journal for Quality in Health Care, vol. 22, no. 5, 2010, pp. 371-379. https://doi.org/10.1093/intqhc/mzq042
6 Rosenbaum, Lisa. The Untold Toll - The Pandemic's Effects on Patients without Covid-19. In: The New England Journal of Medicine, vol. 382, no. 24, 2020, pp. 2368-2371. https://doi.org/10.1056/NEJMms2009984
7 Topol, Eric J. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019.
8 European Parliament and Council. Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union, 2016.
9 Beauchamp, Tom L., and James F. Childress. Principles of Biomedical Ethics. 8th ed., Oxford University Press, 2019.
10 LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. Deep Learning. In: Nature, vol. 521, no. 7553, 2015, pp. 436-444. https://doi.org/10.1038/nature14539
11 Obermeyer, Ziad, and Ezekiel J. Emanuel. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. In: The New England Journal of Medicine, vol. 375, no. 13, 2016, pp. 1216-1219. https://doi.org/10.1056/NEJMp1606181
12 Esteva, Andre, et al. A Guide to Deep Learning in Healthcare. In: Nature Medicine, vol. 25, no. 1, 2019, pp. 24-29. https://doi.org/10.1038/s41591-018-0316-z
13 European Parliament and Council. Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union, 2016.
14 Rudin, Cynthia. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. In: Nature Machine Intelligence, vol. 1, no. 5, 2019, pp. 206-215. https://doi.org/10.1038/s42256-019-0048-x
15 European Parliament and Council. Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union, 2016.
16 Ibidem.
17 The European Commission's legislative proposal for the regulation of artificial intelligence (2021) is known as the 'AI Act.'
18 Jobin, Anna, Marcello Ienca, and Effy Vayena. The Global Landscape of AI Ethics Guidelines. In: Nature Machine Intelligence, vol. 1, no. 9, 2019, pp. 389-399. https://doi.org/10.1038/s42256-019-0088-2
19 Beauchamp, Tom L., and James F. Childress. In: Principles of Biomedical Ethics. 8th ed., Oxford University Press, 2019.
20 LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. Deep Learning. In: Nature, vol. 521, no. 7553, 2015, pp. 436-444. https://doi.org/10.1038/nature14539
21 Esteva, Andre, et al. A Guide to Deep Learning in Healthcare. In: Nature Medicine, vol. 25, no. 1, 2019, pp. 24-29. https://doi.org/10.1038/s41591-018-0316-z
22 McKinney, Scott M., et al. International Evaluation of an AI System for Breast Cancer Screening. In: Nature, vol. 577, no. 7788, 2020, pp. 89-94. https://doi.org/10.1038/d41586-019-03822-8
23 Arrieta, Alejandro Barredo, et al. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI. Information Fusion, vol. 58, 2020, pp. 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
24 Weiskopf, Nicole G., and Chunhua Weng. Methods and Dimensions of Electronic Health Record Data Quality Assessment: Enabling Reuse for Clinical Research. Journal of the American Medical Informatics Association, vol. 20, no. 1, 2013, pp. 144-151. https://doi.org/10.1136/amiajnl-2011-000681
25 European Parliament and Council. Regulation (EU) 2016/679 (General Data Protection Regulation ). Official Journal of the European Union, 2016.
26 Topol, Eric J. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019.
27 Obermeyer, Ziad, and Ezekiel J. Emanuel. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. In: The New England Journal of Medicine, vol. 375, no. 13, 2016, pp. 1216-1219. https://doi.org/10.1056/NEJMp1606181
28 Esteva, Andre, et al. A Guide to Deep Learning in Healthcare. In: Nature Medicine, vol. 25, no. 1, 2019, pp. 24-29. https://doi.org/10.1016/j.ogrm.2018.12.005
29 Weiskopf, Nicole G., and Chunhua Weng. Methods and Dimensions of Electronic Health Record Data Quality Assessment: Enabling Reuse for Clinical Research. In: Journal of the American Medical Informatics Association, vol. 20, no. 1, 2013, pp. 144-151. https://doi.org/10.1136/amiajnl-2011-000681
30 European Parliament and Council. Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union, 2016.
31 Samek, Wojciech, et al. Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. In: ITU Journal: ICT Discoveries, special issue 1, 2018, pp. 39-48.
32 Swan, Melanie. Blockchain: Blueprint for a New Economy. O'Reilly Media, 2015.
33 Floridi, Luciano. The Philosophy of Information. Oxford University Press, 2011. https://doi.org/10.1002/9781444396836.ch10
34 Goodman, Bryce, and Seth Flaxman. European Union Regulations on Algorithmic Decision-Making and a 'Right to Explanation'. In: AI Magazine, vol. 38, no. 3, 2017, pp. 50-57. https://doi.org/10.1609/aimag.v38i3.2741
35 Bauder, Richard A. and Khoshgoftaar, Taghi M. A Study on Rare Fraud Predictions with Big Medicare Claims Fraud Data. 1 Jan. 2020: 141-161. https://doi.org/10.3233/IDA-184415
36 McKinney, Scott M., et al. International Evaluation of an AI System for Breast Cancer Screening. In: Nature, vol. 577, no. 7788, 2020, pp. 89-94. https://doi.org/10.1038/d41586-019-03822-8
37 Weiskopf, Nicole G., and Chunhua Weng. Methods and Dimensions of Electronic Health Record Data Quality Assessment: Enabling Reuse for Clinical Research. In: Journal of the American Medical Informatics Association, vol. 20, no. 1, 2013, pp. 144-151. https://doi.org/10.1136/amiajnl-2011-000681
38 Goodman, Bryce, and Seth Flaxman. European Union Regulations on Algorithmic Decision-Making and a 'Right to Explanation'. In: AI Magazine, vol. 38, no. 3, 2017, pp. 50-57. https://doi.org/10.1609/aimag.v38i3.2741
39 Samek, Wojciech, et al. Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. In: ITU Journal: ICT Discoveries, special issue 1, 2018, pp. 39-48.
40 European Parliament and Council. Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union, 2016.
41 Bates, David W., et al. Big Data in Health Care: Using Analytics to Identify and Manage High-Risk and High-Cost Patients. In: Health Affairs, vol. 33, no. 7, 2014, pp. 1123-1131. https://doi.org/10.1377/hlthaff.2014.0041
42 Rudin, Cynthia. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. In: Nature Machine Intelligence, vol. 1, no. 5, 2019, pp. 206-215. https://doi.org/10.1038/s42256-019-0048-x
43 Kahn, Michael G., et al. Transparent Reporting of Data Quality in Distributed Data Networks. In: EGEMS, vol. 3, no. 1, 2015, p. 7. https://doi.org/10.13063/2327-9214.1052
44 Goodman, Bryce, and Seth Flaxman. European Union Regulations on Algorithmic Decision-Making and a 'Right to Explanation'. In: AI Magazine, vol. 38, no. 3, 2017, pp. 50-57.https://doi.org/10.1609/aimag.v38i3.2741
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