Искусственный интеллект в здравоохранении. Возможности патентной охраны таких разработок

В статье представлен обзор преимуществ и проблем, связанных с использованием искусственного интеллекта (ИИ) и машинного обучения (МО) в медицине. На основании анализа научных публикаций определены лидирующие направления в сфере здравоохранения, использующие ИИ и МО. Описаны прикладные задачи, которые позволяют решить современные технологии, а также цели, которые могут быть достигнуты при использовании таких технологий. Освещены вопросы правовой охраны технологий с применением ИИ. Дано сравнение ключевых аспектов авторского и патентного права, а также представлены преимущества патентного права и комплексной патентной охраны технологий автоматизации процессов в области здравоохранения. На конкретных примерах рассмотрены возможности комплексной патентной охраны и ее стратегия в лидирующих направлениях использования ИИ в области здравоохранения.

1. Schork N.J. Artificial intelligence and personalized medicine. Cancer Treat Res. 2019; 178: 265–83. https://doi.org/10.1007/978-3-030-16391-4_11.

2. Davenport T., Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019; 6 (2): 94–8. https://doi.org/10.7861/futurehosp.6-2-94.

3. Uddin M., Wang Y., Woodbury-Smith M. Artificial intelligence for precision medicine in neurodevelopmental disorders. NPJ Digit. Med. 2019; 2: 112. https://doi.org/10.1038/s41746-019-0191-0.

4. Mehta N., Pandit A., Shukla S. Transforming healthcare with big data analytics and artificial intelligence: a systematic mapping study. J Biomed Inform. 2019; 100: 103311. https://doi.org/10.1016/j.jbi.2019.103311.

5. El Naqa I., Kosorok M.R., Jin J., Mierzwa M., Ten Haken R.K. Prospects and challenges for clinical decision support in the era of big data. JCO Clin Cancer Inform. 2018; 2: CCI.18.00002. https://doi.org/10.1200/cci.18.00002.

6. Xu J., Yang P., Xue S., et al. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet. 2019; 138 (2): 109–24. https://doi.org/10.1007/s00439-019-01970-5.

7. Kermany D.S., Goldbaum M., Cai W., et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018; 172 (5): 1122–31e.9. https://doi.org/10.1016/j.cell.2018.02.010.

8. Esteva A., Kuprel B., Novoa R., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542 (7639): 115–8. https://doi.org/10.1038/nature21056.

9. Poplin R., Varadarajan A.V., Blumer K., et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018; 2: 158–64. https://doi.org/10.1038/s41551-018-0195-0.

10. Kantarjian H., Yu P.P. Artificial intelligence, big data, and cancer. JAMA Oncology. 2015; 1 (5): 573. https://doi.org/10.1001/jamaoncol.2015.1203.

11. Shimizu H., Nakayama K.I. Artificial intelligence in oncology. Cancer Sci. 2020; 111 (5): 1452–60. https://doi.org/10.1111/cas.14377.

12. Ehteshami Bejnordi B., Veta M., Johannes van Diest P., et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017; 318: 2199–2210. https://doi.org/10.1001/jama.2017.14585.

13. Ainscough B.J., Barnell E.K., Ronning P., et al. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat Genet. 2018; 50 (12): 1735–43. https://doi.org/10.1038/s41588-018-0257-y.

14. Chang P., Grinband J., Weinberg B.D., et al. Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am J Neuroradiol. 2018; 39 (7): 1201–7. https://doi.org/10.3174/ajnr.A5667.

15. Liang G., Fan W., Luo H., et al. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother. 2020; 128: 110255. https://doi.org/10.1016/j.biopha.2020.110255.

16. Siegersma K.R., Leiner T., Chew D.P., et al. Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist. Neth Heart J. 2019; 27 (9): 403–13. https://doi.org/10.1007/s12471-019-01311-1.

17. Tai A.M., Albuquerque A., Carmona N.E., et al. Machine learning and big data: implications for disease modeling and therapeutic discovery in psychiatry. Artif Intell Med. 2019; 99: 101704. https://doi.org/10.1016/j.artmed.2019.101704.

18. Brasil S., Pascoal C., Francisco R., et al. Artificial intelligence (AI) in rare diseases: is the future brighter? Genes (Basel). 2019; 10 (12): 978. https://doi.org/10.3390/genes10120978.

19. Kindle R.D., Badawi O., Celi L.A., Sturland S. Intensive care unit telemedicine in the era of big data, artificial intelligence, and computer clinical decision support systems. Critical Care Clinics. 2019; 35 (3): 483–95. https://doi.org/10.1016/j.ccc.2019.02.005.

20. Cosgriff C.V., Celi L.A., Stone D.J. Critical care, critical data. Biomed Eng Comput Biol. 2019; 10: 1179597219856564. https://doi.org/10.1177/1179597219856564.

21. Wang M., Xia C., Huang L., et al. Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation. Lancet Digit Health. 2020; 2 (10): e506–15. https://doi.org/10.1016/S2589-7500(20)30199-0.

22. Vardhanabhuti V. CT scan AI-aided triage for patients with COVID-19 in China. Lancet Digit Health. 2020; 2 (10): e494–5. https://doi.org/10.1016/S2589-7500(20)30222-3.

23. Liang W., Yao J., Chen A., et al. Early triage of critically ill COVID-19 patients using deep learning. Nat Commun. 2020; 11: 3543. https://doi.org/10.1038/s41467-020-17280-8.

24. Fernandes M., Vieira S.M., Leite F., et al. Clinical decision support systems for triage in the emergency department using intelligent systems: a review. Artif Intell Med. 2020; 102: 101762. https://doi.org/10.1016/j.artmed.2019.101762.

25. Kim H.S., Kim D.J., Yoon K.H. Medical big data is not yet available: why we need realism rather than exaggeration. Endocrinol Metab (Seoul). 2019; 34 (4): 349–54. https://doi.org/10.3803/EnM.2019.34.4.349.

26. Manrique de Lara A., Peláez-Ballestas I. Big data and data processing in rheumatology: bioethical perspectives. Clin Rheumatol. 2020; 39: 1007–14. https://doi.org/10.1007/s10067-020-04969-w.

27. Sloane E.B., Silva J.R. Artificial intelligence in medical devices and clinical decision support systems. In: Clinical engineering handbook. 2nd ed. Elsevier; 2020: 556–68. https://doi.org/10.1016/B978-0-12-813467-2.00084-5.