Publications overestimating the medical and ecological sequels of a slight anthropogenic increase in the radiation background have been reviewed recently with examples of different organs and pathological conditions. The overestimation contributed to the strangulation of atomic energy. The use of nuclear energy for electricity production is on the agenda today due to the increasing energy needs of humankind. Apparently, certain scientific writers acted in the interests of fossil fuel producers. Health risks and environmental damage are maximal for coal and oil, lower for natural gas, and much lower for atomic energy. This letter is an addition to previously published materials, this time focused on studies of cataracts in radiation-exposed populations in Russia. Selection and self-selection bias are of particular significance. Apparently, the self-reporting rate correlates with dose estimates and/or with professional awareness about radiation-related risks among nuclear workers or radiologic technologists, the latter being associated with their work experience/duration and hence with the accumulated dose. Individuals informed of their higher doses would more often seek medical advice and receive more attention from medics. As a result, lens opacities are diagnosed in exposed people earlier than in the general population. This explains dose-effect correlations proven for the incidence of cataracts but not for the frequency of cataract surgeries. Along the same lines, various pathological conditions are more often detected in exposed people. Ideological bias and the trimming of statistics have not been unusual in the Russian medical sciences. It is known that ionizing radiation causes cataracts; however, threshold levels associated with risks are understudied. In particular, thresholds for chronic and fractionated exposures are uncertain and may be underestimated.

1. Shalf J. The future of computing beyond Moore’s Law. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2020; 378(2166): 20190061. doi: 10.1098/rsta.2019.0061

2. Wang Y, Zhao Y. Arbitrary spatial trajectory reconstruction based on a single inertial sensor. IEEE Sensors Journal 2023; 23(9): 10009–10022. doi: 10.1109/jsen.2023.3257867

3. Leiserson CE, Thompson NC, Emer JS, et al. There’s plenty of room at the top: What will drive computer performance after Moore’s law? Science 2020; 368(6495). doi: 10.1126/science.aam9744

4. Choi S, Yang J, Wang G. Emerging memristive artificial synapses and neurons for energy‐efficient neuromorphic computing. Advanced Materials 2020; 32(51). doi: 10.1002/adma.202004659

5. Al-Dujaili MJ, Al-dulaimi MA. Fifth-generation telecommunications technologies: Features, architecture, challenges and solutions. Wireless Personal Communications 2022; 128(1): 447–469. doi: 10.1007/s11277-022-09962-x

6. Wang Y, Zhao Y. Handwriting recognition under natural writing habits based on a low-cost inertial sensor. IEEE Sensors Journal 2023; 24(1): 995–1005. doi: 10.1109/jsen.2023.3331011

7. Zhang J, Tao D. Empowering things with intelligence: A survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet of Things Journal 2021; 8(10): 7789–7817. doi: 10.1109/jiot.2020.3039359

8. Benioff P. The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by turing machines. Journal of Statistical Physics 1980; 22(5): 563–591. doi: 10.1007/bf01011339

9. Chalmers DJ. The singularity: A philosophical analysis. In: Science Fiction and Philosophy: From Time Travel to Superintelligence. John Wiley & Sons, Inc.; 2016. pp. 171–224. doi: 10.1002/9781118922590.ch16

10. Fuller A, Fan Z, Day C, et al. Digital twin: Enabling technologies, challenges and open research. IEEE Access 2020; 8: 108952–108971. doi: 10.1109/access.2020.2998358

11. Rashidi P, Mihailidis A. A survey on ambient-assisted living tools for older adults. IEEE Journal of Biomedical and Health Informatics 2013; 17(3): 579–590. doi: 10.1109/jbhi.2012.2234129

12. Preskill J. Quantum computing in the NISQ era and beyond. Quantum 2018; 2: 79. doi: 10.22331/q-2018-08-06-79

13. Holevo AS. Bounds for the quantity of information transmitted by a quantum communication channel. Problemy Peredachi Informatsii 1973; 9(3): 3–11.

14. Shor PW. Quantum computing. Documenta Mathematica 1998; 1(1000): 467–486.

15. Feynman RP. Simulating physics with computers. International Journal of Theoretical Physics 1982; 21(6–7): 467–488. doi: 10.1007/bf02650179

16. Deutsch D. Quantum theory, the Church–Turing principle and the universal quantum computer. Proceedings of the Royal Society of London A Mathematical and Physical Sciences 1985; 400(1818): 97–117. doi: 10.1098/rspa.1985.0070

17. Deutsch D, Jozsa R. Rapid solution of problems by quantum computation. Proceedings of the Royal Society of London Series A: Mathematical and Physical Sciences 1992; 439(1907): 553–558. doi: 10.1098/rspa.1992.0167

18. Gurevich Y. Logic in computer science column. Bulletin of the EATCS 1989; 38: 93–100.

19. De Broglie L. The wave nature of the electron. Nobel Lecture 1929; 12: 244–256.

20. Heisenberg W. The Physical Principles of the Quantum Theory. Courier Corporation; 1949.

21. Joo J, Knight PL, O’Brien JL, et al. One-way quantum computation with four-dimensional photonic qudits. Physical Review A 2007; 76(5). doi: 10.1103/physreva.76.052326

22. Bennett CH, Bernstein E, Brassard G, et al. Strengths and weaknesses of quantum computing. SIAM Journal on Computing 1997; 26(5): 1510–1523. doi: 10.1137/s0097539796300933

23. Grover LK. A fast quantum mechanical algorithm for database search. In: Proceedings of the twenty-eighth annual ACM symposium on Theory of computing; 22–24 May 1996; Philadelphia Pennsylvania, USA. pp. 212–219. doi: 10.1145/237814.237866

24. Preskill J. Quantum information. In: Preskill J (editor). Quantum Shannon Theory. Cambridge University Press; 2016.

25. Fredkin E, Toffoli T. Conservative logic. International Journal of Theoretical Physics 1982; 21(3–4): 219–253. doi: 10.1007/bf01857727

26. DiVincenzo DP, Aliferis P. Effective fault-tolerant quantum computation with slow measurements. Physical Review Letters 2007; 98(2). doi: 10.1103/physrevlett.98.020501

27. Stoneham AM, Harker AH, Morley GW. Could one make a diamond-based quantum computer? Journal of Physics: Condensed Matter 2009; 21(36): 364222. doi: 10.1088/0953-8984/21/36/364222

28. Kreger-Stickles L, Oskin M. Microcoded architectures for ion-tap quantum computers. ACM SIGARCH Computer Architecture News 2008; 36(3): 165–176. doi: 10.1145/1394608.1382136

29. Solenov D, Brieler J, Scherrer JF. The potential of quantum computing and machine learning to advance clinical research and change the practice of medicine. Missouri Medicine 2018; 115(5): 463.

30.

31. Flöther FF. The state of quantum computing applications in health and medicine. Research Directions: Quantum Technologies 2023; e10: 1–10. doi: 10.1017/qut.2023.4

32. Landman J, Mathur N, Li YY, et al. Quantum methods for neural networks and application to medical image classification. Quantum 2022; 6: 881. doi: 10.22331/q-2022-12-22-881

33. Jun K. A highly accurate quantum optimization algorithm for CT image reconstruction based on sinogram patterns. Scientific Reports 2023; 13(1). doi: 10.1038/s41598-023-41700-6

34. Wegner KD, Hildebrandt N. Quantum dots: Bright and versatile in vitro and in vivo fluorescence imaging biosensors. Chemical Society Reviews 2015; 44(14): 4792–4834. doi: 10.1039/c4cs00532e

35. Dixon T. The grey zone of cyber-biological security. International Affairs 2021; 97(3): 685–702. doi: 10.1093/ia/iiab041

36. Wierzbiński M, Falcó-Roget J, Crimi A. Community detection in brain connectomes with hybrid quantum computing. Scientific Reports 2023; 13(1). doi: 10.1038/s41598-023-30579-y

37. Marchetti L, Nifosì R, Martelli PL, et al. Quantum computing algorithms: Getting closer to critical problems in computational biology. Briefings in Bioinformatics 2022; 23(6). doi: 10.1093/bib/bbac437

38. Outeiral C, Strahm M, Shi J, et al. The prospects of quantum computing in computational molecular biology. WIREs Computational Molecular Science 2020; 11(1). doi: 10.1002/wcms.1481

39. Kamel Boulos MN, Zhang P. Digital twins: From personalised medicine to precision public health. Journal of Personalized Medicine 2021; 11(8): 745. doi: 10.3390/jpm11080745

40. Hashizume M. Perspective for future medicine: Multidisciplinary computational anatomy-based medicine with artificial intelligence. Cyborg and Bionic Systems 2021; 2021. doi: 10.34133/2021/9160478

41. Wahl B, Cossy-Gantner A, Germann S, et al. Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Global Health 2018; 3(4): e000798. doi: 10.1136/bmjgh-2018-000798

42. Ye Z, Yang J, Zhong N, et al. Tackling environmental challenges in pollution controls using artificial intelligence: A review. Science of The Total Environment 2020; 699: 134279. doi: 10.1016/j.scitotenv.2019.134279

43. Freedman MH. P/NP, and the quantum field computer. Proceedings of the National Academy of Sciences 1998; 95(1): 98–101. doi: 10.1073/pnas.95.1.98

44. Miyadera T. Quantum Kolmogorov complexity and information-disturbance theorem. Entropy 2011; 13(4): 778–789. doi: 10.3390/e13040778

45. Bernstein DJ, Lange T. Post-quantum cryptography. Nature 2017; 549(7671): 188–194. doi: 10.1038/nature23461

46. Malina L, Dzurenda P, Ricci S, et al. Post-quantum era privacy protection for intelligent infrastructures. IEEE Access 2021; 9: 36038–36077. doi: 10.1109/access.2021.3062201