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Emerging Technologies and Health

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Innovation in Scientific Research and Emerging Technologies

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Abstract

The exponential growth of emerging technologies opens up new opportunities for knowledge and its application, on human being, forcing us at the same time to rethink some traditional ethical and legal categories, such as freedom, responsibility, conscience, will, intention. The chapter focuses on neuroscience and neurotechnologies, gene-editing and genome-wide tests, the new paradigm of the 4P medicine (prediction, precision, personalization, participation), citizen science, the use of information and communication technologies, big data, mobile-health and biometrics, related to health and healthcare, underlining the main ethical and legal challenges. 

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Notes

  1. 1.

    Bird (2005), p. 1310; Wolpe (2004), pp. 1894–1898.

  2. 2.

    Libet (1999), pp. 47–57; Libet et al. (1983), pp. 623–642; Libet (1985), pp. 529–566.

  3. 3.

    Cf. Levy (2007).

  4. 4.

    Cf. Spence (1996), pp. 75–90; Pockett (2004), pp. 23–40; Tancredi (2005); Greene and Cohen (2004), pp. 1775–1785. In this last essay it is explicitly stated that neuroscience is progressively undermining the concept of legal responsibility.

  5. 5.

    In this sense the criticism of Clarke (1999), pp. 279–293. It should be noted, incidentally, that the denial of temporal-causal isomorphism highlighted by Libet’s experiments can also be understood as a denial of reductionism: the rules of the mind do not identify sic et simpliciter with those of the brain.

  6. 6.

    Cf. Farah and Heberlein (2007), pp. 37–48: Farah (2002), pp. 1123–1129; Farah (2007), pp. 363–364.

  7. 7.

    Vincent (2010), pp. 77–98; Damasio (2005); Damasio (2007), pp. 3–7; Illes (2006); De Caro and MacArthur (2004).

  8. 8.

    Garland (2004); Gazzaniga (2008), pp. 412–415.

  9. 9.

    Asilomar (1975)

  10. 10.

    Baltimore et al. (2015), pp. 36–38.

  11. 11.

    Lanphier et al. (2015).

  12. 12.

    The document on Human Genome Editing: Science, Ethics and Governance addresses different aspects of the applications of gene-editing on human subjects, from laboratory experiments on somatic cells and germ cells and embryos to possible clinical trials in adults. Committee on Human Gene Editing (2017).

  13. 13.

    Kaplan and Roy (2002).

  14. 14.

    Cf. Citi GPS Global Perspectives and Solutions (2016), p. 935.

  15. 15.

    In the text (p. 60) it states: “Important scientific and clinical issues relevant to human fertility and reproduction require continued laboratory research on human gametes and their progenitors, human embryos and pluripotent stem cells. This research is necessary for medical and scientific purposes that are not directed at heritable genome editing, though it will also provide valuable information and techniques that could be applied if heritable genome editing were to be attempted in the future”.

  16. 16.

    See also: Nuffield Council on Bioethics (2016, 2018); Italian Committee for Bioethics (2017a). The European Group on Ethics in Science and New Technologies is currently working in order to elaborate an Opinion on the topics.

  17. 17.

    Araki and Ishii (2014) and Jasanoff et al. (2015).

  18. 18.

    See the Opinions of the Italian Committee for Bioethics (1999, 2006, 2017b).

  19. 19.

    Van El et al. (2013).

  20. 20.

    Knoppers (2014), pp. 6–10.

  21. 21.

    Christenhusz (2013), pp. 248–255; Hehir-Kwa et al. (2015), pp. 1601–1606; Hellenic National Bioethics Commission (2015). In the Meeting Report it is stressed that the use of the expression incidental findings includes (1) unexpected positive findings, but also (2) the intentional search for pathogenic variants not associated with the primary diagnostic query. It is considered that the use of a different term, for example, “unexpected” or “secondary” or “unsolicited” findings, is just as problematic and therefore it is advised to keep to the most common use of ‘incidental findings’.

  22. 22.

    Presidential Commission for the Study of Bioethical Issues (2012, 2013). The Commission distinguishes between “primary”, “secondary” and “discovery findings”: “primary findings” refer to a result that is actively sought, using a test or procedure designed to find such result; the “secondary findings” refer to the results that are actively sought by a professional, but which are not the “primary target”; the “discovery findings” refer to the results of wide tests, aimed at detecting any potentially interesting data.

  23. 23.

    UNESCO, International Bioethics Committee (2015).

  24. 24.

    Flores et al. (2013), pp. 565–576; Hood and Flores (2012), pp. 613–624; Prainsack (2018).

  25. 25.

    Collins and Varmus (2015), p. 26; Shringarpure and Busamante (2015), pp. 1–18.

  26. 26.

    National Research Council (2011).

  27. 27.

    Commission staff working document (2013), p. 436.

  28. 28.

    President Obama State of the Union Address, January 30th 2015.

  29. 29.

    Weber (2014), pp. 2–3; Joyner and Paneth (2015); Coote and Joyner (2015).

  30. 30.

    Bayer and Galea (2015), pp. 499–501.

  31. 31.

    European Group on Ethics in Science and New Technologies (2015).

  32. 32.

    Mueller et al. (2013), Prainsack (2014) and Seife (2013).

  33. 33.

    PatientsLikeMe is a platform for sharing information and disease experiences; it enables patients to connect with patients of the same illness and encourages patients to share data and information. Members (more than 300,000) may choose different privacy settings that may be changed in time: shared data are accessible to third parties, non-shared data are not. The website reports aggregated data on symptoms and treatments that may be useful to patients. It is founded on an ‘openness philosophy’.

  34. 34.

    PGP website: http://www.personalgenomes.org/.

  35. 35.

    MIT Technology Review (2013).

  36. 36.

    Frost et al. (2011); Kaye et al. (2012), pp. 371–376; Vayena and Tasioulas (2015), pp. 479–485.

  37. 37.

    The European Group on Ethics in Science and New Technologies (2012) has identified some ethical guidelines for the establishment of rules in this sensitive sector. See also (1999).

  38. 38.

    UNESCO (2011) and Italian Committee for Bioethics (2006).

  39. 39.

    In sites: national (health ministry, Scientific Societies, University) or international (WHO, EMEA, NIH, Medline, FDA; pharmaceutical industry sites; patient association sites).

  40. 40.

    Cf. Müller et al. (2016), pp. 172–177.

  41. 41.

    Mayer-Schönberger and Cukier (2013); Bowker (2014), pp. 1795–1799; Mittelstadt and Floridi (2016), pp. 303–341; Cohen et al. (2018).

  42. 42.

    There is no consolidated literature on the topic. There are documents and opinions of international and national ethics committees, which may contribute to the development of an ethical framework in this analysis. Among the main documents, it is worthwhile recalling: European Group on Ethics in Science and New Technologies (2015) and Opinion 7/2015 from the European Data Protection Supervisor on Meeting the Challenge of Big Data; UNESCO International Bioethics Committee (2017), OECD (2013, 2017), Italian Committee for Bioethics (2016) and Nuffield Council on Bioethics (2015).

  43. 43.

    Raghupathil and Raghupathi (2014). There is a discussion on what forms of regulations could provide for data quality monitoring (collection, storage, analysis), as a requirement of data use, with flexible and updatable tools (as code of conducts), ensuring a control on competences and correctness of operators (clinicians, analysts, as engineers, statisticians, bioinformaticians) and correct interactions among them. Proposals include ‘soft regulation’ such as an updated code of practice for clinicians or other professionals involved in collecting health-related data. Others aim to foster interdisciplinarity between clinicians/researchers and engineers working together to translate and extend their existing and advanced data analysis technology (including on the one hand the clinically trained human mind), into targeted big data analytical approaches that will achieve clinically effective outputs. Although engineers and clinicians have long collaborated successfully, development work on “Big Data Healthcare” will particularly require mutual understanding by each disciplinary culture of the other. This will resort to further cultural development in both areas.

  44. 44.

    The information requested is of an heterogeneous nature. With specific regard to health-related data, consideration should also be given to the fact that boundaries between the strictly medical and non-medical spheres are becoming increasingly blurred, like those between health and society; information on lifestyles and behaviours tends to become increasingly more relevant to health even within the perspective of prevention. In this sense, health information is not only deemed to be the outcome of laboratory tests or epidemiological data, but also the general news that comes from social networks.

  45. 45.

    Bock (2016), p. 9.

  46. 46.

    Wyber et al. (2015), pp. 203–208.

  47. 47.

    WHO (2012), p. 27. Among the most significant documents on the subject adopted by the Council of Europe: Convention for the Protection of Individuals with Regard to Automatic Processing of Personal Data, Council of Europe, 1981 and additional protocol (Additional Protocol to Convention ETS No. 108 on Supervisory Authorities and Transborder Data Flows); Recommendation CM/Rec (2010)13 of the Committee of Ministers to Member States on the Protection of Individuals with Regard to Automatic Processing of Personal Data in the Context of Profiling (23 November 2010); Recommendation CM/Rec (2012)4 on the Protection of Human Rights with Regard to Social Networking Services; Recommendation CM/Rec (2014)6 on Human Rights for Internet Users; Recommendation CM/Rec (2016)1 on Protecting and Promoting the Right to Freedom of Expression and the Right to Private Life with Regard to Network Neutrality.

  48. 48.

    Hoeren (2014), pp. 751–754.

  49. 49.

    The delivery of healthcare services via mobile communication devices (2010 mHealth Summit FNIH) applies to both software and hardware, “Healthcare delivered wirelessly”.

  50. 50.

    Bert et al. (2014), p. 9995; Giacometti et al. (2013), pp. 249–259.

  51. 51.

    Examples of medical applications for health: ‘Welp’ to detect the paralysis often characterizing seizures; applications for preventing falling of older people; applications to identify the first signs of Parkinson’s disease; applications to manage certain diseases (HIV, diabetes, chronic conditions).

  52. 52.

    On these issues, there is already a wide-ranging debate: McCartney (2013), p. 181; Buijink et al. (2013), pp. 90–92; Haffey et al. (2013), pp. 111–117; Wolf et al. (2013), pp. 422–426. The problem that remains open on the practical level concerns the fact that the exponential rate of designing new apps makes it difficult to carry out the evaluation of the risk/benefit. Medical apps can work with different operating systems, so it is not possible to test them all for safety.

  53. 53.

    E.g. some applications for asthma, food diary, ‘my recovery’ applied to preparation management for operations and after surgery, for rehabilitation; use of games to control panic attacks; home HIV testing, sexual diseases, streptococcus.

  54. 54.

    E.g. if one collects data of the counting of the number of steps taken once only, such data reasonably will not lead to any inference. These are not data in a medical context and are not correlated with other data, therefore not relevant for research. But if they are systematically collected and combined with other data (e.g. gender difference, age, habits) they can become important for research.

  55. 55.

    This is the line of thought based on the interpretation of para. 107 of the Explanatory Memorandum Recommendation N° 97) 5 on the Protection of Medical Data: “But even in cases where his/her consent is not required—that is, when the collection and processing of medical data follow an obligation under the law or under a contract, are provided for or authorised by law, or when the consent requirement is dispensed with—the recommendation provides that the data subject is entitled to relevant information”. Also Article 29 Working Party has recently published a document On apps on smart devices, which emphasizes the need to inform in a clear and unambiguous way the way in which the data are used (data type, purpose, period) before installation of the app. The right to be informed is also expressed in art. 10 Directive 95/46/EC; art. 5.3 of the ePrivacy Directive 2002/58/EC.

  56. 56.

    Mantovani et al. (2013); Parker (2012), pp. 50–52; Siòlberman and Clark (2012).

  57. 57.

    Cf. Italian Committee for Bioethics (2014).

  58. 58.

    See Italian Committee for Bioethics (2015).

  59. 59.

    Identification (“who is this person?”) is the determination of a subject’s identity by comparing a measured biometric in a database of records (a one-to-many comparison); verification (“is this person who he claims to be?”) corresponds to a one-to-one comparison between a measured biometric and a particular person. All biometrics can be used for verification, only some may be used for identification.

  60. 60.

    Davis (1997) and Jain et al. (1998).

  61. 61.

    DNA analysis does not allow authentication in real time, as the other biometrics technologies. The temporal criterion is not covered in the definition of biometric technologies and therefore does not prevent to include DNA analysis among them.

  62. 62.

    Cf. Mordini and Petrini (2007), pp. 5–11. See also European Group on Ethics in Science and New Technologies (2014).

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  1. Department of Law, Economics, Politics and Modern Languages, LUMSA University, Rome, Italy

    Laura Palazzani

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  1. Laura Palazzani
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Palazzani, L. (2019). Emerging Technologies and Health. In: Innovation in Scientific Research and Emerging Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-16733-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-16733-2_3

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