Lorenzini, Giorgia. Medical Artificial Intelligence and related ethics issues. 2023, Doctoral Thesis, University of Basel, Faculty of Medicine.
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Abstract
Chapter 1 introduces the topics and concepts discussed in the following Chapters of this thesis.
In particular, it presents medical artificial intelligence (MAI) and it argues that it bears the potential to
affect the doctor-patient relationship, both doctors’ and patients’ autonomy, patients’ safety,
cybersecurity, and shared decision-making (SDM). It concludes by highlighting the importance of a
reflection on how we talk about MAI since our narratives have a performative power and can therefore
influence its uptake and development.
Chapter 2 gives an overview of the methodologies used for the present research. It also aims to
explain how theoretical and empirical research can be combined in bioethics while showing the benefits
of this approach. Eventually, this thesis is based on what is known as empirical bioethics, although
theoretical work is prevalent.
Chapter 3 analyses the role of AI-based clinical decision support systems (CDSS) for shared
decision-making (SDM) to better comprehend its promise and associated ethical issues. Artificial
intelligence (AI) based CDSS are becoming ever more widespread in healthcare and could play an
important role in diagnostic and treatment processes. For this reason, AI-based CDSS has an impact on
the doctor-patient relationship, shaping their decisions with its suggestions. We may be on the verge of
a paradigm shift, where the doctor-patient relationship is no longer a dual relationship, but a triad.
Moreover, Chapter 3 investigates how certain AI implementations may instead foster the inappropriate
paradigm of paternalism. Understanding how AI relates to doctors and influences doctor-patient
communication is essential to promoting more ethical medical practice. Both doctors’ and patients’
autonomy need to be considered in the light of AI.
Informed consent is at the core of the clinical relationship. With the introduction of machine
learning (ML) in healthcare, the role of informed consent is challenged. Chapter 4 addresses the issue
of whether patients must be informed about medical ML applications and asked for consent. It aims to
expose the discrepancy between ethical and practical considerations while arguing that this polarization
is a false dichotomy: in reality, ethics is applied to specific contexts and situations. Bridging this gap
and considering the whole picture is essential for advancing the debate. In light of the possible future
developments of the situation and the technologies, as well as the benefits that informed consent for ML
can bring to shared decision-making, Chapter 4 concludes that it is necessary to prepare the ground for
a future requirement of informed consent for medical ML.
Healthcare cybersecurity is increasingly targeted by malicious hackers. This sector has many
vulnerabilities and health data is very sensitive and valuable. Consequently, any damage caused by
malicious intrusions is particularly alarming. The consequences of these attacks can be enormous and
endanger patient care. Amongst the already-implemented cybersecurity measures and the ones that need to be further improved, Chapter 5 aims to demonstrate how penetration tests can greatly benefit
healthcare cybersecurity. It is already proven that this approach has enforced cybersecurity in other
sectors. However, it is not popular in healthcare since many prejudices still surround the hacking practice
and there is a lack of education on hackers’ categories and their ethics. Chapter 5 analyses hacker ethics
to comprehend who ethical hackers are. Currently, hacker ethics has the status of personal ethics;
however, to employ penetration testers in healthcare, it is recommended to draft an official code of
ethics, comprising principles, standards, expectations, and best practices. Additionally, it is important
to distinguish between malicious hackers and ethical hackers. Amongst the latter, penetration testers are
only a sub-category. Acknowledging the subtle differences between ethical hackers and penetration
testers allows to better understand why and how the latter can offer their services to healthcare facilities.
The discourse surrounding medical artificial intelligence (AI) often focuses on narratives that
either hype the technology's potential or predict dystopian futures. AI narratives have a significant
influence on the direction of research, funding, and public opinion and thus shape the future of medicine.
Chapter 6 aims to offer critical reflections on AI narratives, with a specific focus on medical AI. This
Chapter raises awareness as to how people working with medical AI talk about AI and discharge their
‘narrative responsibility’. Qualitative semi-structured interviews were conducted with participants from
different disciplines who were exposed to medical AI. The research represents a secondary analysis of
data using a thematic narrative approach. Stories about the AI-doctor interaction depicted either a
competitive or collaborative relationship. Some participants argued that AI might replace doctors as it
performs better than physicians. However, others believed that doctors should not be replaced and that
AI should rather assist and support physicians. The idea of excessive technological deferral and
automation bias was discussed, highlighting the risk of ‘losing’ decisional power. The possibility that
AI could relieve doctors from burnout and allow them to spend more time with patients was also
considered. Finally, a few participants reported an extremely optimistic account of medical AI while the
majority criticized this type of story. The latter lamented the existence of a ‘magical theory’ of medical
AI, identified with techno-solutionist positions. The majority of the participants reported a nuanced view
of technology, recognizing both its benefits and challenges, and avoiding polarized narratives. However,
some participants did contribute to the hype surrounding medical AI, comparing it to human capabilities
and depicting it as superior. Overall, the majority agreed that medical AI should assist rather than replace
clinicians. Chapter 6 concludes that a balanced narrative (that focuses on the technology's present
capabilities and limitations) is necessary to fully realize the potential of medical AI while avoiding
unrealistic expectations and hype.
Finally, Chapter 7 provides a summary of the recommendations and conclusions presented in
the previous chapters. It indicates further research directions and limitations while concisely
recommending the next steps for ethically implementing MAI. The final considerations centre on
patients’ safety and rights, which should always be prioritised.
In particular, it presents medical artificial intelligence (MAI) and it argues that it bears the potential to
affect the doctor-patient relationship, both doctors’ and patients’ autonomy, patients’ safety,
cybersecurity, and shared decision-making (SDM). It concludes by highlighting the importance of a
reflection on how we talk about MAI since our narratives have a performative power and can therefore
influence its uptake and development.
Chapter 2 gives an overview of the methodologies used for the present research. It also aims to
explain how theoretical and empirical research can be combined in bioethics while showing the benefits
of this approach. Eventually, this thesis is based on what is known as empirical bioethics, although
theoretical work is prevalent.
Chapter 3 analyses the role of AI-based clinical decision support systems (CDSS) for shared
decision-making (SDM) to better comprehend its promise and associated ethical issues. Artificial
intelligence (AI) based CDSS are becoming ever more widespread in healthcare and could play an
important role in diagnostic and treatment processes. For this reason, AI-based CDSS has an impact on
the doctor-patient relationship, shaping their decisions with its suggestions. We may be on the verge of
a paradigm shift, where the doctor-patient relationship is no longer a dual relationship, but a triad.
Moreover, Chapter 3 investigates how certain AI implementations may instead foster the inappropriate
paradigm of paternalism. Understanding how AI relates to doctors and influences doctor-patient
communication is essential to promoting more ethical medical practice. Both doctors’ and patients’
autonomy need to be considered in the light of AI.
Informed consent is at the core of the clinical relationship. With the introduction of machine
learning (ML) in healthcare, the role of informed consent is challenged. Chapter 4 addresses the issue
of whether patients must be informed about medical ML applications and asked for consent. It aims to
expose the discrepancy between ethical and practical considerations while arguing that this polarization
is a false dichotomy: in reality, ethics is applied to specific contexts and situations. Bridging this gap
and considering the whole picture is essential for advancing the debate. In light of the possible future
developments of the situation and the technologies, as well as the benefits that informed consent for ML
can bring to shared decision-making, Chapter 4 concludes that it is necessary to prepare the ground for
a future requirement of informed consent for medical ML.
Healthcare cybersecurity is increasingly targeted by malicious hackers. This sector has many
vulnerabilities and health data is very sensitive and valuable. Consequently, any damage caused by
malicious intrusions is particularly alarming. The consequences of these attacks can be enormous and
endanger patient care. Amongst the already-implemented cybersecurity measures and the ones that need to be further improved, Chapter 5 aims to demonstrate how penetration tests can greatly benefit
healthcare cybersecurity. It is already proven that this approach has enforced cybersecurity in other
sectors. However, it is not popular in healthcare since many prejudices still surround the hacking practice
and there is a lack of education on hackers’ categories and their ethics. Chapter 5 analyses hacker ethics
to comprehend who ethical hackers are. Currently, hacker ethics has the status of personal ethics;
however, to employ penetration testers in healthcare, it is recommended to draft an official code of
ethics, comprising principles, standards, expectations, and best practices. Additionally, it is important
to distinguish between malicious hackers and ethical hackers. Amongst the latter, penetration testers are
only a sub-category. Acknowledging the subtle differences between ethical hackers and penetration
testers allows to better understand why and how the latter can offer their services to healthcare facilities.
The discourse surrounding medical artificial intelligence (AI) often focuses on narratives that
either hype the technology's potential or predict dystopian futures. AI narratives have a significant
influence on the direction of research, funding, and public opinion and thus shape the future of medicine.
Chapter 6 aims to offer critical reflections on AI narratives, with a specific focus on medical AI. This
Chapter raises awareness as to how people working with medical AI talk about AI and discharge their
‘narrative responsibility’. Qualitative semi-structured interviews were conducted with participants from
different disciplines who were exposed to medical AI. The research represents a secondary analysis of
data using a thematic narrative approach. Stories about the AI-doctor interaction depicted either a
competitive or collaborative relationship. Some participants argued that AI might replace doctors as it
performs better than physicians. However, others believed that doctors should not be replaced and that
AI should rather assist and support physicians. The idea of excessive technological deferral and
automation bias was discussed, highlighting the risk of ‘losing’ decisional power. The possibility that
AI could relieve doctors from burnout and allow them to spend more time with patients was also
considered. Finally, a few participants reported an extremely optimistic account of medical AI while the
majority criticized this type of story. The latter lamented the existence of a ‘magical theory’ of medical
AI, identified with techno-solutionist positions. The majority of the participants reported a nuanced view
of technology, recognizing both its benefits and challenges, and avoiding polarized narratives. However,
some participants did contribute to the hype surrounding medical AI, comparing it to human capabilities
and depicting it as superior. Overall, the majority agreed that medical AI should assist rather than replace
clinicians. Chapter 6 concludes that a balanced narrative (that focuses on the technology's present
capabilities and limitations) is necessary to fully realize the potential of medical AI while avoiding
unrealistic expectations and hype.
Finally, Chapter 7 provides a summary of the recommendations and conclusions presented in
the previous chapters. It indicates further research directions and limitations while concisely
recommending the next steps for ethically implementing MAI. The final considerations centre on
patients’ safety and rights, which should always be prioritised.
Advisors: | Elger, Bernice Simone |
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Committee Members: | Shaw, David and Sterckx, Sigrid |
Faculties and Departments: | 08 Cross-disciplinary Subjects > Ethik > Institut für Bio- und Medizinethik > Bio- und Medizinethik (Elger) 03 Faculty of Medicine > Departement Public Health > Ethik in der Medizin > Bio- und Medizinethik (Elger) |
UniBasel Contributors: | Elger, Bernice Simone and Shaw, David |
Item Type: | Thesis |
Thesis Subtype: | Doctoral Thesis |
Thesis no: | 15603 |
Thesis status: | Complete |
Number of Pages: | 126 |
Language: | English |
Identification Number: |
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edoc DOI: | |
Last Modified: | 30 Jan 2025 11:56 |
Deposited On: | 27 Jan 2025 11:12 |
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