Artificial intelligence (AI) is transforming the field of healthcare, offering new possibilities for diagnosis, treatment, and prevention of diseases. AI systems can assist healthcare professionals in making decisions, improving efficiency, and reducing errors. AI can also empower patients to manage their own health and well-being, through personalized and interactive applications.
However, the use of AI in healthcare also poses significant ethical considerations and challenges that need to be addressed. How can we ensure that AI systems respect the privacy and security of sensitive health data? How can we ensure that AI systems are transparent, accountable, and fair in their actions and outcomes? How can we ensure that AI systems align with the values and preferences of patients, clinicians, and society? How can we ensure that AI systems do not harm or replace human relationships and interactions in healthcare?
In this article, I will explore some of these ethical considerations and challenges associated with the use of AI in healthcare. I will also discuss some of the initiatives and frameworks that aim to promote responsible AI development and deployment in healthcare. I invite you to join me on this journey of discovery and learning, as we explore the potential benefits and risks of AI in healthcare.
The Potential Benefits of AI in Healthcare
AI has the potential to revolutionize healthcare, by enhancing the quality, accessibility, and affordability of care. According to a report by Kalis et al. (2018), there are 10 promising AI applications in healthcare, including:
- Virtual nursing assistants: AI systems that can provide 24/7 support to patients, answering questions, monitoring symptoms, and providing reminders.
- Administrative workflow assistance: AI systems that can automate tasks such as scheduling appointments, billing, and documentation.
- Fraud detection: AI systems that can detect and prevent fraudulent claims and payments.
- Dosage error reduction: AI systems that can optimize drug dosing and prevent medication errors.
- Connected machines: AI systems that can enable remote monitoring and maintenance of medical devices and equipment.
- Clinical trial participant identifier: AI systems that can identify and recruit suitable candidates for clinical trials.
- Preliminary diagnosis: AI systems that can analyze symptoms and medical history to provide a preliminary diagnosis.
- Automated image diagnosis: AI systems that can interpret medical images such as X-rays, CT scans, and MRI scans to detect abnormalities.
- Treatment plan design: AI systems that can design personalized and optimal treatment plans based on patient data and preferences.
- Drug discovery: AI systems that can accelerate the process of discovering new drugs and therapies.
These applications demonstrate how AI can improve patient outcomes, enhance efficiency, and reduce costs in healthcare. For example, Laraki (2019) argues that technology alone won’t save healthcare but will redefine it. He discusses how AI can enable new possibilities such as:
- Predictive analytics: AI systems that can use data to predict future events such as disease outbreaks, hospital admissions, or patient deterioration.
- Precision medicine: AI systems that can tailor treatments to individual patients based on their genetic, environmental, and lifestyle factors.
- Digital therapeutics: AI systems that can deliver interventions through digital platforms such as apps or games to treat or prevent conditions such as depression or addiction.
Ethical Considerations and Challenges
However, the use of AI in healthcare also raises significant ethical considerations and challenges that need to be addressed. Rigby (2019) discusses some of the ethical dimensions of using AI in healthcare, highlighting the need for transparency, accountability, and fairness in the development and deployment of AI systems. Yu et al. (2018) provide an overview of some of the challenges associated with the use of AI in healthcare, including:
- Data privacy and security: The use of AI in healthcare requires the collection, integration, and analysis of large amounts of sensitive health data from various sources such as electronic health records, wearable devices, or social media. This poses risks to data privacy and security, as data could be accessed or misused by unauthorized parties. For example, Kharpal (2017) reports on a data-sharing deal between Google DeepMind and the UK National Health Service (NHS) that was deemed illegal by a watchdog. The article highlights concerns about data privacy and security in the use of AI in healthcare.
- Transparency: The use of AI in healthcare involves complex algorithms that may not be easily understandable or explainable to patients, clinicians, or other stakeholders. This poses challenges to transparency, as it may be difficult to understand how or why an AI system made a certain decision or recommendation. For example, Wakabayashi (2019) reports on a lawsuit against Google and the University of Chicago over data sharing. The article highlights concerns about transparency and accountability in the use of AI in healthcare.
- Fairness: The use of AI in healthcare involves data-driven algorithms that may reflect or amplify existing biases or inequalities in society. This poses challenges to fairness, as it may result in discrimination or harm to certain groups or individuals. For example, Mittelstadt (2019) analyzes some of the ethical challenges of algorithmic decision-making in healthcare, such as fairness, accountability, and human dignity. He discusses how algorithms may be biased by their data, design, or context, and how this may affect the quality and safety of care.
The Need for Cooperation and Collaboration
To address these ethical considerations and challenges, there is a need for cooperation and collaboration among various stakeholders, such as policymakers, regulators, developers, providers, and users of AI in healthcare. There is a need for establishing good governance frameworks that are fit for purpose, dynamic, and responsive to emerging challenges. There is also a need for developing ethical principles and guidelines that can guide the responsible development and deployment of AI in healthcare.
In this regard, there are some initiatives and frameworks that aim to promote responsible AI development and deployment in healthcare. For example:
- The G20 (2019) issued a ministerial statement on trade and digital economy, calling for cooperation among countries to promote innovation and growth while addressing challenges such as data privacy and security.
- The Pontifical Academy for Life (PAV) (2020) issued the Rome Call for AI Ethics, calling for the development of AI that is transparent, inclusive, socially beneficial, and accountable. In a previous post, I provided a comprehensive guide to the Rome Call for AI Ethics and its impact areas.
- The IEEE (2019) published Ethically Aligned Design Principles, providing guidance on ethical issues in AI development. In another post, I explored the IEEE’s Ethically Aligned Design Principles.
In this article, I have explored some of the potential benefits and risks of AI in healthcare. I have also discussed some of the ethical considerations and challenges associated with the use of AI in healthcare. I have also highlighted some of the initiatives and frameworks that aim to promote responsible AI development and deployment in healthcare.
AI in healthcare is not a silver bullet that can solve all the problems of the health system. Rather, it is a powerful tool that can augment human capabilities and enable new possibilities. However, it also requires a careful consideration of its ethical implications and a responsible approach to its development and deployment.
I hope you found this article informative and engaging. I invite you to join the conversation on Twitter by tweeting me @PaulWagle or by commenting on the article below. What are your thoughts on AI in healthcare? What are some of the ethical considerations and challenges that you think are important? How can we ensure that AI is used responsibly in healthcare?
- Kalis B., Collier M., Fu R. (2018). 10 promising AI applications in health care. Harvard Business Review. Retrieved from https://hbr.org/2018/05/10-promising-ai-applications-in-health-care
- Laraki O. (2019). Technology alone won’t save health care but it will redefine it. Forbes. Retrieved from https://www.forbes.com/sites/insights-intelai/2019/02/11/technology-alone-wont-save-healthcare-but-it-will-redefine-it/#5e7a96c44269
- Rigby M.J. (2019). Ethical dimensions of using artificial intelligence in health care. AMA Journal of Ethics 21: E121-E124. Retrieved from: https://journalofethics.ama-assn.org/article/ethical-dimensions-using-artificial-intelligence-health-care/2019-02
- Yu K.H., Beam A.L., Kohane I.S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering 2: 719-731.
- Kharpal A. (2017). Google DeepMind patient data deal with UK health service illegal, watchdog says. CNBC. Retrieved from
- Wakabayashi D. (2019). Google and the University of Chicago are sued over data sharing. The New York Times. Retrieved from https://www.nytimes.com/2019/06/26/technology/google-university-chicago-data-sharing-lawsuit.html
- Mittelstadt B.D. (2019). The ethics of algorithms: key problems and solutions. AI & Society 34: 543-569.
- IEEE (2019). Ethically aligned design: a vision for prioritizing human well-being with autonomous and intelligent systems (1st ed.). Retrieved from https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_v2.pdf
- PAV (2020). Rome call for AI ethics. Retrieved from http://www.academyforlife.va/content/dam/pav/documenti%20pdf/2020/CALL%2028%20febbraio/AI%20Rome%20Call%20x%20firma_DEF_DEF_.pdf
- G20 (2019). Ministerial statement on trade and digital economy. Retrieved from https://www.mofa.go.jp/files/000486596.pdf