Idée - Medical Artificial Intelligence - # Ethical Considerations of AI in Oncology

Oncologists Express Ethical Concerns About Integrating AI into Cancer Treatment Decisions

Q: How can oncologists be better equipped to identify and mitigate bias in AI models used for cancer treatment decisions?

Oncologists can enhance their ability to recognize and address bias in AI models by undergoing specialized training on AI ethics and bias detection. This training should focus on understanding how bias can manifest in AI algorithms, such as data selection bias or algorithmic bias. Additionally, oncologists should collaborate closely with data scientists and AI experts to gain insights into the development and validation processes of AI models. By actively participating in the design and evaluation of AI tools, oncologists can better identify potential biases and work towards mitigating them. Regular audits and reviews of AI algorithms should be conducted to ensure fairness and accuracy in treatment recommendations. Furthermore, establishing clear guidelines and protocols for evaluating and addressing bias in AI systems can help oncologists navigate the ethical challenges associated with using AI in cancer care.

Q: What are the potential legal and ethical implications if an AI-driven treatment recommendation leads to a worse outcome for the patient compared to the oncologist's recommendation?

If an AI-driven treatment recommendation results in a poorer outcome for the patient compared to the oncologist's recommendation, there could be significant legal and ethical ramifications. From a legal standpoint, the oncologist may face liability issues if the patient experiences harm due to following the AI's recommendation. This could lead to malpractice claims or lawsuits against the healthcare provider. Ethically, the oncologist may question the reliability and trustworthiness of AI systems in making critical treatment decisions. Patients may also lose confidence in AI technology, impacting its acceptance and adoption in clinical practice. Additionally, there could be concerns about accountability and responsibility in cases where AI-driven decisions lead to adverse outcomes, raising questions about the autonomy of healthcare professionals and the role of AI in decision-making processes.

Q: How can the medical community and AI developers work together to improve transparency and shared understanding of AI systems in a way that empowers both oncologists and patients to make informed decisions?

Collaboration between the medical community and AI developers is essential to enhance transparency and foster a shared understanding of AI systems in cancer care. To achieve this, stakeholders should prioritize open communication and knowledge-sharing to demystify AI algorithms and their implications for treatment decisions. Establishing interdisciplinary teams comprising oncologists, data scientists, ethicists, and patient advocates can facilitate a holistic approach to AI integration in oncology. Regular forums, workshops, and training sessions can be organized to educate healthcare professionals and patients about the capabilities and limitations of AI technology. Furthermore, promoting ethical guidelines and standards for AI development and deployment in healthcare settings can ensure that oncologists and patients are equipped to critically evaluate AI-driven recommendations. By fostering a culture of transparency, collaboration, and education, the medical community and AI developers can empower stakeholders to make informed decisions and navigate the complexities of AI in cancer care effectively.

Concepts de base

Oncologists have significant ethical concerns about the use of AI in cancer care, including issues with bias, transparency, and responsibility for AI-driven treatment recommendations.

Résumé

The article discusses the results of a survey of 204 US oncologists regarding their views on the ethical implications of using AI for cancer care. Key findings include:

Most oncologists (81%) support obtaining patient consent before using AI models in treatment decisions, and 85% feel they need to be able to explain the AI model to patients. However, only 23% believe patients should also be able to understand how the AI works.
When an AI model recommends a different treatment than the oncologist, the most common approach (36.8%) is to present both options and let the patient decide. About 34% would recommend the oncologist's regimen, while 22% would recommend the AI's regimen.
While 76.5% of oncologists agree they should protect patients from biased AI, only 27.9% feel confident they can identify biased AI models.
Most oncologists (91%) believe AI developers are responsible for medico-legal issues with AI, while less than half (47%) say oncologists or hospitals (43%) share this responsibility.

The authors conclude that the ethical adoption of AI in oncology will require rigorous assessment of its impact on care decisions and clear delineation of responsibility when problems arise.

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www.medscape.com

Stats

81% of oncologists support obtaining patient consent before using AI models in treatment decisions.
85% of oncologists feel they need to be able to explain the AI model to patients.
23% of oncologists believe patients should also be able to understand how the AI works.
36.8% of oncologists would present both the AI's and their own treatment recommendations and let the patient decide.
34% of oncologists would present both options but recommend their own treatment regimen.
22% of oncologists would present both options but recommend the AI's regimen.
76.5% of oncologists agree they should protect patients from biased AI.
27.9% of oncologists feel confident they can identify biased AI models.
91% of oncologists believe AI developers are responsible for medico-legal issues with AI.
47% of oncologists say they share responsibility for medico-legal issues with AI.
43% of oncologists say hospitals share responsibility for medico-legal issues with AI.

Citations

"Together, these data characterize barriers that may impede the ethical adoption of AI into cancer care. The findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions, as well as decisional responsibility when problems related to AI use arise."

Idées clés tirées de

Oncologists Voice Ethical Concerns Over AI in Cancer Care

by Megan Brooks à www.medscape.com 04-12-2024

https://www.medscape.com/viewarticle/oncologists-voice-ethical-concerns-over-ai-cancer-care-2024a100071i

Oncologists Voice Ethical Concerns Over AI in Cancer Care

Questions plus approfondies

How can oncologists be better equipped to identify and mitigate bias in AI models used for cancer treatment decisions?

Oncologists can enhance their ability to recognize and address bias in AI models by undergoing specialized training on AI ethics and bias detection. This training should focus on understanding how bias can manifest in AI algorithms, such as data selection bias or algorithmic bias. Additionally, oncologists should collaborate closely with data scientists and AI experts to gain insights into the development and validation processes of AI models. By actively participating in the design and evaluation of AI tools, oncologists can better identify potential biases and work towards mitigating them. Regular audits and reviews of AI algorithms should be conducted to ensure fairness and accuracy in treatment recommendations. Furthermore, establishing clear guidelines and protocols for evaluating and addressing bias in AI systems can help oncologists navigate the ethical challenges associated with using AI in cancer care.

What are the potential legal and ethical implications if an AI-driven treatment recommendation leads to a worse outcome for the patient compared to the oncologist's recommendation?

If an AI-driven treatment recommendation results in a poorer outcome for the patient compared to the oncologist's recommendation, there could be significant legal and ethical ramifications. From a legal standpoint, the oncologist may face liability issues if the patient experiences harm due to following the AI's recommendation. This could lead to malpractice claims or lawsuits against the healthcare provider. Ethically, the oncologist may question the reliability and trustworthiness of AI systems in making critical treatment decisions. Patients may also lose confidence in AI technology, impacting its acceptance and adoption in clinical practice. Additionally, there could be concerns about accountability and responsibility in cases where AI-driven decisions lead to adverse outcomes, raising questions about the autonomy of healthcare professionals and the role of AI in decision-making processes.

How can the medical community and AI developers work together to improve transparency and shared understanding of AI systems in a way that empowers both oncologists and patients to make informed decisions?

Collaboration between the medical community and AI developers is essential to enhance transparency and foster a shared understanding of AI systems in cancer care. To achieve this, stakeholders should prioritize open communication and knowledge-sharing to demystify AI algorithms and their implications for treatment decisions. Establishing interdisciplinary teams comprising oncologists, data scientists, ethicists, and patient advocates can facilitate a holistic approach to AI integration in oncology. Regular forums, workshops, and training sessions can be organized to educate healthcare professionals and patients about the capabilities and limitations of AI technology. Furthermore, promoting ethical guidelines and standards for AI development and deployment in healthcare settings can ensure that oncologists and patients are equipped to critically evaluate AI-driven recommendations. By fostering a culture of transparency, collaboration, and education, the medical community and AI developers can empower stakeholders to make informed decisions and navigate the complexities of AI in cancer care effectively.