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THE USE OF AI IN ONCOLOGY: ETHICAL CHALLENGES AND FUTURE DIRECTIONS

Recent studies highlight the ethical and operational challenges that AI is bringing to the field of oncology. A ’survey conducted at Harvard Medical School, published in JAMA Network Open, highlighted three points of consensus among oncologists regarding the use of AI :

AI models must be explainable by oncologists

Patients must give consent for the use of AI in their care 

Oncologists must protect patients from bias in AI tools

The survey, led by Dr. Andrew Hantel, involved 204 0ncologists from 37 states. Key findings include the fact that 37% of oncologists would let the patient decide whether to follow an AI recommendation contrary to their own opinion. However, although 77% of oncologists felt it was essential to protect patients from AI tool bias, only 28% felt confident in recognizing it.

In a follow-up article published in CA: A Cancer Journal for Clinicians, Dr. Shiraj Sen, who was not involved in the Harvard study, discussed future directions for AI in oncology. Sen identified three main areas of development:

1. Therapeutic Decisions:

The emergence of new therapeutic options provides oncologists with multiple treatment options, although these have often not been thoroughly studied. AI tools can integrate prognostic factors and biomarkers to help in this context.

2. Radiographic Response Assessment : 

AI tools are already supporting radiographic assessment in anticancer treatments. In the future, it could characterize tumor heterogeneity, predict treatment response, and guide personalized therapeutic strategies.

3. Identification and Evaluation of Clinical Trials : 

Less than 5% of cancer patients participate in clinical trials. AI could facilitate the identification of appropriate trials and assist in the preliminary assessment of patient eligibility.

Hantel stresses the importance of structured AI education and ethical guidelines for oncologists. It is important to develop infrastructure that supports physician education, ensuring transparency, consent, accountability, and equity. 

In addition, it is essential to understand the views of patients, especially those from historically marginalized groups, on these issues. The goal is to maximize the benefits and minimize the risks of AI in oncology by educating clinicians about AI models and the ethics of their use.