Page 138 - EJMO-9-1

Eurasian Journal of Medicine and
Oncology
Oncology care with AI chatbots and assistants

AI-powered ML models will integrate various types of data 6.6. Ethical AI and human-AI collaboration
to predict patients’ responses to specific chemotherapy As AI continues to integrate into oncology, it is essential
agents or immunotherapies. In addition, AI can identify to ensure that its deployment adheres to ethical principles,
early biomarkers of adverse effects, such as organ damage, including transparency, fairness, and bias mitigation.
enabling timely interventions. A clinical example includes These principles will guide the responsible development
AI tools predicting the risk of cardiotoxicity from and use of AI models to prevent unintended consequences
chemotherapy by analyzing imaging markers, genomic and ensure equitable outcomes for all patients.
data, and EHRs, helping clinicians adjust treatment
protocols or implement monitoring strategies to prevent One of the key ethical challenges in AI deployment
serious complications. is addressing bias in algorithms. Multi-modal AI models
must be trained on diverse and representative datasets
6.4. Integration with wearable health monitoring to avoid discriminatory biases that could impact patient
devices care (e.g., underrepresented populations). Without this,
AI could perpetuate existing disparities or introduce new
Wearable devices and mobile health tools are rapidly forms of bias, compromising the fairness of diagnostic and
becoming essential data sources for AI-driven oncology treatment decisions.
care. These tools can track real-time physiological
parameters such as heart rate, stress levels, physical activity, Another critical ethical concern is patient data privacy.
and biomarkers, including oxygen saturation. When Ensuring compliance with regulations, such as HIPAA
integrated with AI, wearable devices offer continuous and other privacy laws while maintaining transparency in
monitoring, alerting healthcare providers to any deviations the usage of patient data is essential in building trust in
in a patient’s health status and enabling timely interventions. AI systems. Patients must feel assured that their personal
In the next decade, AI-powered health monitoring will health information is being handled securely and ethically.
play a crucial role in predicting early signs of relapse Finally, the collaboration between humans and AI will be
or treatment complications. ML models will analyze crucial. Efforts will focus on developing hybrid AI-human
the longitudinal data collected from wearable devices, decision-making models that ensure transparency and
providing early interventions before clinical symptoms maintain clinicians’ trust in AI-generated insights. The goal
become apparent. This approach has the potential to is to empower clinicians to use AI as a tool for informed
improve patient outcomes by enabling proactive care and decision-making while retaining the final authority to
reducing the need for reactive treatments. make clinical decisions based on their expertise. By
addressing these ethical challenges, AI in oncology can
6.5. AI and robotics for enhanced treatment delivery ensure its responsible and equitable deployment.
In the next 5 – 10 years, the integration of AI with robotics
in oncology is expected to advance significantly, enhancing 6.7. Summary of key projections over the next
treatment precision and reducing complications in decade
surgical oncology. AI-powered robotic systems will be Table 6 outlines key areas of AI evolution in oncology over
important in optimizing surgical techniques, particularly the next decade. AI-driven multi-modal data integration
through minimally invasive procedures that allow for will enhance precision medicine by synthesizing genomic,
highly targeted tumor resections. These advancements imaging, clinical, and environmental data. Predictive AI
aim to improve patient outcomes by minimizing the risks models will advance early detection and intervention for
associated with traditional open surgeries, such as longer chemotherapy resistance, relapse, and side effects. The
recovery times and increased complications. AI-driven integration of AI with wearable health monitoring devices
robotic surgery predictions focus on the use of AI will enable real-time tracking and proactive interventions.
algorithms for real-time image-guided precision, helping AI-driven robotic surgery is expected to improve precision
surgical instruments align with imaging modalities, such and reduce surgical trauma through enhanced imaging-
as MRI or CT scans. In addition, predictive AI models guided techniques. In addition, ethical AI governance
will assist surgeons by identifying tumor boundaries or will focus on transparency, bias mitigation, and ensuring
predicting organ movement during dynamic procedures, responsible use of AI in clinical settings.
ensuring greater accuracy and reducing the likelihood of The next 5 – 10 years represent an exciting horizon
complications. This combination of AI and robotic systems for AI in oncology, with multi-modal data integration,
will enhance surgical precision, ultimately leading to better AI-driven predictive modeling, robotic interventions, and
clinical outcomes for patients. wearable health monitoring devices at the forefront of

Volume 9 Issue 1 (2025) 130 doi: 10.36922/ejmo.6251