Page 137 - EJMO-9-1

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

adopted, emphasizing patient engagement, empathy, and mechanisms, thereby improving early detection, risk
shared decision-making. Finally, continuous feedback stratification, and treatment prediction. Genomic data, for
mechanisms between clinicians and AI systems are crucial example, provides critical information about mutations,
for ensuring the systems evolve in alignment with clinical gene expression, and epigenetic changes linked to cancer
practice. Ongoing performance evaluation will allow risk and treatment resistance, which AI models can
adjustments based on patient outcomes and emerging analyze to identify predictive markers. Medical imaging
clinical evidence, enhancing the overall effectiveness and data, such as CT, MRI, and positron emission tomography
reliability of AI in oncology care. scans, offer visual markers for tumor size, location, and
While AI-powered virtual assistants and chatbots bring progression, with deep learning techniques, such as
advanced computational power and scalability to oncology CNNs, already employed to detect early-stage cancer or
care, they cannot replace the clinical judgment, empathy, predict disease progression. EHRs contribute clinical
and individualized decision-making of human providers. data, such as lab results, treatment history, and patient
Real-world cases emphasize that human oversight remains demographics, which AI-powered NLP models extract to
essential in managing complex cancer cases, where identify risk factors and trends. In addition, environmental
nuanced clinical judgment is required. These systems are and lifestyle factors, such as smoking, diet, pollution, and
valuable for providing evidence-based recommendations, stress, interact with genetic predispositions and cancer
monitoring, and supporting patients, but they cannot fully risks. AI-driven models can correlate these factors with
understand the emotional complexity and nuanced context biological and clinical data to enhance risk stratification
of individual patient interactions. Human providers and personalized treatment strategies. By integrating
are crucial in applying personalized clinical expertise, these data streams, AI systems can offer a holistic view
making critical decisions, and addressing the emotional of a patient’s condition, facilitating more precise and
and psychological needs of patients, ensuring a more personalized oncology care.
comprehensive and empathetic approach to cancer care. 6.2. The role of AI in multi-modal data integration
Thoughtful integration of AI into clinical workflows, such
as decision support systems, administrative automation, By integrating various data streams, AI can provide a
real-time monitoring, and tele-oncology, enables comprehensive view of a patient’s condition, enabling
oncologists to focus on high-value patient interactions. tailored interventions. Multi-modal AI models, employing
Ultimately, the future of oncology care depends on creating advanced ML techniques, such as graph neural networks,
a seamless balance between technological innovation and ensemble learning, and transformer models, will be
compassionate human care. essential in analyzing complex interactions across diverse
datasets. Over the next 5 – 10 years, AI will advance
6. Future directions toward real-time, dynamic integration of multi-modal
The integration of AI into oncology care has already shown data to predict disease progression, therapeutic resistance,
remarkable potential, but its evolution over the next 5 – and personalized drug responses. Clinical workflows will
10 years will be shaped by technological advancements, increasingly rely on AI systems capable of synthesizing this
improved data integration, and increasing clinical information, empowering oncologists to make informed,
applications. One of the most exciting prospects lies in data-driven decisions without the need to manually
the integration of multi-modal data to enable a new level analyze disparate datasets. Enhanced imaging-genomics
of precision medicine, allowing for individualized and correlations will drive precision radiotherapy, where
targeted treatment strategies based on a comprehensive AI-guided imaging not only identifies anatomical targets
understanding of a patient’s unique biological, genetic, but also biological biomarkers predictive of treatment
and clinical factors. Below are key areas of focus and resistance, further optimizing patient care.
projections for AI’s future development in oncology over 6.3. AI-driven predictive models and early warning
the next decade.
systems
6.1. Integration of multi-modal data for precision Within the next decade, AI will play a pivotal role in
medicine developing predictive models that forecast treatment
The convergence of diverse data types, such as genomic outcomes, disease progression, and patient responses.
data, imaging data, EHRs, clinical notes, and environmental These models will leverage real-time patient data,
factors, is essential for achieving comprehensive, patient- including clinical, genomic, and imaging insights,
specific insights in oncology. Multi-modal data integration to anticipate complications, predict side effects, and
allows AI to analyze complex and interrelated biological optimize personalized treatment regimens. For example,

Volume 9 Issue 1 (2025) 129 doi: 10.36922/ejmo.6251