Page 132 - EJMO-9-1

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

reminders. ML models, as demonstrated by Chen enabling earlier detection, and supporting personalized
et al., provide predictive monitoring of patient behavior, treatment strategies. AI-powered chatbots and virtual
9
enabling early detection of non-adherence. AI-powered assistants have also proven effective in improving patient
virtual assistants have also bridged gaps in care for engagement, treatment adherence, emotional well-being,
underserved populations, reducing disparities in access to and access to care. These tools provide 24/7 support,
9,10
critical oncology information. Furthermore, AI chatbots personalized information, and real-time assistance,
contribute to shared decision-making by offering evidence- particularly for underserved or remote populations. In
based treatment options and empowering patients to make addition, AI’s integration into clinical trials has shown
informed decisions. 13 promising results, supporting improvements in diagnostic
Overall, these findings demonstrate AI’s transformative accuracy, chemotherapy response prediction, and the
impact on oncology care, improving clinical outcomes, delivery of care in remote or underserved areas through
patient engagement, and access to care, particularly for telemedicine platforms. Overall, AI’s adoption in oncology
underserved populations. is driving advancements that address key challenges in
cancer care, particularly in improving access, reducing
3.5. Evidence from AI implementation in clinical geographic disparities, and enhancing the quality of
trials patient outcomes.
Evidence from clinical trials underscores the growing role of 4. Result comparison
AI in oncology, particularly in diagnostics, chemotherapy
response prediction, and telemedicine. In diagnostic AI-powered virtual assistants and chatbots have several
trials, AI-based imaging tools have demonstrated advantages over traditional care, as detailed in Table 1.
superior performance compared to human radiologists. These tools provide 24/7 availability, faster response times,
For instance, Rajpurkar et al. introduced CheXNet, and personalized support, which traditional care often
12
9,17
an AI system capable of identifying disease patterns in lacks. However, as shown in Table 2, they fall short of
chest X-rays with diagnostic accuracy comparable to providing the nuanced emotional connection offered by
radiologists. Bejnordi et al. further validated AI’s ability human providers. 5
13
to detect lymph node metastases in breast cancer patients 4.1. AI virtual assistants and chatbots versus
with high sensitivity rates (85%). traditional oncology care (human-provider
AI has also been leveraged in predicting chemotherapy interaction)
responses, with ML models analyzing clinical profiles The comparison of AI virtual assistants and chatbots
and genomic data to offer clinicians actionable insights with traditional oncology care, telemedicine solutions,
for personalized treatment planning. Lee et al. and mobile health apps highlights key differences in
17
reported a 20% improvement in chemotherapy response functionality, cost, and the scope of support provided.
prediction, enhancing treatment regimens and patient AI virtual assistants and chatbots offer key advantages
outcomes. In addition, telemedicine trials powered over traditional oncology care, telemedicine solutions,
by AI have demonstrated significant improvements in and mobile health apps in terms of accessibility, cost,
treatment adherence and reduced missed appointments. efficiency, and support scope (Tables 2-4). They provide
Chen et al. found that AI-driven telemedicine platforms 24/7 availability, lower operational costs, and real-time,
9
improved adherence rates by 30%, especially for rural personalized responses that enhance patient engagement
populations, highlighting AI’s role in addressing and symptom management. However, they complement—
geographic disparities and ensuring access to oncology not replace—human providers, particularly for complex
care. cases requiring clinical judgment and emotional support.

Overall, these clinical studies and real-world evidence
highlight that AI tools improve diagnostic accuracy and 4.2. AI virtual assistants and chatbots versus
enhance the delivery of treatment plans, while predictive telemedicine solutions
algorithms and AI-based remote interventions contribute AI virtual assistants and chatbots present significant
to better patient adherence and care quality in oncology. advantages over telemedicine solutions in terms of
The studies and data presented highlight several key accessibility, cost-effectiveness, efficiency, and the breadth
findings. AI has played a crucial role in improving early of support they offer (Table 3). While telemedicine
cancer detection, treatment planning, and survival rates. solutions provide a broader range of care, they often lack
ML and deep learning algorithms have demonstrated the interactive and adaptive capabilities that AI virtual
significant promise in enhancing diagnostic accuracy, assistants offer.

Volume 9 Issue 1 (2025) 124 doi: 10.36922/ejmo.6251