4.2.1. Early detection and diagnosis

ML algorithms, particularly deep learning (DL) models such as convolutional neural networks (CNNs), have demonstrated remarkable success in image-based diagnostics such as mammography, computed tomography (CT) scans, magnetic resonance imaging (MRI) and histopathological slide analysis (Sharafaddini et al. 2024). These models are trained on thousands of annotated medical images, learning to recognize subtle patterns indicative of malignancies that may be missed by the human eye. For example, in breast cancer screening, ML tools can identify microcalcifications or masses that may suggest early-stage tumors, sometimes with greater sensitivity and specificity than radiologists.

Beyond static imaging, ML is increasingly applied to dynamic and multi-modal diagnostic data. Natural language processing (NLP) algorithms are used to mine electronic health records (EHRs) for symptoms, clinical notes and risk factors, enhancing diagnostic accuracy when combined with imaging and genomic data (Thatoi et al. 2023). In dermatology, ML models are used to classify skin lesions based on dermoscopic images, differentiating between benign and malignant conditions with dermatologist-level accuracy.

Importantly, ML supports real-time decision-making in clinical workflows. For instance, AI-powered decision support systems integrated into radiology suites can flag suspicious findings for further review, thereby reducing diagnostic delay and improving clinical outcomes (Oyeniyi and Oluwaseyi 2024). The efficiency and scalability of ML also allow for widespread deployment in resource-constrained settings, offering a solution to the global shortage of skilled diagnosticians.

Nevertheless, ensuring model generalizability across diverse populations and imaging modalities remains a challenge. Variations in equipment, data labeling standards and patient demographics can influence model performance (Hadjiiski et al. 2023). Thus, continuous model validation, retraining on local datasets and rigorous clinical trials are essential to translate ML-based diagnostic tools into reliable, real-world applications.

4.2.2. Prognosis and risk stratification

Predictive models can analyze genetic, proteomic, radiologic and clinical data to forecast disease progression, recurrence and patient outcomes. By leveraging supervised and unsupervised learning techniques, ML tools can identify risk factors and establish correlations that may not be apparent through conventional statistical analysis (Eckhardt et al. 2023). For instance, ensemble models, such as random forests and gradient boosting machines, have been used to stratify breast cancer patients based on recurrence risk, tumor subtype and likely response to treatment. This stratification enables clinicians to tailor therapy plans, minimize overtreatment and improve long-term outcomes.

In hematologic malignancies such as leukemia and lymphoma, ML models have been developed to predict survival and relapse by integrating high-dimensional datasets such as next-generation sequencing, flow cytometry and treatment history (Yuan et al. 2024). These models assist clinicians in categorizing patients into different prognostic groups, which informs decisions about stem cell transplantation, chemotherapy intensity and post-remission surveillance.

Moreover, deep learning frameworks have demonstrated potential in predicting metastasis and treatment resistance. In lung cancer, for example, ML algorithms analyzing CT scans and genomic mutations have shown accuracy in forecasting the likelihood of disease spread to other organs (Huang et al. 2024). These insights are critical for deciding whether aggressive interventions or palliative care are more appropriate.

ML is also contributing to real-time risk stratification in clinical settings. By continuously analyzing updated patient records, ML systems can generate alerts for high-risk patients, enabling timely interventions (Fatima 2024). Integration with electronic health records (EHRs) ensures that risk assessments evolve alongside the patient’s clinical course.

However, accurate prognosis and risk stratification depend on the quality and completeness of input data. Inconsistent data collection practices, missing information and lack of standardization can affect model performance (Nijman et al. 2022). Thus, developing robust data pipelines and validating ML models across diverse populations remain essential to ensure fairness, accuracy and generalizability.

4.2.3. Treatment planning and optimization

Radiotherapy and chemotherapy can be optimized using ML to determine the most effective dose, timing and modality of treatment. ML models use a vast array of patient data – including tumor histology, genetic mutations, imaging features and prior treatment responses – to simulate therapeutic outcomes and guide precision dosing strategies (Khalighi et al. 2024). For instance, reinforcement learning algorithms can be applied to dynamically optimize radiotherapy schedules, adjusting doses based on tumor response and minimizing damage to dynamically surrounding healthy tissues.

Chemotherapy regimens also benefit from ML-assisted planning. Predictive algorithms analyze pharmacogenomic data to forecast individual drug metabolism and toxicity profiles, helping oncologists tailor treatment plans that balance efficacy and side-effect burden (Showbharnikhaa et al. 2024). In addition, ML aids in predicting resistance to specific chemotherapeutic agents, enabling early shifts to alternative therapies and reducing trial-and-error prescribing.

ML has made a significant impact on the field of immunotherapy, where patient selection is critical. Advanced ML techniques analyze complex biomarker data – such as PD-L1 expression, tumor mutational burden and immune cell infiltration – to identify individuals most likely to benefit from checkpoint inhibitors and other immunotherapies (Qin et al. 2024). By integrating omics data and clinical indicators, ML models contribute to a more precise stratification of patients, enhancing treatment efficacy and cost-effectiveness.

Furthermore, ML supports adaptive treatment planning through continuous learning from real-world data. As patients progress through therapy, ML systems can re-evaluate predictions and update recommendations in real time, allowing for personalized adjustments based on evolving clinical conditions (Christopoulou 2024). This adaptability not only enhances treatment outcomes but also promotes shared decision-making between patients and healthcare providers.

Despite these advancements, challenges remain, including the need for large, high-quality datasets and the interpretability of ML-generated recommendations (Bucholc et al. 2023). Ensuring that treatment algorithms are transparent, clinically validated and ethically deployed is essential for widespread adoption in oncology practice.

4.2.4. Monitoring and follow-up

ML-powered wearable devices and electronic health record (EHR) analytics support continuous patient monitoring, enabling early detection of relapse or adverse effects.

Wearable technologies such as smartwatches, biosensors and mobile health (mHealth) applications collect real-time physiological data including heart rate, respiration, activity levels and even biochemical markers. These data are analyzed using ML algorithms to identify patterns and deviations that may signal disease recurrence, treatment complications or emerging side effects.

In oncology, continuous monitoring allows clinicians to detect early signs of tumor progression or treatment-related toxicity, such as cardiotoxicity from chemotherapy or radiation-induced inflammation. ML models trained on longitudinal patient data can flag anomalies and provide predictive insights, supporting proactive clinical interventions and potentially improving survival rates (Terranova and Venkatakrishnan 2024). Moreover, ML-driven symptom tracking applications help patients self-report their experiences, which are then analyzed to guide supportive care adjustments.

In addition, EHR analytics enable population-level surveillance and personalized follow-up strategies. ML algorithms can sift through large-scale EHRs to identify patients at high risk of non-adherence, hospitalization or post-treatment complications (Chekroud et al. 2021). Predictive models support scheduling of follow-up appointments, tailoring surveillance imaging and recommending lifestyle modifications based on patient-specific trajectories.

Remote monitoring also plays a vital role in enhancing patient engagement and reducing healthcare costs. By minimizing the need for frequent in-person visits, ML-enabled telehealth solutions ensure continuous oversight while maintaining convenience and accessibility for patients, particularly those in rural or underserved areas (Vadia et al. 2025).

Despite these advantages, several challenges persist, including data integration from diverse devices, maintaining data privacy and ensuring the accuracy of predictions. Ensuring interoperability among platforms and maintaining transparency in ML-driven decision-making are critical to fostering trust and clinical adoption.