Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care

Abstract

Critically ill cancer patients have a unique physiological profile marked by severe immunosuppression, frailty, and multimorbidity, making traditional tools like Acute Physiology and Chronic Health Evaluation II or Sequential Organ Failure Assessment often inadequate for accurate risk assessment. This review explores artificial intelligence’s potential to transform onco-critical care from reactive to predictive management. We will synthesize literature on two key applications: Early sepsis detection in critically ill cancer patients and refining mortality prediction models to guide ethical care. The analysis will show how dynamic machine learning models, unlike static scores, use vital signs, lab trends, and unstructured machine learning data to detect deterioration hours before clinical decompensation. The review will also examine barriers to adoption, highlighting the need for explainable artificial intelligence to build clinician trust and address data heterogeneity across cancer populations. Ultimately, it will suggest that analytics can transform oncology intensive care unit care, balancing aggressive treatments and palliative care. While focusing on cancer patients, some evidence from general intensive care unit populations will be identified as extrapolated with caution, given different pathophysiology.

Key Words: Onco-critical care; Sepsis; Artificial intelligence; Machine learning; Febrile neutropenia; Prognostication; Explainable artificial intelligence; Electronic health records

Core Tip: Integrating artificial intelligence (AI) and machine learning into onco-critical care could enable earlier sepsis detection and better risk assessment in critically ill cancer patients. Traditional scoring systems like Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II may be limited due to physiological traits like immunosuppression and treatment side effects. Emerging AI models that use real-time health data, vital signs, and unstructured clinical info show promise. Still, most evidence comes from retrospective studies and general intensive care unit data, and their use in oncology settings needs more validation. Explainable AI aids interpretability and clinician trust but should supplement, not replace, clinical judgment. More studies are needed for widespread adoption.

INTRODUCTION

Recent advancements in critical care and oncology have greatly enhanced survival rates for patients with hematological malignancies and solid tumors. The development of innovative therapies, including chimeric antigen receptor (CAR) T-cells, bispecific antibodies, and immune checkpoint inhibitors, has transformed cancer treatment but also introduced unique, life-threatening toxicities that often necessitate admission to the intensive care unit (ICU)[1]. Consequently, critically ill cancer patients form a diverse group marked by severe immunosuppression, complex multimorbidity, and organ dysfunction caused by treatment. Sepsis and septic shock continue to be major causes of critical illness and death in this vulnerable group[2,3]. This population is highly heterogeneous, including patients with hematological malignancies, hematopoietic stem cell transplantation (HSCT) recipients, solid tumors, and those with febrile neutropenia or receiving advanced immunotherapies, each with distinct clinical trajectories and risk profiles.

Despite advances in care protocols, early sepsis detection and precise risk assessment remain significant challenges. Conventional severity scores, like Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) II, were designed for general ICU patients and mainly rely on static parameters measured within the first 24 hours of admission[4-6]. These parameters do not account for the dynamic, non-linear trajectories of critically ill cancer patients and often show limited sensitivity and specificity in predicting outcomes for patients with unique pathophysiological states, such as chemotherapy-induced neutropenia[4,7]. These shortcomings are particularly evident in specific oncologic subgroups and care settings. For instance, patients with hematological malignancies and those undergoing HSCT often exhibit profound immunosuppression and atypical inflammatory responses, reducing the reliability of conventional physiological thresholds. Similarly, in febrile neutropenia, the absence of a robust inflammatory response may mask early sepsis, while patients with advanced solid tumors frequently present with chronic organ dysfunction that confounds baseline scoring. In addition, individuals receiving newer therapies, such as immune checkpoint inhibitors or CAR T-cell therapy, may develop treatment-related toxicities (e.g., cytokine release syndrome) that overlap with sepsis physiology, further limiting the specificity of traditional scoring systems[8].

To overcome these limitations, the medical field is transforming toward data-driven sciences, heavily centered on artificial intelligence (AI) and machine learning (ML)[5,9]. AI algorithms can analyze large and complex datasets from electronic health records (EHRs). They can identify subtle clinical patterns that precede acute deterioration[3,10]. This narrative review synthesizes the current literature on the application of AI and ML in onco-critical care. Specifically, it explores the utility of ML for the early detection of sepsis in neutropenic patients, dynamic prognostication of ICU mortality, and the integration of explainable AI (XAI) to build clinician trust. Finally, we will address the ethical implications of using AI to inform goals-of-care discussions and the barriers to its widespread implementation[11] (Table 1).

Table 1 Comparison of traditional scoring systems vs machine learning models in sepsis prognostication.

Feature	Traditional scores (SOFA, APACHE II, SAPS II)	Machine learning models (CatBoost, random forest, XGBoost, DeepAISE)
Data utilization	Uses static data, typically the worst values recorded within the first 24 hours of ICU admission[6]	Utilizes continuous, dynamic, real-time data streams including high-frequency vital signs and lab trends[18,20]
Data types	Limited to predefined structured physiological variables and lab values	Capable of processing structured data, unstructured clinical notes (via NLP), and multi-omics data[12,17]
Handling of complexity	Assumes linear relationships between physiological variables and patient outcomes[39]	Capable of modeling highly complex, non-linear interactions among high-dimensional data points[24]
Performance in oncology	Often inadequate due to lack of cancer-specific variables; altered baselines in cytopenic patients skew results[4,5]	Highly adaptable; incorporates cancer-specific comorbidities, treatment history, and specific biomarkers (e.g., blood urea nitrogen, red cell distribution width)[3,4]
Interpretability	Transparent, simple point-based systems; easily calculated manually at the bedside	Inherently “black box”; requires explainable AI techniques like SHAP or LIME to translate logic to clinicians[35]
Application	Retrospective severity assessment and general benchmarking	Real-time early warning alerts, dynamic mortality probability, and personalized treatment recommendations[39]

SOFA: Sequential Organ Failure Assessment; APACHE: Acute Physiology and Chronic Health Evaluation; SAPS: Simplified Acute Physiology Score; ICU: Intensive care unit; NLP: Natural language processing; AI: Artificial intelligence; SHAP: SHapley Additive exPlanations; LIME: Local interpretable model-agnostic explanations.

SEARCH STRATEGY

A literature search was conducted using PubMed/MEDLINE, Scopus, and Web of Science, with Google Scholar used for supplementary screening, covering studies published from January 2015 to December 2025. Keywords included “sepsis”, “oncology ICU”, “machine learning”, “artificial intelligence”, “deep learning”, and “prognostication”, combined using Boolean operators. In addition, grey literature sources, including medRxiv, conference proceedings, and clinical guidelines, were screened to enhance comprehensiveness and reduce publication bias. Studies were included if they evaluated AI/ML models for sepsis prediction, mortality prognostication, or clinical implementation in critical care or oncology settings. Non-clinical, animal-based, and unrelated studies were excluded, and this review follows a narrative approach without formal systematic methodology.

THE BURDEN OF SEPSIS IN THE ONCOLOGY ICU

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, presenting a major global health challenge[12]. In the context of oncology, patients have an exceedingly high risk of developing sepsis - estimated to be nearly ten times higher than the general population - due to the immunosuppressive nature of both their underlying malignancies and myelosuppressive therapies[13,14]. Bloodstream infections occur in 10%-25% of neutropenic patients with hematological malignancies and can escalate to 55% in those undergoing HSCT[13]. Importantly, patients with hematological malignancies and HSCT recipients often experience profound and prolonged immunosuppression, whereas those with solid tumors may present with different patterns of organ dysfunction. Similarly, febrile neutropenia represents a unique clinical entity with attenuated inflammatory responses, while patients receiving immunotherapies or CAR-T cell therapy may develop overlapping syndromes such as cytokine release syndrome, which can mimic or confound sepsis diagnosis.

When cancer patients develop sepsis, their clinical presentation can be highly atypical. The absence of adequate white blood cells blunts the standard inflammatory response, meaning fever or subtle hemodynamic shifts may be the only initial signs of a profound infection[2]. If recognition is delayed, neutropenic sepsis can rapidly progress to multiple organ dysfunction syndrome and death. Every hour of delayed diagnosis and delayed administration of appropriate antimicrobial therapy is associated with a quantifiable increase in mortality[15-17]. Clinicians frequently resort to empirical broad-spectrum antibiotics because specific pathogen identification via traditional blood cultures typically requires a delay of 2 days to 5 days. This practice, while life-saving, contributes to antimicrobial resistance and potential drug toxicity[2]. Therefore, there is a critical, unmet need for predictive tools that can accurately identify the onset of sepsis and the specific risk of deterioration in cancer patients long before overt clinical shock ensues.

These differences are particularly evident in patients with hematological malignancies, especially those with neutropenia or undergoing HSCT, who often exhibit attenuated inflammatory responses and are at higher risk of rapid clinical deterioration. In contrast, patients with solid tumors may present with more localized infections but are frequently complicated by comorbid organ dysfunction and prior treatment-related toxicity. Emerging therapies such as CAR-T cells and immune checkpoint inhibitors introduce additional complexity, with overlapping syndromes such as cytokine release syndrome that may mimic or coexist with sepsis[1,7]. These differences highlight the need for risk stratification approaches that account for tumor type, treatment stage, and underlying immune status.

AI FOR THE EARLY DETECTION OF SEPSIS IN NEUTROPENIC PATIENTS

AI models, particularly deep learning and ensemble ML algorithms, excel at identifying the prodromal phases of sepsis. Unlike human cognition, which is limited by short-term memory constraints and the sheer volume of variables generated by continuous ICU monitoring, AI can process thousands of data points simultaneously[18]. Although several ML approaches for sepsis prediction have been developed in general ICU populations, their applicability to oncology patients requires careful consideration due to distinct immunological and clinical characteristics.

Leveraging routine laboratory and physiological data

Recent studies have successfully utilized basic, readily available hematological and physiological parameters to predict sepsis. A pioneering study by Vijayakumar et al[2] explored an AI-powered system capable of predicting bacterial growth in blood cultures of neutropenic patients up to 2-5 days ahead of actual culture results. By analyzing the consecutive trends of routine hematological parameters during the prodromal phase of sepsis, their best-performing models achieved an accuracy and F1 score of 78%. This novel approach captures the temporal patterns in laboratory values, providing a crucial window for tailoring empirical antibiotic therapy to pathogen-specific targeted treatments rather than defaulting to broad-spectrum agents[2,13].

Similarly, models using complete blood count with differential (complete blood count + differential blood count) data have been trained to rapidly detect sepsis. Because complete blood count tests are routinely performed and minimally invasive, algorithms like the light gradient boosting machine have utilized features such as neutrophil-to-lymphocyte ratios to yield an area under the curve (AUC) of 0.90 in differentiating septic from non-septic states. These models are highly adaptable for resource-limited settings or rapid triage environments, as they bypass the need for extensive, complex clinical variable inputs[19].

Continuous high-frequency monitoring

In the ICU, continuous physiological data streams - such as minute-by-minute heart rate, respiratory rate, and blood pressure variations - provide a rich substrate for ML algorithms. Studies using random forest classifiers on continuous physiological data have successfully distinguished between sepsis and non-sepsis patients up to 5 hours before clinical onset. These models achieve high sensitivity without relying heavily on static EHR inputs[15]. Despite promising predictive performance, there is limited prospective evidence demonstrating that earlier detection through these models consistently translates into improved patient-centered outcomes. Furthermore, deep learning architectures, such as recurrent neural networks combined with parametric Weibull-Cox survival models (e.g., DeepAISE), have demonstrated an ability to predict sepsis onset up to 12 hours before clinical manifestation by tracking high-order interactions among clinical risk factors over time[20,21].

Unstructured data and natural language processing

A significant paradigm shift in AI-driven early detection is the incorporation of unstructured data. Approximately 80% of health data in EHRs exists in unstructured formats, such as free-text physician progress notes and radiology reports[17,22]. Traditional models often ignore this data. However, algorithms employing natural language processing (NLP), such as Latent Dirichlet allocation for topic modeling, have successfully extracted semantic patterns and clinical judgments from these notes[17].

Integrating NLP-processed text with structured vital signs has proven superior in predicting sepsis. For instance, the sepsis early risk assessment algorithm combined unstructured clinical notes with structured data to predict sepsis with an AUC of 0.94, providing a reliable warning 12 hours to 48 hours before the onset of the condition. NLP captures the nuanced clinical judgment documented by physicians, often reflecting early signs of deterioration before they cross standard vital sign thresholds. As a result, it improves the timeliness and accuracy of sepsis prediction[23] (Table 2). It is important to distinguish that a substantial proportion of these models have been trained and validated on general ICU datasets rather than oncology-specific cohorts.

Table 2 Key artificial intelligence applications and methodologies in onco-critical care sepsis.

Clinical application	Key AI/ML algorithms used	Primary data inputs	Demonstrated clinical impact
Early prediction of bacteremia in neutropenia	Random forest, XGBoost, Support vector machines	Routine hematological parameters (complete blood count + differential blood count trends), demographics[2,19]	Predicts bacterial growth 2-5 days prior to culture results, reducing indiscriminate use of broad-spectrum antibiotics[2]
Early warning of septic shock	DeepAISE (recurrent neural survival model), CNN-LSTM	Moving time-windows of vital signs, continuous EHR physiological data[15,17]	Identifies patient deterioration up to 12-24 hours before clinical onset, facilitating early administration of antimicrobials[20,25]
Integration of clinical notes	Latent Dirichlet allocation, natural language processing	Free-text physician progress notes, unstructured EHR data[17]	Captures physician intuition and nuanced clinical status; significantly improves accuracy of predictions 12-48 hours ahead of onset[17]
ICU mortality prediction	CatBoost, LightGBM, gradient boosting decision trees	Age, minimum blood urea nitrogen, urine output, red cell distribution width, metastasis status, SOFA/APS III[3,4]	Outperforms APACHE II/SOFA (AUC > 0.82-0.94); informs end-of-life and goals-of-care discussions[4,27,39]
Treatment optimization	Reinforcement learning digital twins	Historical treatment data, fluid balances, vasopressor responses[3,40]	Optimizes individual fluid resuscitation and vasopressor titration, preventing volume overload and reducing mortality[3]

AI: Artificial intelligence; ML: Machine learning; CNN: Convolutional Neural Network; LSTM: Long short-term memory; EHR: Electronic health record; ICU: Intensive care unit; SOFA: Sequential Organ Failure Assessment; APS: Acute Physiology Score; APACHE: Acute Physiology and Chronic Health Evaluation; AUC: Area under the curve.

Although many studies report high predictive performance of ML models, often with AUC values above 0.85, these results should be interpreted with caution. Most models are developed using retrospective datasets, frequently with limited representation of oncology patients, which raises concerns about overfitting and generalizability. External validation across institutions remains limited, and model calibration is not consistently reported. Commonly used datasets such as MIMIC-III and MIMIC-IV predominantly reflect general ICU populations and may not fully capture the unique physiological and immunological characteristics of critically ill cancer patients. Therefore, performance metrics derived from general ICU populations should not be directly generalized to critically ill cancer patients without appropriate validation.

Importantly, strong predictive performance does not necessarily translate into clinical benefit. Improvements in AUC do not automatically lead to better patient outcomes or meaningful changes in management. In general, simpler models such as gradient boosting methods perform well with structured clinical data, whereas deep learning approaches are better suited to high-frequency time-series data; however, consistent superiority across settings has not been established. Furthermore, heterogeneity in study design, input variables, and outcome definitions makes direct comparison between algorithms challenging. At present, there is limited prospective evidence demonstrating improvement in key clinical endpoints such as early antibiotic administration, ICU survival, or decision-making in oncology-specific settings. Future research should prioritize external validation, standardized reporting, and real-world evaluation to better define the clinical utility of these models.

Overall, findings across multiple studies suggest that ML models consistently outperform traditional rule-based approaches in early sepsis detection, although performance varies depending on data type, model architecture, and patient population[5,19,24,25]. However, these findings are primarily derived from retrospective analyses and should be interpreted as indicative of potential rather than definitive evidence of clinical benefit.

Clinical applicability of ML algorithms

From a clinical perspective, the choice of ML approach should be guided by the type and availability of data in the oncology ICU. Models such as gradient boosting methods (e.g., light gradient boosting machine) are well suited to structured clinical data and perform reliably even in resource-limited settings where data inputs are limited and irregular[24,25]. In contrast, deep learning models, particularly recurrent neural networks, are better suited for continuous, high-frequency physiological data, allowing detection of subtle temporal trends preceding clinical deterioration[15,20]. NLP-based models are particularly valuable when large volumes of unstructured data, such as clinical notes and radiology reports, are available, as they can capture contextual clinical information not reflected in numerical variables[17,23].

However, no single model is universally optimal. Simpler models may offer greater interpretability and ease of integration into clinical workflows, whereas more complex architectures may achieve higher predictive accuracy in data-rich environments[5,24]. Therefore, model selection in the oncology ICU should be individualized, balancing data availability, computational resources, interpretability, and the specific clinical question being addressed[18,25]. The overall workflow of AI-driven sepsis prediction in oncology ICU patients is illustrated in Figure 1.

Figure 1 Conceptual workflow of artificial intelligence-driven sepsis prediction in oncology intensive care unit patients. Clinical data from critically ill cancer patients - including vital signs, laboratory parameters, and unstructured electronic health record data - are processed and integrated using machine learning and deep learning models. These models generate risk scores and early warning alerts that support clinical decision-making, including early intervention and goals-of-care discussions. Continuous feedback from clinicians enables model refinement and improved performance over time. ICU: Intensive care unit; EHR: Electronic health record; NLP: Natural language processing; AI: Artificial intelligence; ML: Machine learning; LGBM: Light gradient boosting machine; RNN: Recurrent neural network; LSTM: Long short-term memory.

DYNAMIC PROGNOSTICATION AND MORTALITY PREDICTION MODELS

In critically ill oncology patients, accurate prognostication of in-hospital or 28-day mortality is essential. It directly informs complex therapeutic and ethical decisions. Furthermore, improvements in predictive accuracy do not inherently equate to improved clinical outcomes, and the real-world impact of these models on decision-making and survival remains insufficiently established. Traditional severity indices (SOFA, Simplified Acute Physiology Score II, APACHE II) rely on generalized linear relationships and are notoriously weak at evaluating cancer-specific parameters, leading to high false-positive rates and limited specificity[5,14,26].

Superiority of ML over traditional scoring systems

ML algorithms bypass the constraints of linear modeling by natively handling non-linear interactions, high-dimensional spaces, and complex multi-morbidities[24]. In massive retrospective studies utilizing the MIMIC-III and MIMIC-IV databases, ML models have consistently outperformed established clinical scores[10,24]. These comparisons, however, are largely based on retrospective datasets and may not fully capture the complexities of real-time clinical decision-making. Across multiple studies, ML models have demonstrated improved performance over conventional scoring systems in predicting mortality among critically ill patients, including those with cancer[10,24]. For example, in predicting hospital mortality for cancer-related sepsis, an ensemble model known as CatBoost achieved an AUC of 0.828, significantly outperforming both Simplified Acute Physiology Score-II (AUC 0.725) and SOFA (AUC 0.682)[4]. CatBoost’s unique algorithmic design handles categorical data intelligently without inflating dataset dimensionality, making it highly robust against overfitting[4].

Similarly, for predicting mortality in patients with febrile neutropenia, non-linear models such as gradient boosting trees and artificial neural networks have achieved AUCs of up to 0.92. These models operate independently of a physician’s subjective evaluation, relying instead on objective data like respiratory failure markers, shock indices, and specific age demographics to accurately stratify risk[27,28]. While these findings are promising, studies specifically focused on homogeneous oncology ICU cohorts remain limited, and extrapolation from general ICU data should be interpreted cautiously.

Importantly, the performance and applicability of ML models may vary across oncology subpopulations. Models developed in heterogeneous ICU cohorts may not perform equally well in patients with hematological malignancies, HSCT recipients, or those receiving advanced immunotherapies. Differences in immune status, baseline organ function, and treatment-related toxicities can influence both predictor variables and outcome patterns. Some studies suggest that incorporating cancer-specific variables - such as neutropenia, transplant status, and treatment history - can improve model performance and clinical relevance[6,14,26]. These findings underscore the importance of developing and validating subgroup-specific models tailored to distinct oncology populations.

Identification of novel and cancer-specific prognostic biomarkers

The agnostic, data-driven nature of ML has also highlighted prognostic variables that were previously underappreciated in standard scoring systems. Through the use of feature importance ranking, algorithms have identified that minimum blood urea nitrogen and urine output are two of the most critical predictors of mortality in cancer-related sepsis, emphasizing the profound impact of sepsis-associated acute kidney injury in oncology patients who may have already suffered nephrotoxic hits from prior chemotherapy regimens[4].

Furthermore, factors such as the red cell distribution width have emerged as powerful, cost-effective predictors of 28-day mortality in septic shock patients with thrombocytopenia. Elevated red cell distribution width likely reflects intense systemic inflammation and oxidative stress, acting as an easily measurable proxy for disease severity[3]. In advanced stage 4 solid cancer patients, ML models using balanced random forests highlighted initial body temperature, serum albumin, and creatine kinase-MB levels as profound contributors to 28-day mortality risk, outperforming APACHE II scores and initial lactate levels[6] (Table 3). Importantly, the identification of such variables in oncology-focused datasets highlights the added value of cancer-specific modeling, which may not be captured in general ICU-based algorithms.

Table 3 Common clinical variables and biomarkers utilized by machine learning models for sepsis prediction in oncology patients.

Data category	Specific variables/features included	Clinical relevance in onco-critical care
Demographics and medical history	Age, gender, primary cancer type, metastatic status, history of solid organ or bone marrow transplant, prior chemotherapy regimens	Captures baseline physiological reserve, specific immunosuppressive states, and the inherent mortality risk associated with the patient’s underlying malignancy[4,27]
High-frequency vital signs	Heart rate, respiratory rate, temperature (max/minute/avg), systolic/diastolic blood pressure, peripheral oxygen saturation	Continuous streams of this data allow algorithms to detect subtle, non-linear trajectories of deterioration hours before a clinical diagnosis of septic shock[14]
Routine laboratory findings	Complete blood count (absolute neutrophil count, platelet count), red cell distribution width, hematocrit	Dynamic shifts in complete blood count parameters, particularly absolute neutrophil count and red cell distribution width, are powerful predictors of bacteremia and mortality in patients with chemotherapy-induced myelosuppression[2]
Metabolic and organ function markers	Minimum/maximum lactate, blood urea nitrogen, serum creatinine, bilirubin, minimum pH, base excess	Highlights critical sepsis-induced organ dysfunction, particularly acute kidney injury which is a major mortality driver in cancer patients receiving nephrotoxic drugs[4,14]
Inflammatory biomarkers	C-reactive protein, procalcitonin, interleukin-6	Aids the model in differentiating between non-infectious tumor fevers (or drug reactions) and true bacterial sepsis[13,28]
Unstructured EHR data	Free-text physician progress notes, nursing assessments, radiology reports	Analyzed via natural language processing, these notes capture nuanced clinical intuition and symptom descriptions that structured numerical data misses[23]

EHR: Electronic health record.

Tumor-related comorbidities significantly influence both the performance and clinical relevance of ML models in onco-critical care. For example, chemotherapy-related kidney injury and sepsis-associated acute injury are common in cancer patients and are closely linked to mortality. As a result, renal function parameters such as serum creatinine, blood urea nitrogen, and urine output are important predictors in many models. Similarly, immune dysfunction due to chemotherapy, corticosteroids, or HSCT alters the host response to infection and affects commonly used variables such as white blood cell count and inflammatory markers[19,29].

Incorporating these cancer-specific factors - such as neutropenia, prior chemotherapy exposure, transplant status, and organ dysfunction - can improve the clinical relevance of prediction models. Some studies have explored stratified approaches, where models account for differences across subgroups such as immunosuppressed patients or those with organ dysfunction. However, many existing models are still derived from general ICU datasets and include these variables only indirectly. This limits their ability to fully capture the complex interaction between cancer biology, treatment-related toxicity, and sepsis progression[30].

Overall, while current models are capable of handling complex clinical data, they often lack explicit oncology-specific design. Future work should focus on incorporating tumor-related comorbidities more directly and developing models tailored to specific oncology subpopulations.

INFORMING ETHICAL GOALS-OF-CARE DISCUSSIONS

Critically ill patients with hematological malignancies or metastatic solid tumors frequently present to the ICU with high expectations for intensive therapies. However, when multiple organ failure ensues, the balance between aggressive life-sustaining treatments and unnecessary suffering becomes precarious[1,31]. The decision to pursue a “time-limited trial” of intensive care requires an accurate, individualized understanding of the patient’s likely trajectory[1].

By providing highly accurate, dynamic probability scores of mortality, AI models serve as objective tools to guide shared decision-making. At present, this role remains largely conceptual, as robust prospective evidence supporting the integration of AI-driven predictions into ethical decision-making frameworks is limited. When physicians are equipped with reliable data predicting a minimal chance of survival, they are better positioned to initiate transparent, empathetic goals-of-care discussions with patients and surrogates[1,18]. AI-generated prognostic scores can prompt the re-evaluation of aggressive measures - such as invasive mechanical ventilation or continuous renal replacement therapy - favoring instead early palliative integration, optimal symptom management, and the preservation of patient dignity[1]. Consequently, AI does not replace clinical empathy; rather, it provides the statistical certainty necessary to navigate uncertainty and avoid futile interventions.

Despite these potential benefits, the use of AI in goals-of-care discussions raises important ethical concerns. Algorithmic bias is a key issue, particularly in oncology populations where datasets may underrepresent certain subgroups, such as patients with rare malignancies or those from diverse socioeconomic backgrounds. This may lead to unequal risk predictions and potentially influence treatment decisions in a biased manner[32]. In addition, reliance on AI-generated prognostic estimates in end-of-life settings introduces ethical challenges, including the risk of over-reliance on algorithmic outputs and the potential to undermine clinician judgment or patient autonomy. Data privacy is another critical consideration, as these models depend on large volumes of sensitive EHR data. Ensuring secure data handling, transparency in data use, and compliance with regulatory standards is essential to maintain patient trust. These concerns highlight the need for careful and responsible integration of AI into ethically complex clinical decision-making.

In practical settings, the application of AI in goals-of-care discussions often presents nuanced dilemmas. For example, when an AI model predicts a very high probability of mortality, clinicians may face the challenge of balancing continued aggressive treatment with an early transition to palliative care[1]. While such predictions can support timely decision-making, there is a risk that they may unduly influence clinical judgment or lead to premature limitation of care if interpreted without sufficient context[33]. It is therefore essential that these predictions are discussed within a multidisciplinary framework and aligned with patient values and preferences. Equally important is the preservation of clinician autonomy and patient trust. Over-reliance on algorithmic outputs may reduce individualized clinical reasoning, particularly in complex oncology patients where disease trajectory is often unpredictable. Clear communication with patients and families regarding the role of AI, including its uncertainties and limitations, is essential to ensure informed consent and shared decision-making[33].

Ethical governance of clinical AI systems

The safe and ethical deployment of AI in clinical practice requires structured governance frameworks. Algorithmic stewardship should include regular auditing of model performance, assessment of bias across patient subgroups, and periodic recalibration to maintain accuracy over time. Human-in-the-loop approaches remain essential, ensuring that AI outputs support rather than replace clinical judgment[34].

In addition, transparent reporting of model development, validation, and limitations is critical for building clinician trust. Institutions should establish oversight mechanisms to monitor real-world performance, address unintended consequences, and ensure accountability in high-risk scenarios such as end-of-life decision-making. Patient-centered considerations, including informed consent and clear communication about the role of AI in care decisions, are also fundamental. Together, these measures can help ensure that AI systems are implemented in a manner that is fair, transparent, and aligned with ethical principles in oncology critical care.

In addition to general governance principles, practical implementation pathways are essential for effective algorithmic stewardship. Hospitals should establish dedicated clinical oversight mechanisms, such as multidisciplinary AI governance committees involving intensivists, oncologists, data scientists, and ethicists, to supervise model deployment and monitor real-world performance[25]. Regular auditing of model outputs should be conducted to identify predictive drift and potential bias, particularly across high-risk oncology subgroups such as patients with advanced malignancies or those receiving immunosuppressive therapies[12,24].

Bias assessment should include periodic evaluation of model performance across predefined strata - such as tumor type, stage, treatment status, and organ dysfunction - to ensure equitable accuracy and avoid systematic under- or overestimation of risk[5,24]. In addition, fairness safeguards should be incorporated into model design and validation, including recalibration using local data and exclusion of inappropriate surrogate variables that may unintentionally encode bias[18,25]. Continuous clinician feedback, supported by audit dashboards and structured reporting systems, can further help identify unintended consequences and guide iterative model refinement[12]. Together, these measures provide a practical framework for ensuring that AI systems remain safe, transparent, and equitable in high-stakes oncology ICU settings.

The necessity for XAI to foster clinician trust

A prominent barrier to the integration of AI in high-stakes environments like the ICU is the “black box” phenomenon. Advanced algorithms, especially deep neural networks and ensemble methods, often operate as “black boxes”. Their internal decision pathways are difficult for clinicians to interpret[20,35]. Clinicians, who bear the ultimate ethical and legal responsibility for patient outcomes, are understandably hesitant to alter treatment paradigms based on inscrutable algorithmic dictates[10,18].

Methods of explainability

To bridge this gap, the field of XAI has become paramount[5,35]. XAI techniques demystify algorithm outputs by quantifying the exact contribution of individual clinical variables to a specific prediction.

SHapley Additive exPlanations: Rooted in cooperative game theory, SHapley Additive exPlanations (SHAP) assigns an importance value to each feature for a given prediction. For example, a SHAP visualization might reveal to a clinician that a patient’s high mortality risk score is primarily driven by a recent spike in blood urea nitrogen, a dropping platelet count, and an elevated heart rate, aligning the machine’s output with standard pathophysiological reasoning[3,36].

Local interpretable model-agnostic explanations and break down profiles: These local-level explanation tools approximate the behavior of complex models for individual patients, showing how a change in a specific variable (e.g., lowering the lactate level) might theoretically improve the patient’s predicted outcome[36,37].

Bayesian networks and intrinsic transparency

While post-hoc tools like SHAP are valuable, intrinsically interpretable models offer another solution. Bayesian networks (BNs) and Dynamic BNs (DBNs) model clinical reasoning under uncertainty by updating probabilities as new evidence (e.g., new lab results) arrives. BNs mimic the natural diagnostic process of physicians, handle missing data gracefully through probabilistic priors, and offer a transparent graphical representation of causality. Studies utilizing BNs for sepsis prediction have demonstrated AUCs of up to 0.95 while maintaining complete mathematical interpretability, making them highly attractive for critical care implementation where epistemic humility is required[35].

Human-in-the-loop and algorithmic stewardship

XAI facilitates a “human-in-the-loop” paradigm, where AI serves to augment human cognition rather than replace it[35,38]. By presenting interpretable risk scores alongside the variables driving them, AI systems empower clinicians to validate the machine’s logic against their own clinical assessment. This collaborative synergy - termed “algorithmic stewardship” - ensures that AI deployment is guided by fairness, transparency, and patient-centered values, effectively mitigating cognitive biases and preventing the blind acceptance of algorithmic errors[38].

IMPLEMENTATION BARRIERS AND DATA HETEROGENEITY

Despite the robust retrospective performance of ML models, transitioning from bytes to the bedside involves substantial logistical, technical, and regulatory hurdles.

Data quality and heterogeneity

AI algorithms are entirely dependent on the quality of their training data. Retrospective EHR datasets are frequently plagued by missing values, irregular sampling intervals, and misaligned time-series data[18,25]. Furthermore, the oncology population is notoriously heterogeneous. A model trained mainly on patients with solid tumors may perform poorly in patients undergoing allogeneic stem cell transplantation. This reflects the problem of “domain shift”[38]. To counteract class imbalance - where non-survivors or septic patients are significantly outnumbered by stable patients - techniques like the synthetic minority over-sampling technique are required during the data preprocessing phase to prevent models from developing a dangerous bias toward the majority class[26,39]. This further underscores the limitation of applying models derived from general ICU populations to heterogeneous oncology subgroups without recalibration.

Alert fatigue and workflow integration

Even the most accurate AI model will fail clinically if it disrupts workflow or generates excessive false positives. In the high-stress environment of the ICU, constant, uninterpretable alarms lead to “alert fatigue”, causing clinicians to eventually ignore the system. Successful integration necessitates the development of user-friendly interfaces embedded directly into the EHR, coupled with carefully calibrated alert thresholds that prioritize high specificity alongside sensitivity[10,18]. Systems such as the Targeted Real-Time Early Warning System have shown that well-calibrated algorithms can be integrated into clinical workflows. These systems can reduce time to antibiotic administration and may improve sepsis-related outcomes[10]. In addition, several important methodological limitations within the current literature warrant consideration. External validation of ML models remains limited, with many studies relying on single-center datasets, thereby restricting generalizability across diverse healthcare settings. Model calibration is inconsistently reported, despite being critical for clinical applicability, as well-calibrated risk estimates are essential for decision-making. Dataset heterogeneity and institutional variability - including differences in patient populations, treatment protocols, and data acquisition practices - further complicate model transportability. Another key challenge lies in the variability of sepsis definitions and labeling strategies, particularly when retrospective criteria are used, which may introduce misclassification bias. Class imbalance, common in critical care datasets, can lead to overly optimistic performance metrics if not appropriately addressed. Furthermore, model performance is often dependent on specific workflow integrations and data availability, limiting reproducibility across institutions. Importantly, high predictive performance (e.g., AUC) does not necessarily translate into clinical usefulness, and there remains a critical distinction between statistical accuracy and meaningful impact on patient outcomes. These limitations underscore the need for standardized reporting, robust external validation, and prospective studies evaluating real-world clinical utility.

The need for prospective validation

Currently, the vast majority of AI models in onco-critical care exist as retrospective proofs-of-concept[18,25]. To achieve widespread clinical acceptance and regulatory approval, these algorithms must undergo rigorous, multi-center, prospective randomized controlled trials[12]. Only through such validation can the true clinical utility, safety, and ethical implementation of these models be confirmed across diverse oncology populations[12,25] (Table 4).

Table 4 Key barriers and proposed solutions for the clinical implementation of artificial intelligence in the oncology intensive care unit.

Implementation barrier	Description and clinical impact	Proposed solutions and emerging strategies
The “black box” phenomenon	Deep learning and complex ensemble models lack transparency, making clinicians hesitant to trust or act upon life-altering predictions without understanding the underlying reasoning[3,9]	Implementation of explainable AI tools (e.g., SHAP, LIME) to visualize feature importance; use of intrinsically transparent models like Bayesian Networks[24,35]
Data heterogeneity and domain shift	Models trained on general ICU populations (e.g., MIMIC databases) often perform poorly on highly specific subgroups like hematologic malignancy patients due to physiological differences[3,7]	Development of diverse, multi-center datasets; specific local recalibration of models; utilizing federated learning to train models across institutions while preserving data privacy[3,24]
Alert fatigue and workflow disruption	Overly sensitive algorithms generate excessive false-positive alarms, overwhelming staff and leading clinicians to ultimately ignore the system[9,11]	Calibrating models for higher specificity and positive predictive value; embedding alerts seamlessly into the EHR workflow; human-in-the-loop system designs[9,21]
Class imbalance in datasets	In hospital datasets, the number of patients dying from sepsis is significantly lower than the number of survivors, causing ML models to become biased toward predicting survival[17,39]	Utilizing data preprocessing techniques such as the synthetic minority over-sampling technique to synthetically balance the dataset during the training phase[17,39]
Lack of prospective validation	The vast majority of current models are based on retrospective observational data, lacking the rigorous evidence required to prove they safely improve patient outcomes[9,18]	Conducting large-scale, multi-center randomized controlled trials specifically assessing the safety, efficacy, and ethical integration of AI-driven clinical decision support systems[9,21]

SHAP: SHapley Additive exPlanations; LIME: Local interpretable model-agnostic explanations; ICU: Intensive care unit; EHR: Electronic health record; ML: Machine learning.

Practical considerations for clinical implementation

Beyond technical performance, several practical considerations influence the clinical feasibility of AI-based systems in the ICU. Regulatory approval remains a key challenge, as clinical decision support tools must meet evolving standards for safety, transparency, and accountability before routine deployment. Integration with existing hospital EHR systems is another major barrier. It requires interoperability, real-time data access, and minimal disruption to clinical workflows. In addition, implementation requires adequate computational infrastructure, including secure data storage, processing capacity, and dedicated IT support, which may be limited in resource-constrained settings. Cost implications - both initial investment and ongoing maintenance - must also be considered, particularly in low- and middle-income healthcare systems.

Despite these challenges, early real-world implementations have shown promising results. Systems such as real-time sepsis alert platforms integrated within EHRs have demonstrated reductions in time to antibiotic administration and improved adherence to sepsis care bundles in selected settings. However, these outcomes remain variable and context-dependent, underscoring the need for careful local validation, clinician engagement, and continuous performance monitoring before widespread adoption.

CONCLUSION

The intersection of AI, critical care, and oncology represents a frontier of immense potential. Critically ill cancer patients facing sepsis require rapid, personalized interventions that exceed the capabilities of traditional, static scoring systems. Dynamic ML models utilizing continuous physiological streams, sophisticated laboratory trends, and unstructured clinical notes have proven highly adept at predicting sepsis onset and stratifying mortality risk with unprecedented accuracy. However, much of the current evidence base is derived from general ICU populations, and there remains a critical need for oncology-specific model development, validation, and prospective evaluation.

The realization of this potential hinges on overcoming significant implementation barriers. Embracing XAI is absolutely essential to demystify complex algorithms, ensuring that clinicians can trust, interpret, and act upon AI-generated insights. When thoughtfully integrated into clinical workflows, interpretable AI can foster a powerful symbiosis between human empathy and machine precision. AI-driven analytics may help improve care in the oncology ICU. They can support early intervention and contribute to more informed goals-of-care discussions.