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Artificial intelligence (AI) is increasingly recognized for its potential to transform medicine, providing benefits for physicians and patients across various aspects of care. Its integration into healthcare aims to personalize treatment, improve patient outcomes, and address challenges such as rising costs and resource limitations. AI applications already support treatment planning, disease diagnosis, outcome prediction, operational efficiency, and patient monitoring. However, adoption remains cautious within the medical community due to concerns about accuracy, unfamiliarity, and ethical implications.

AI comprises computer systems that learn from data to identify patterns and assist in decision-making. Machine learning (ML) handles structured data like lab results, while deep learning (DL) processes complex inputs such as medical images and clinical notes. Techniques like natural language processing (NLP) enable AI to interpret written language, and neural networks support image recognition and disease prediction. Studies have demonstrated AI’s diagnostic capabilities, including dermatology models performing on par with experts and echocardiographic analyses predicting cardiac conditions with high accuracy. Moreover, during the COVID-19 pandemic, AI facilitated outbreak detection and digital contact tracing through analysis of unstructured data.

Despite advances, a knowledge gap persists regarding AI’s role in holistic patient care, particularly in palliative care where quality of life is prioritized. Patients with blood cancers such as leukemia, lymphoma, and multiple myeloma face complex symptoms and disease courses, yet research on AI’s application in their palliative care remains limited.

This review introduces AI’s emerging role in palliative oncology, focusing on hematologic malignancies. Palliative oncology here refers to integrating symptom management, communication, and psychosocial support within oncology throughout the disease trajectory. Highlighting current capabilities, future promise, and areas requiring human judgment, the article aims to clarify practical AI applications for clinicians.

A systematic PubMed review was conducted, focusing on randomized controlled trials (RCTs) from 2023-2024 for AI in palliative care and RCTs from 2000-2024 for AI in hematologic malignancies. Relevant studies were selected based on applicability.

Recent clinical trials demonstrate AI’s potential in palliative care. AI decision support tools increased palliative care consultations and reduced readmissions. ML-triggered behavioral prompts improved serious illness discussions and curtailed aggressive therapies near end of life. NLP enhanced the accurate documentation of goals-of-care conversations, reducing manual workload. AI combined with wearable devices predicted short-term mortality in terminal cancer patients with high accuracy, underscoring the value of continuous physiological monitoring. Mobile patient-administered recordings improved informed decision-making and awareness of palliative care options. Collectively, these findings underscore AI’s role in optimizing patient care and clinical efficiency.

In symptom management, chronic pain—a major contributor to patient burden and healthcare costs—can benefit from AI’s pattern recognition capabilities. AI models, especially random forests, have effectively predicted pain intensity, opioid response, and chronic pain risk, outperforming traditional methods. Automated pain assessments using facial expressions, language, posture, and neurophysiological signals have standardized and improved accuracy. Clustering algorithms stratify patients by shared pain characteristics, guiding personalized treatments. AI has also been applied to other symptoms including fatigue, depression, anxiety, and gastrointestinal distress through analysis of electronic health records (EHRs) and patient-reported data. Wearable sensors provide real-time monitoring of vital signs and movement, correlating with survival outcomes and assisting prognostication and care planning.

AI supports decision-making in palliative care by predicting therapy responses from genomic data and clinical variables, assisting prognosis discussions, and optimizing drug dosing to reduce adverse effects. Algorithm-based predictions of patient preferences offer decision support when patients cannot express their wishes, though they require careful oversight to avoid bias from incomplete or subjective records. AI-powered telehealth tools extend care access in remote or underserved areas, facilitating virtual consultations and symptom monitoring.

In hematologic malignancies, AI applications span disease classification, prognosis, treatment optimization, and response assessment. Programs like LEAP in chronic myeloid leukemia guide personalized tyrosine kinase inhibitor therapy, improving survival. ML models aid relapse prediction, minimal residual disease detection, and response evaluation in acute myeloid leukemia and myelodysplastic syndromes. In lymphoma, DL and random forest models enhance diagnostic accuracy and risk stratification beyond conventional methods. Myeloma research uses machine and deep learning to predict survival and adverse events; AI-assisted PET/CT imaging quantifies bone marrow involvement, supporting treatment planning.

AI enhances symptom management in hematologic cancers by predicting complications such as anemia, infections, and treatment toxicities through EHR data and wearable devices. Noninvasive hemoglobin estimation via smartphone imaging and ML models predicting transfusion needs facilitate tailored interventions. AI-driven pain assessment tools inform medication strategies. Models also predict febrile neutropenia, sepsis, and respiratory distress, enabling early intervention. Psychological symptom detection through NLP analysis of notes and patient data supports timely mental health care, while AI chatbots provide ongoing symptom and medication support.

Personalized care is furthered by AI analytics differentiating patients by symptom burden and treatment response, guiding palliative interventions and therapy adjustments. AI-based monitoring tools track vital signs and adverse effects to enable proactive management. AI-driven decision support in transfusion medicine reduces unnecessary procedures while maintaining safety.

Accurate prognostication and risk stratification are critical in hematologic malignancies. AI integrates clinical, genomic, and imaging data, improving predictions of mortality, relapse, and treatment outcomes beyond traditional models. In stem cell transplantation, AI models predict graft-versus-host disease severity, transplant mortality, and relapse to tailor patient selection and management. AI also supports survivorship care by monitoring late treatment complications and optimizing follow-up schedules. At end of life, AI prognostic models identify candidates for early palliative care referrals, enhancing care quality.

Ethical considerations include data privacy, bias, consent, and preserving patient autonomy. AI should augment, not replace, clinical judgment and empathy in palliative care. Transparency, explainability, and continuous validation are essential to minimize disparities and avoid unintended care limitations. Clinician training, infrastructure support, and regulatory clarity are needed to ensure responsible AI adoption.

AI holds substantial promise to transform palliative oncology and hematologic cancer care by improving diagnostics, symptom management, prognostication, and personalized treatments. Future research should emphasize prospective studies and interdisciplinary collaboration to integrate AI effectively and ethically, ultimately enhancing patient-centered care across disease trajectories.

Artificial intelligence (AI) is increasingly recognized for its transformative potential in medicine, aiding physicians and patients across various aspects of care. Its integration into health care aims to enhance personalization and improve patient outcomes while addressing challenges such as rising costs and resource limitations. AI applications currently support treatment planning, disease diagnosis, outcome prediction, operational efficiency, and patient monitoring. Despite growing interest, cautious adoption persists in the medical community, often due to concerns regarding accuracy, ethical considerations, and unfamiliarity.

Fundamentally, AI involves computer systems designed to learn from data to detect patterns and assist decision-making. Common AI models include machine learning (ML), which processes structured data like lab results, and deep learning (DL), which handles complex inputs such as medical imaging and clinical notes. Natural language processing (NLP) aids in interpreting written language, whereas neural networks underpin image recognition and predictive models. Studies have demonstrated AI’s diagnostic capabilities, for example, deep convolutional neural networks matching dermatologist performance in skin cancer classification and ML algorithms predicting cardiac function with high accuracy and speed.

Noteworthy advances include AlphaFold, a DL system predicting protein 3D structures to aid drug discovery, and AI systems analyzing large-scale textual data during the COVID-19 pandemic for outbreak detection and contact tracing. Nevertheless, knowledge gaps remain regarding AI’s holistic role in clinical care. Concerns around reliability, accuracy, limited prospective validation, patient autonomy, and integration training continue to temper enthusiasm.

In palliative care, which focuses on quality of life rather than cure, AI offers promise in symptom management, treatment planning, and understanding patient needs. This is especially relevant for blood cancer patients, who often experience complex symptoms and unpredictable disease courses. However, research specifically addressing AI’s role in palliative care for hematologic malignancies remains limited.

A systematic review identified recent randomized controlled trials (RCTs) demonstrating AI’s benefits in palliative care, including increased palliative consultations, improved serious illness conversations, accurate NLP-based documentation of goals-of-care discussions, mortality prediction using wearables, and enhanced patient decision-making with mobile recordings. These findings highlight AI’s potential to improve patient outcomes, clinical efficiency, and patient-clinician communication.

Chronic pain, prevalent in cancer patients, poses substantial challenges. AI facilitates pain management by analyzing large datasets to classify pain, predict opioid responsiveness, and identify high-risk patients. AI techniques assessing facial expressions, language, posture, and neurophysiological signals provide standardized, objective pain assessment. Patient clustering algorithms enable tailored treatments and predictive models forecast individual therapeutic responses. Additionally, AI addresses other symptoms common in palliative settings, such as fatigue, depression, anxiety, and nausea, by detecting distress through clinical records and EHR data analysis. Wearable devices monitor vital signs, correlating movement data with survival likelihood, supporting prognostication and timely care planning.

AI-based predictive models enhance decision-making by forecasting chemotherapy responses with over 80% accuracy and optimizing drug dosing based on genetic and clinical profiles. These models also aid when patients cannot express preferences, generating personalized care predictions to align treatments with patient values, potentially reducing family burden. AI-driven telehealth extends access to palliative care in remote or underserved regions, enabling symptom monitoring and virtual consultations.

In hematologic malignancies, AI facilitates disease classification, prognosis prediction, treatment optimization, and response assessment. For leukemia, platforms like the LEukemia Artificial Intelligence Program (LEAP) personalize tyrosine kinase inhibitor therapy, improving survival. ML models enhance relapse prediction and minimal residual disease detection in acute myeloid leukemia. In lymphoma, deep learning and random forest models outperform traditional approaches in disease classification and risk stratification. AI applications in multiple myeloma predict survival outcomes and assess bone marrow involvement through PET/CT imaging.

Diagnostic advancements include AI algorithms achieving over 90% accuracy in detecting acute myeloid leukemia from blood smears and malignant lymphoma from histopathology images, outperforming pathologists. In multiple myeloma, AI systems using routine laboratory data provide early and accurate diagnosis. PET/CT analysis using AI quantifies marrow infiltration to inform prognosis and treatment.

Symptom management in hematologic cancers benefits from AI through early detection and personalized care using EHRs, wearable devices, and physiological monitoring. AI predicts hemoglobin trends, optimizes transfusion timing, and guides pain control strategies. Models also forecast complications such as febrile neutropenia and sepsis, enabling early interventions. Psychological and emotional support is enhanced via NLP analysis of clinical notes and AI-driven chatbots offering personalized symptom management and medication reminders.

AI-driven analytics stratify patients by symptom burden, treatment response, and prognosis to tailor palliative interventions and therapy adjustments. Wearable AI monitors vital signs and treatment toxicities, facilitating proactive symptom management. Integration into clinical practice involves combining AI tools with EHR systems, clinician training, and interdisciplinary collaboration.

For prognostication and risk stratification, AI models integrate genomic, imaging, and clinical data to improve survival predictions and guide treatment decisions in acute myeloid leukemia, lymphoma, and multiple myeloma. AI predicts graft-versus-host disease severity and transplant outcomes, supporting personalized transplantation strategies. Posttreatment monitoring using AI tracks late complications and directs follow-up care. End-of-life care benefits from AI prognostic models that identify patients who may benefit from early palliative services, supporting timely interventions and enhancing quality of life.

Ethical considerations include concerns about data privacy, bias, informed consent, and the preservation of patient autonomy. AI must supplement, not replace, clinical judgment, empathy, and individualized care. Transparency, explainability, and continuous validation of AI models are critical to mitigate bias and ensure equitable care. Real-world challenges include clinician resistance, insufficient training, and infrastructure limitations. Prospective validation and clear regulatory guidance are essential for safe and effective AI integration.

AI has the potential to revolutionize palliative oncology and hematologic malignancy care by enhancing diagnostics, symptom management, personalized treatment, and care delivery. Ongoing research and interdisciplinary efforts are needed to refine AI applications and ensure they complement clinical practice, ultimately improving patient-centered outcomes in hematologic cancers.