Applying Artificial Intelligence Prediction Tools for Advancing Precision Oncology in Immunotherapy: Future Perspectives in Personalized Care

Damane, Botle Precious; Mkhize-Kwitshana, Zilungile Lynette; Kgokolo, Mahlatse Cordelia; Luvhengo, Thifhelimbilu Emmanuel; Dlamini, Zodwa

doi:10.1007/978-3-031-21506-3_12

Cited by 2 publications

(3 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…30 Access to LLM chatbots as well as mobile apps are newer ways that can provide a patient greater access to their health information. 14 A recent study found similar accuracy between humans and ChatGPT in answering questions related to clinical genetics. However, it was noted that ChatGPT displayed poorer performance if critical thought was required.…”

Section: Challenges Of Efficiency In Clinical Workflowsmentioning

confidence: 91%

“…11 ML approaches traditionally used for biomarker discovery or immune-therapeutic response have been reviewed previously. [12][13][14][15] This review focuses on recent advancements in artificial intelligence (AI) and ML, namely state-of-the-art neural network architectures and related applications. We note that there are numerous reviews that have focused on validation of IO targets and development of effective immunotherapies.…”

Section: Introductionmentioning

confidence: 99%

“…We note that there are numerous reviews that have focused on validation of IO targets and development of effective immunotherapies. [12][13][14][15][16][17] Therefore, we have focused on other areas where AI/ML holds promise to improve IO in clinical care.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Just how transformative will AI/ML be for immuno-oncology?

Bottomly,

McWeeney

2024

J Immunother Cancer

View full text Add to dashboard Cite

Immuno-oncology involves the study of approaches which harness the patient’s immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.

show abstract

Section: Challenges Of Efficiency In Clinical Workflowsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Just how transformative will AI/ML be for immuno-oncology?

Bottomly,

McWeeney

2024

J Immunother Cancer

View full text Add to dashboard Cite

show abstract

Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence

Zakariya,

Salem,

Alamrain

et al. 2024

Eur J Med Res

View full text Add to dashboard Cite

Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale. With the robustness and advancement in research techniques, artificial intelligence (AI) is a potential tool that can help refine the mutanome-based immunotherapy for melanoma. Mutanome-based techniques are being employed in the development of immune-stimulating vaccines, improving current options such as adoptive cell treatment, and simplifying immunotherapy responses. Although the use of AI in these approaches is limited by data paucity, cost implications, flaws in AI inference capabilities, and the incapacity of AI to apply data to a broad population, its potential for improving immunotherapy is limitless. Thus, in-depth research on how AI might help the individualisation of immunotherapy utilising knowledge of mutanomes is critical, and this should be at the forefront of melanoma management.

show abstract

Applying Artificial Intelligence Prediction Tools for Advancing Precision Oncology in Immunotherapy: Future Perspectives in Personalized Care

Cited by 2 publications

References 62 publications

Just how transformative will AI/ML be for immuno-oncology?

Just how transformative will AI/ML be for immuno-oncology?

Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence

Contact Info

Product

Resources

About