Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations

Sevgili, Seda Ardahan; Şenol, Selmin

doi:10.1038/s41390-022-02356-6

Cited by 4 publications

(6 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Already, there have been AI‐based applications to optical coherence tomography, which demonstrate improvements in automated image pattern recognition 11 . Some clear assets of AI include natural language processing eliminating human variability in translating information from the medical record, assisting clinician decision‐making processes for managing complications, and phenotypic prediction 12–14 …”

Section: Discussionmentioning

confidence: 99%

“…11 Some clear assets of AI include natural language processing eliminating human variability in translating information from the medical record, assisting clinician decision-making processes for managing complications, and phenotypic prediction. [12][13][14] Of the various types of AI that have emerged, each type of AI has its own strengths, limitations, and applications. Whereas DL is catered to the development of tailored therapies, ML is believed to be most applicable to clinical trials.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence in rare disease diagnosis and treatment

Wojtara,

Rana,

Rahman

et al. 2023

Clinical Translational Sci

View full text Add to dashboard Cite

Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient analysis of vast datasets, identifying patterns, and generating key insights. Predictions can then be made for medical diagnosis and personalized treatment recommendations. The use of AI can bypass some conventional limitations associated with rare diseases. Namely, it can optimize traditional randomized control trials, and may eventually reduce costs for drug research and development. Recent advancements have enabled researchers to train models based on large datasets and then fine‐tune these models on smaller datasets typically associated with rare diseases. In this mini‐review, we discuss recent advancements in AI and how AI can be applied to streamline rare disease diagnosis and optimize treatment.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in rare disease diagnosis and treatment

Wojtara,

Rana,

Rahman

et al. 2023

Clinical Translational Sci

View full text Add to dashboard Cite

show abstract

“…These treatments are generally personalized and depend on age, risk, and type of cancer. Much research has been conducted for diagnosing staging and prognosis, but research surrounding targeted therapies and their complications remains limited [11,12]. Nonetheless, these limited studies have created remarkable potential for evaluating pediatric cancers and possible treatments.…”

Section: Pediatric Oncologymentioning

confidence: 99%

“…Some trials have been conducted for these patients with acute lymphoblastic leukemia (ALL) through utilizing leukemia samples for identifying Ph-like expression and its related variants [13]. Targeted therapies have also been determined through these successful trials, but more post-treatment complications should be monitored and recorded for patients with varying survival rates [12].…”

Section: Pediatric Oncologymentioning

confidence: 99%

AI Applications in Pediatrics: Past, Present, and Future

Pathak,

Saikia,

Lim-Dy

2024

J Inter Med Res & Rep

View full text Add to dashboard Cite

In pediatric medicine, Artificial Intelligence (AI) currently remains as a novel area for research and development. Most AI applications created for adults are often complicated to fully transfer over to pediatrics due to several limiting factors. These factors include physiological differences, lack of data, and unknown implications. Nevertheless, applications in adult medicine have influenced extensive reviews for algorithms that show significant potential in pediatrics. As pediatric data gradually increases, several AI algorithms for adults can be fine-tuned to provide similar accuracies and sensitivities for diagnosing specific pediatric diseases. This chapter will discuss the pioneering applications of AI in medicine as well as current and arising applications or subspecialties. Areas of discussion encompass precision medicine, oncology, cardiology, radiology, chatbots, neurology, and ophthalmology. AI algorithms within these domains range from assistance with diagnoses, shorter waiting times, to continuous monitoring. Common benefits exhibited by many applications include increased accuracy, time and cost efficiency, patient compliance, and flexibility. For example, medical wearable technology and chatbots are two AI applications that can provide certain patient care in both outpatient and inpatient settings. These algorithms are convenient to use and offer continuous monitoring when a provider may not be readily available. Furthermore, other topics explored in this chapter relate to common concerns for using AI in pediatric medicine and education for health professionals, parents, and children. Most apprehension pertains to the misuse of data and quality of care. Reviewing these concerns is crucial for AI to exponentially flourish in medicine. Without established comprehension, hesitance and distrust may prevail over the promising potential AI can offer. Education and training can help alleviate these common misunderstandings by explaining how AI can affect both medicine and health care. Moreover, learning environments can allow health professionals, parents, and children to openly communicate their concerns that could guide ethical frameworks for developing AI applications. Analyzing these past, present, future applications, and related concerns can help demonstrate how AI will transform pediatric medicine.

show abstract

“…A large body of literature has extensively investigated and documented the potential off-target toxicities of chemotherapeutic agents, as well as the underlying principles that contribute to the complications induced by chemotherapy 8 , 9 , 10 . The prevalence of comorbid disorders within cancer patients has also garnered the attention and interest of oncologists and scientists, and increasing evidence has shed light on their potential influence on and intimate relationship with cancer 11 , 12 .…”

Section: Introductionmentioning

confidence: 99%

Complications and comorbidities associated with antineoplastic chemotherapy: Rethinking drug design and delivery for anticancer therapy

Mao,

Wu,

Huang

et al. 2024

Acta Pharmaceutica Sinica B

View full text Add to dashboard Cite

Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations

Cited by 4 publications

References 49 publications

Artificial intelligence in rare disease diagnosis and treatment

Artificial intelligence in rare disease diagnosis and treatment

AI Applications in Pediatrics: Past, Present, and Future

Complications and comorbidities associated with antineoplastic chemotherapy: Rethinking drug design and delivery for anticancer therapy

Contact Info

Product

Resources

About