Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina, Michaela; Cè, Maurizio; Khenkina, Natallia; Sinichich, Polina; Cervelli, Marco; Poggi, Vittoria; Boemi, Sara; Ierardi, Anna Maria; Carrafiello, Gianpaolo

doi:10.1177/15330338221141793

Cited by 7 publications

(2 citation statements)

References 135 publications

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“…An interdisciplinary application of ML lies in cancer diagnostics, risk stratification, and prognosis [62,63]. These aspects play a crucial role in comprehending the progression of diseases and determining survival probabilities.…”

Section: Diagnosis and Clinical Decision Makingmentioning

confidence: 99%

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Bekbolatova,

Mayer,

Ong

et al. 2024

Healthcare

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Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

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Section: Diagnosis and Clinical Decision Makingmentioning

confidence: 99%

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Bekbolatova,

Mayer,

Ong

et al. 2024

Healthcare

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) is a term coined by John McCarthy in 1956 with the aim of describing the approach of using computers and technology to simulate intelligent behaviour and critical thinking comparable to a human [1]. Several improvements in different medical fields can stem from its potential in diagnosis, management, and treatment outcome prediction through the analysis and interpretation of complex data [2].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Cellina,

Cacioppa,

Cè

et al. 2023

Cancers

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Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

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