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Topic Review
Artificial Intelligence and Lung Cancer
Lung cancer is the second most common cancer in both males and females, with the highest mortality worldwide, causing 21% of total cancer-related deaths. The notion of artificial intelligence (AI) was initially proposed by John McCarthy in 1956. It involves using computer systems and technology to replicate human-like intelligent behavior and critical thinking abilities. In the realm of medicine, AI is divided into two main categories: virtual and physical. The virtual branch is further categorized into machine learning (ML) and deep learning (DL).
  • 210
  • 14 Nov 2023
Topic Review
Artificial Intelligence and Machine Learning in Stroke Care
Stroke is an emergency for which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke.
  • 435
  • 16 Jun 2023
Topic Review
Artificial Intelligence Application to Pancreas Imaging
Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65–75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. 
  • 338
  • 30 Oct 2023
Topic Review
Artificial Intelligence Applications in Interstitial Lung Diseases
Interstitial lung diseases (ILDs) comprise a rather heterogeneous group of diseases varying in pathophysiology, presentation, epidemiology, diagnosis, treatment and prognosis. In the majority of ILDs, imaging modalities and especially high-resolution Computed Tomography (CT) scans have been the cornerstone in patient diagnostic approach and follow-up. The intricate nature of ILDs and the accompanying data have led to an increasing adoption of artificial intelligence (AI) techniques, primarily on imaging data but also in genetic data, spirometry and lung diffusion, among others.
  • 303
  • 20 Jul 2023
Topic Review
Artificial Intelligence for Alzheimer’s Disease
The recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from Alzheimer's disease (AD) patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field.
  • 609
  • 03 Sep 2021
Topic Review
Artificial Intelligence for Gastrointestinal Diseases
The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited.
  • 617
  • 24 Sep 2021
Topic Review
Artificial Intelligence for Liver Transplant with Hepatocarcinoma
Hepatocellular carcinoma is the most common primary malignant hepatic tumor and occurs most often in the setting of chronic liver disease. Liver transplantation is a curative treatment option and is an ideal solution because it solves the chronic underlying liver disorder while removing the malignant lesion. Artificial intelligence is an emerging technology with multiple applications in medicine with a predilection for domains that work with medical imaging, like radiology. With the help of these technologies, laborious tasks can be automated, and new lesion imaging criteria can be developed based on pixel-level analysis.
  • 414
  • 26 May 2023
Topic Review
Artificial Intelligence for Surgeons
Computer vision (CV) is a field of artificial intelligence (AI) that deals with the automatic analysis of videos and images. Recent advances in AI and CV methods coupled with the growing availability of surgical videos of minimally invasive procedures have led to the development of AI-based algorithms to improve surgical care.
  • 424
  • 27 Aug 2022
Topic Review
Artificial Intelligence in Alzheimer’s Disease
Alzheimer’s disease (AD) represents most of the dementia cases and stands as the most common neurodegenerative disease. A shift from a curative to a preventive approach is imminent, and we are moving towards the application of personalized medicine, whereas we can shape the best clinical intervention for each patient at a given point. This new step in medicine requires the most recent tools and the analysis of huge amounts of data where the application of artificial intelligence (AI) plays a critical part in the depiction of disease-patient dynamics, critical to reach early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. 
  • 717
  • 02 Mar 2022
Topic Review
Artificial Intelligence in Brain Tumor Imaging
The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages of the diagnostic and therapeutic process. Although the histological investigation will remain difficult to replace, in the near future the radiomic approach will allow a complementary, repeatable and non-invasive characterization of the lesion, assisting oncologists and neurosurgeons in selecting the best therapeutic option and the correct molecular target in chemotherapy. AI-driven tools are already playing an important role in surgical planning, delimiting the extent of the lesion (segmentation) and its relationships with the brain structures, thus allowing precision brain surgery as radical as reasonably acceptable to preserve the quality of life. AI-assisted models allow the prediction of complications, recurrences and therapeutic response, suggesting the most appropriate follow-up.
  • 503
  • 20 Mar 2023
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