Future Artificial Intelligence tools and perspectives in medicine
- URL: http://arxiv.org/abs/2206.03289v1
- Date: Sat, 4 Jun 2022 11:27:43 GMT
- Title: Future Artificial Intelligence tools and perspectives in medicine
- Authors: Ahmad Chaddad, Yousef Katib, Lama Hassan
- Abstract summary: Currently, limited management of cancers benefits from artificial intelligence, mostly related to a computer-aided diagnosis that avoids a biopsy analysis that presents additional risks and costs.
This review explores the progress of AI-based radiomic tools for clinical applications with a brief description of necessary technical steps.
- Score: 1.7532045941271799
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Purpose of review: Artificial intelligence (AI) has become popular in medical
applications, specifically as a clinical support tool for computer-aided
diagnosis. These tools are typically employed on medical data (i.e., image,
molecular data, clinical variables, etc.) and used the statistical and machine
learning methods to measure the model performance. In this review, we
summarized and discussed the most recent radiomic pipeline used for clinical
analysis. Recent findings:Currently, limited management of cancers benefits
from artificial intelligence, mostly related to a computer-aided diagnosis that
avoids a biopsy analysis that presents additional risks and costs. Most AI
tools are based on imaging features, known as radiomic analysis that can be
refined into predictive models in non-invasively acquired imaging data. This
review explores the progress of AI-based radiomic tools for clinical
applications with a brief description of necessary technical steps. Explaining
new radiomic approaches based on deep learning techniques will explain how the
new radiomic models (deep radiomic analysis) can benefit from deep
convolutional neural networks and be applied on limited data sets. Summary: To
consider the radiomic algorithms, further investigations are recommended to
involve deep learning in radiomic models with additional validation steps on
various cancer types.
Related papers
- The Era of Foundation Models in Medical Imaging is Approaching : A Scoping Review of the Clinical Value of Large-Scale Generative AI Applications in Radiology [0.0]
Social problems stemming from the shortage of radiologists are intensifying, and artificial intelligence is being highlighted as a potential solution.
Recently emerging large-scale generative AI has expanded from large language models (LLMs) to multi-modal models.
This scoping review systematically organizes existing literature on the clinical value of large-scale generative AI applications.
arXiv Detail & Related papers (2024-09-03T00:48:50Z) - Automated Radiology Report Generation: A Review of Recent Advances [5.965255286239531]
Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation.
Recent advances in artificial intelligence have demonstrated great potential for automatic radiology report generation.
arXiv Detail & Related papers (2024-05-17T15:06:08Z) - CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models [3.8940162151291804]
This study introduces an innovative paradigm to create an assistive co-pilot system for empowering radiologists.
We develop a collaborative framework to integrate Large Language Models (LLMs) and medical image analysis tools.
arXiv Detail & Related papers (2024-04-11T01:33:45Z) - Radiology Report Generation Using Transformers Conditioned with
Non-imaging Data [55.17268696112258]
This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information.
The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
arXiv Detail & Related papers (2023-11-18T14:52:26Z) - ChatRadio-Valuer: A Chat Large Language Model for Generalizable
Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations.
The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations.
ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
arXiv Detail & Related papers (2023-10-08T17:23:17Z) - Radiology-Llama2: Best-in-Class Large Language Model for Radiology [71.27700230067168]
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning.
Quantitative evaluations using ROUGE metrics on the MIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves state-of-the-art performance.
arXiv Detail & Related papers (2023-08-29T17:44:28Z) - AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future
Directions [3.2071249735671348]
This review paper summarizes a large collection of articles related to artificial intelligence (AI)-based techniques used in the diagnosis of thyroid cancer.
The focus of this study is on how AI-based tools can support the diagnosis and treatment of thyroid cancer, through supervised, unsupervised, or hybrid techniques.
arXiv Detail & Related papers (2023-08-25T17:27:53Z) - Artificial Intelligence-Based Detection, Classification and
Prediction/Prognosis in PET Imaging: Towards Radiophenomics [2.2509387878255818]
This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging.
There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches.
Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors.
arXiv Detail & Related papers (2021-10-20T01:05:47Z) - BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer
Diagnosis in Breast Ultrasound Images [69.41441138140895]
This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images.
The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis.
Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice.
arXiv Detail & Related papers (2021-10-05T19:14:46Z) - Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype
Prediction [55.94378672172967]
We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients.
We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks.
Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
arXiv Detail & Related papers (2020-09-02T02:50:30Z) - Medical Instrument Detection in Ultrasound-Guided Interventions: A
Review [74.22397862400177]
This article reviews medical instrument detection methods in the ultrasound-guided intervention.
First, we present a comprehensive review of instrument detection methodologies, which include traditional non-data-driven methods and data-driven methods.
We discuss the main clinical applications of medical instrument detection in ultrasound, including anesthesia, biopsy, prostate brachytherapy, and cardiac catheterization.
arXiv Detail & Related papers (2020-07-09T13:50:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.