OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical
settings
- URL: http://arxiv.org/abs/2109.04004v1
- Date: Thu, 9 Sep 2021 02:59:36 GMT
- Title: OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical
settings
- Authors: Yunyou Huang, Nana Wang, Suqin Tang, Li Ma, Tianshu Hao, Zihan Jiang,
Fan Zhang, Guoxin Kang, Xiuxia Miao, Xianglong Guan, Ruchang Zhang, Zhifei
Zhang and Jianfeng Zhan
- Abstract summary: We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches.
We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings.
In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system.
- Score: 11.287929392365756
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper quantitatively reveals the state-of-the-art and
state-of-the-practice AI systems only achieve acceptable performance on the
stringent conditions that all categories of subjects are known, which we call
closed clinical settings, but fail to work in real-world clinical settings.
Compared to the diagnosis task in the closed setting, real-world clinical
settings pose severe challenges, and we must treat them differently. We build a
clinical AI benchmark named Clinical AIBench to set up real-world clinical
settings to facilitate researches. We propose an open, dynamic machine learning
framework and develop an AI system named OpenClinicalAI to diagnose diseases in
real-world clinical settings. The first versions of Clinical AIBench and
OpenClinicalAI target Alzheimer's disease. In the real-world clinical setting,
OpenClinicalAI significantly outperforms the state-of-the-art AI system. In
addition, OpenClinicalAI develops personalized diagnosis strategies to avoid
unnecessary testing and seamlessly collaborates with clinicians. It is
promising to be embedded in the current medical systems to improve medical
services.
Related papers
- Framework for developing and evaluating ethical collaboration between expert and machine [4.304304889487245]
Precision medicine is a promising approach for accessible disease diagnosis and personalized intervention planning.
By leveraging artificial intelligence (AI), precision medicine tailors diagnosis and treatment solutions to individual patients.
However, the adoption of AI in medical applications faces significant challenges.
This paper proposes a framework to develop and ethically evaluate expert-guided multi-modal AI.
arXiv Detail & Related papers (2024-11-17T06:49:38Z) - Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
This study focuses on the clinical evaluation of medical Synthetic Data Generation using Artificial Intelligence (AI) models.
The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis.
The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools.
arXiv Detail & Related papers (2024-10-31T19:48:50Z) - Establishing Rigorous and Cost-effective Clinical Trials for Artificial Intelligence Models [18.240773244542474]
A profound gap persists between artificial intelligence (AI) and clinical practice in medicine.
State-of-the-art and state-of-the-practice AI model evaluations are limited to laboratory studies on medical datasets or direct clinical trials with no or solely patient-centered controls.
For the first time, we emphasize the critical necessity for rigorous and cost-effective evaluation methodologies for AI models in clinical practice.
arXiv Detail & Related papers (2024-07-11T14:37:08Z) - TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design.
We provide basic validation methods for each task to ensure the datasets' usability and reliability.
We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
arXiv Detail & Related papers (2024-06-30T09:13:10Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare [73.78776682247187]
Concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI.
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
arXiv Detail & Related papers (2023-08-11T10:49:05Z) - OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease
Diagnosis [11.775648630734949]
Alzheimer's disease (AD) cannot be reversed or cured, but timely diagnosis can significantly reduce the burden of treatment and care.
Current research on AD diagnosis models usually regards the diagnosis task as a typical classification task.
We propose OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical settings.
arXiv Detail & Related papers (2023-07-03T12:35:03Z) - AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in
Healthcare with Automated Machine Learning [72.2614468437919]
We present a machine learning framework, AutoPrognosis 2.0, to develop diagnostic and prognostic models.
We provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank.
Our risk score has been implemented as a web-based decision support tool and can be publicly accessed by patients and clinicians worldwide.
arXiv Detail & Related papers (2022-10-21T16:31:46Z) - Inheritance-guided Hierarchical Assignment for Clinical Automatic
Diagnosis [50.15205065710629]
Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making.
We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
arXiv Detail & Related papers (2021-01-27T13:16:51Z) - An Overview on the Web of Clinical Data [12.52352583112911]
The Web of Clinical Data (WCD) is a universal repository of clinical hyperlinked data.
The WCD will dramatically change the AI approach to medicine and its effectiveness.
arXiv Detail & Related papers (2020-08-14T17:34:05Z) - Bridging the gap between AI and Healthcare sides: towards developing
clinically relevant AI-powered diagnosis systems [18.95904791202457]
We hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics.
Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adrial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training.
arXiv Detail & Related papers (2020-01-12T12:45:46Z)
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.