Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey
- URL: http://arxiv.org/abs/2408.12880v1
- Date: Fri, 23 Aug 2024 07:31:01 GMT
- Title: Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey
- Authors: Qika Lin, Yifan Zhu, Xin Mei, Ling Huang, Jingying Ma, Kai He, Zhen Peng, Erik Cambria, Mengling Feng,
- Abstract summary: We conduct a comprehensive survey of the current progress of medical multimodal learning from the perspectives of datasets, task-oriented methods, and universal foundation models.
We discuss the proposed question from five issues to explore the real impacts of advanced techniques in healthcare, from data and technologies to performance and ethics.
The answer is that current technologies have NOT achieved universal intelligence and there remains a significant journey to undertake.
- Score: 42.112157133171486
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid development of artificial intelligence has constantly reshaped the field of intelligent healthcare and medicine. As a vital technology, multimodal learning has increasingly garnered interest due to data complementarity, comprehensive modeling form, and great application potential. Currently, numerous researchers are dedicating their attention to this field, conducting extensive studies and constructing abundant intelligent systems. Naturally, an open question arises that has multimodal learning delivered universal intelligence in healthcare? To answer the question, we adopt three unique viewpoints for a holistic analysis. Firstly, we conduct a comprehensive survey of the current progress of medical multimodal learning from the perspectives of datasets, task-oriented methods, and universal foundation models. Based on them, we further discuss the proposed question from five issues to explore the real impacts of advanced techniques in healthcare, from data and technologies to performance and ethics. The answer is that current technologies have NOT achieved universal intelligence and there remains a significant journey to undertake. Finally, in light of the above reviews and discussions, we point out ten potential directions for exploration towards the goal of universal intelligence in healthcare.
Related papers
- From Screens to Scenes: A Survey of Embodied AI in Healthcare [31.183244202702983]
"EmAI in healthcare" spans diverse fields such as algorithms, robotics, and biomedicine.
We provide a comprehensive overview of the "brain" of EmAI for healthcare, wherein we introduce AI algorithms for perception, actuation, planning, and memory.
We discuss the technical barriers and explore ethical considerations, offering a forward-looking perspective on the future of EmAI in healthcare.
arXiv Detail & Related papers (2025-01-13T16:35:52Z) - Overview of TREC 2024 Medical Video Question Answering (MedVidQA) Track [19.06634510937997]
We introduce new tasks to foster research toward designing systems that can understand medical videos to provide visual answers to natural language questions.
These tasks have the potential to support the development of sophisticated downstream applications that can benefit the public and medical professionals.
arXiv Detail & Related papers (2024-12-15T05:18:01Z) - Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [129.08019405056262]
Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial Intelligence (AGI)
MLMs andWMs have attracted significant attention due to their remarkable perception, interaction, and reasoning capabilities.
In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
arXiv Detail & Related papers (2024-07-09T14:14:47Z) - Multimodal Federated Learning in Healthcare: a Review [5.983768682145731]
Federated Learning (FL) provides a decentralized mechanism where data need not be consolidated.
This paper outlines the current state-of-the-art approaches to Multimodal Federated Learning (MMFL) within the healthcare domain.
It aims to bridge the gap between cutting-edge AI technology and the imperative need for patient data privacy in healthcare applications.
arXiv Detail & Related papers (2023-10-14T19:43:06Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - Machine Unlearning: A Survey [56.79152190680552]
A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning.
This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality.
No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios.
The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
arXiv Detail & Related papers (2023-06-06T10:18:36Z) - Towards Medical Artificial General Intelligence via Knowledge-Enhanced
Multimodal Pretraining [121.89793208683625]
Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks.
We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
arXiv Detail & Related papers (2023-04-26T01:26:19Z) - Natural Language Processing for Smart Healthcare [21.059050223047926]
Natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language.
We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view.
In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios.
arXiv Detail & Related papers (2021-10-19T02:48:44Z) - Smart Healthcare in the Age of AI: Recent Advances, Challenges, and
Future Prospects [3.3336265497547126]
The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies.
This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment.
arXiv Detail & Related papers (2021-06-24T05:10:47Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z)
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.