Artificial Intelligence-based Decision Support Systems for Precision and Digital Health
- URL: http://arxiv.org/abs/2407.16062v1
- Date: Mon, 22 Jul 2024 21:39:34 GMT
- Title: Artificial Intelligence-based Decision Support Systems for Precision and Digital Health
- Authors: Nina Deliu, Bibhas Chakraborty,
- Abstract summary: We discuss the opportunity offered by AI, more specifically reinforcement learning, to current trends in healthcare.
We focus on the area of adaptive interventions.
This article has undergone anonymous review and is intended as a book chapter for the volume "Frontiers of Statistics and Data Science"
- Score: 0.49109372384514843
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice. This invited article has undergone anonymous review and is intended as a book chapter for the volume "Frontiers of Statistics and Data Science" edited by Subhashis Ghoshal and Anindya Roy for the International Indian Statistical Association Series on Statistics and Data Science, published by Springer. It covers the material from a short course titled "Artificial Intelligence in Precision and Digital Health" taught by the author Bibhas Chakraborty at the IISA 2022 Conference, December 26-30 2022, at the Indian Institute of Science, Bengaluru.
Related papers
- Data-Driven Analysis of AI in Medical Device Software in China: Deep Learning and General AI Trends Based on Regulatory Data [3.1485639585141114]
We leverage a data-driven approach to automatically extract and analyze AI-enabled medical devices.
More than 4 million entries were assessed, identifying 2,174 MDSW registrations.
Leading medical specialties utilizing AIMD include respiratory (20.5%), ophthalmology/endocrinology (12.8%), and orthopedics (10.3%)
arXiv Detail & Related papers (2024-11-11T21:28:50Z) - A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.
As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.
The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
arXiv Detail & Related papers (2024-05-21T06:44:40Z) - An Explainable AI Framework for Artificial Intelligence of Medical
Things [2.7774194651211217]
We leverage a custom XAI framework, incorporating techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-Cam)
The proposed framework enhances the effectiveness of strategic healthcare methods and aims to instill trust and promote understanding in AI-driven medical applications.
We apply the XAI framework to brain tumor detection as a use case demonstrating accurate and transparent diagnosis.
arXiv Detail & Related papers (2024-03-07T01:08:41Z) - A survey of recent methods for addressing AI fairness and bias in
biomedicine [48.46929081146017]
Artificial intelligence systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender.
We surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV)
We performed a literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords.
We reviewed other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness.
arXiv Detail & Related papers (2024-02-13T06:38:46Z) - Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare.
The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body.
HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed.
Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
arXiv Detail & Related papers (2024-01-22T03:17:41Z) - Safe AI for health and beyond -- Monitoring to transform a health
service [51.8524501805308]
We will assess the infrastructure required to monitor the outputs of a machine learning algorithm.
We will present two scenarios with examples of monitoring and updates of models.
arXiv Detail & Related papers (2023-03-02T17:27:45Z) - HEAR4Health: A blueprint for making computer audition a staple of modern
healthcare [89.8799665638295]
Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems.
Computer audition can be seen to be lagging behind, at least in terms of commercial interest.
We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data.
arXiv Detail & Related papers (2023-01-25T09:25:08Z) - The stochastic digital human is now enrolling for in silico imaging
trials -- Methods and tools for generating digital cohorts [0.0]
In silico imaging trials are computational studies that seek to ascertain the performance of a medical device.
The benefits of in silico trials for evaluating new technology include significant resource and time savings.
To conduct in silico trials, digital representations of humans are needed.
arXiv Detail & Related papers (2023-01-20T18:31:22Z) - Explainable AI for clinical and remote health applications: a survey on
tabular and time series data [3.655021726150368]
It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare.
This paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality.
arXiv Detail & Related papers (2022-09-14T10:01:29Z) - BiteNet: Bidirectional Temporal Encoder Network to Predict Medical
Outcomes [53.163089893876645]
We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey.
An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys.
We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
arXiv Detail & Related papers (2020-09-24T00:42:36Z) - Survey of XAI in digital pathology [3.4591414173342643]
We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs.
We give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging.
In doing, we incorporate uncertainty estimation methods as an integral part of the XAI landscape.
arXiv Detail & Related papers (2020-08-14T13:11:54Z)
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.