Artificial Intelligence in Healthcare: Lost In Translation?

• URL: http://arxiv.org/abs/2107.13454v1
• Date: Wed, 28 Jul 2021 16:10:40 GMT
• Title: Artificial Intelligence in Healthcare: Lost In Translation?
• Authors: Vince I. Madai and David C. Higgins
• Abstract summary: We highlight the major areas, where we observe current challenges for translation in AI in healthcare. Our work will lead to improved translation of AI in healthcare products into the clinical setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Artificial intelligence (AI) in healthcare is a potentially revolutionary tool to achieve improved healthcare outcomes while reducing overall health costs. While many exploratory results hit the headlines in recent years there are only few certified and even fewer clinically validated products available in the clinical setting. This is a clear indication of failing translation due to shortcomings of the current approach to AI in healthcare. In this work, we highlight the major areas, where we observe current challenges for translation in AI in healthcare, namely precision medicine, reproducible science, data issues and algorithms, causality, and product development. For each field, we outline possible solutions for these challenges. Our work will lead to improved translation of AI in healthcare products into the clinical setting

Related papers

• The Dual Impact of Artificial Intelligence in Healthcare: Balancing Advancements with Ethical and Operational Challenges [1.3302498881305604]
  This paper takes a close look at how AI is transforming areas such as diagnostics, precision medicine, and drug discovery. Issues like patient privacy, safety, and the fairness of AI decisions are explored to understand whether AI in healthcare is a positive force, a potential risk, or perhaps both.
  arXiv  Detail & Related papers  (2024-11-08T23:36:16Z)
• AI-driven innovation in medicaid: enhancing access, cost efficiency, and population health management [1.4802369202548666]
  The U.S. Medicaid program is experiencing critical challenges that include rapidly increasing healthcare costs, uneven care accessibility, and the challenge associated with addressing a varied set of population health needs. This paper investigates the transformative potential of Artificial Intelligence (AI) in reshaping Medicaid by streamlining operations, improving patient results, and lowering costs.
  arXiv  Detail & Related papers  (2024-10-11T07:14:42Z)
• AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias [2.398440840890111]
  AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions. These advancements also introduce substantial ethical and fairness challenges. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups.
  arXiv  Detail & Related papers  (2024-07-29T02:39:17Z)
• Towards Clinical AI Fairness: Filling Gaps in the Puzzle [15.543248260582217]
  This review systematically pinpoints several deficiencies concerning both healthcare data and the provided AI fairness solutions. We highlight the scarcity of research on AI fairness in many medical domains where AI technology is increasingly utilized. To bridge these gaps, our review advances actionable strategies for both the healthcare and AI research communities.
  arXiv  Detail & Related papers  (2024-05-28T07:42:55Z)
• 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)
• Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness [47.51360338851017]
  Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task.
  arXiv  Detail & Related papers  (2023-11-19T03:29:45Z)
• 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)
• 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 Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)
• Explainable AI meets Healthcare: A Study on Heart Disease Dataset [0.0]
  The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques. Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness.
  arXiv  Detail & Related papers  (2020-11-06T05:18:43Z)

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.