Functional requirements to mitigate the Risk of Harm to Patients from Artificial Intelligence in Healthcare

• URL: http://arxiv.org/abs/2309.10424v1
• Date: Tue, 19 Sep 2023 08:37:22 GMT
• Title: Functional requirements to mitigate the Risk of Harm to Patients from Artificial Intelligence in Healthcare
• Authors: Juan M. Garc\'ia-G\'omez, Vicent Blanes-Selva, Jos\'e Carlos de Bartolom\'e Cenzano, Jaime Cebolla-Cornejo and Ascensi\'on Do\~nate-Mart\'inez
• Abstract summary: This study proposes 14 functional requirements that AI systems may implement to reduce the risks associated with their medical purpose. Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Directorate General for Parliamentary Research Services of the European Parliament has prepared a report to the Members of the European Parliament where they enumerate seven main risks of Artificial Intelligence (AI) in medicine and healthcare: patient harm due to AI errors, misuse of medical AI tools, bias in AI and the perpetuation of existing inequities, lack of transparency, privacy and security issues, gaps in accountability, and obstacles in implementation. In this study, we propose fourteen functional requirements that AI systems may implement to reduce the risks associated with their medical purpose: AI passport, User management, Regulation check, Academic use only disclaimer, data quality assessment, Clinicians double check, Continuous performance evaluation, Audit trail, Continuous usability test, Review of retrospective/simulated cases, Bias check, eXplainable AI, Encryption and use of field-tested libraries, and Semantic interoperability. Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.

Related papers

• Safety challenges of AI in medicine [23.817939398729955]
  Review examines potential risks in AI practices that may compromise safety in medicine. Examines reduced performance across diverse populations, inconsistent operational stability, the need for high-quality data for effective model tuning, and the risk of data breaches during model development and deployment. Second part of this article explores safety issues specific to large language models (LLMs) in medical contexts.
  arXiv  Detail & Related papers  (2024-09-11T13:47:47Z)
• 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)
• 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)
• Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations [0.0]
  Generative AI is rapidly transforming medical imaging and text analysis. This paper explores issues of accuracy, informed consent, data privacy, and algorithmic limitations. We aim to foster a roadmap for ethical and responsible implementation of generative AI in healthcare.
  arXiv  Detail & Related papers  (2024-06-15T13:28:07Z)
• Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology [35.284458448940796]
  Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. Recent AI developments demonstrate the feasibility of robustly detecting NGT placement from Chest X-ray images. We present a human-centered approach to the problem and describe insights derived following contextual inquiry and in-depth interviews with 15 clinical stakeholders.
  arXiv  Detail & Related papers  (2024-05-08T14:16:22Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• RAISE -- Radiology AI Safety, an End-to-end lifecycle approach [5.829180249228172]
  The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency. The focus should be on ensuring models meet the highest standards of safety, effectiveness and efficacy. The roadmap presented herein aims to expedite the achievement of deployable, reliable, and safe AI in radiology.
  arXiv  Detail & Related papers  (2023-11-24T15:59:14Z)
• 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)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

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.