The doctor will polygraph you now: ethical concerns with AI for fact-checking patients
- URL: http://arxiv.org/abs/2408.07896v1
- Date: Thu, 15 Aug 2024 02:55:30 GMT
- Title: The doctor will polygraph you now: ethical concerns with AI for fact-checking patients
- Authors: James Anibal, Jasmine Gunkel, Hannah Huth, Hang Nguyen, Shaheen Awan, Yael Bensoussan, Bradford Wood,
- Abstract summary: Clinical artificial intelligence (AI) methods have been proposed for predicting social behaviors which could be reasonably understood from patient-reported data.
This raises ethical concerns about respect, privacy, and patient awareness/control over how their health data is used.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Clinical artificial intelligence (AI) methods have been proposed for predicting social behaviors which could be reasonably understood from patient-reported data. This raises ethical concerns about respect, privacy, and patient awareness/control over how their health data is used. Ethical concerns surrounding clinical AI systems for social behavior verification were divided into three main categories: (1) the use of patient data retrospectively without informed consent for the specific task of verification, (2) the potential for inaccuracies or biases within such systems, and (3) the impact on trust in patient-provider relationships with the introduction of automated AI systems for fact-checking. Additionally, this report showed the simulated misuse of a verification system and identified a potential LLM bias against patient-reported information in favor of multimodal data, published literature, and the outputs of other AI methods (i.e., AI self-trust). Finally, recommendations were presented for mitigating the risk that AI verification systems will cause harm to patients or undermine the purpose of the healthcare system.
Related papers
- Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering [51.26412822853409]
We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models.
Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs.
arXiv Detail & Related papers (2024-10-23T00:31:17Z) - Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts [1.9374282535132377]
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare.
We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes.
There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures.
arXiv Detail & Related papers (2024-03-14T15:58:13Z) - Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework [13.215318138576713]
The paper reviews interpretable AI processes, methods, applications, and the challenges of implementation in healthcare.
It aims to foster a comprehensive understanding of the crucial role of a robust interpretability approach in healthcare.
arXiv Detail & Related papers (2023-11-18T12:29:18Z) - Functional requirements to mitigate the Risk of Harm to Patients from
Artificial Intelligence in Healthcare [0.0]
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.
arXiv Detail & Related papers (2023-09-19T08:37:22Z) - Informing clinical assessment by contextualizing post-hoc explanations
of risk prediction models in type-2 diabetes [50.8044927215346]
We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state.
We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability.
Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
arXiv Detail & Related papers (2023-02-11T18:07:11Z) - 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) - Leveraging Clinical Context for User-Centered Explainability: A Diabetes
Use Case [4.520155732176645]
We implement a proof-of-concept (POC) in type-2 diabetes (T2DM) use case where we assess the risk of chronic kidney disease (CKD)
Within the POC, we include risk prediction models for CKD, post-hoc explainers of the predictions, and other natural-language modules.
Our POC approach covers multiple knowledge sources and clinical scenarios, blends knowledge to explain data and predictions to PCPs, and received an enthusiastic response from our medical expert.
arXiv Detail & Related papers (2021-07-06T02:44:40Z) - Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration [55.88616573143478]
Outcome prediction from clinical text can prevent doctors from overlooking possible risks.
Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets.
We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
arXiv Detail & Related papers (2021-02-08T10:26:44Z) - Privacy-preserving medical image analysis [53.4844489668116]
We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging.
We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets.
We empirically evaluate the framework's security against a gradient-based model inversion attack.
arXiv Detail & Related papers (2020-12-10T13:56:00Z) - 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)
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.