AI as a Medical Ally: Evaluating ChatGPT's Usage and Impact in Indian
Healthcare
- URL: http://arxiv.org/abs/2401.15605v1
- Date: Sun, 28 Jan 2024 08:20:36 GMT
- Title: AI as a Medical Ally: Evaluating ChatGPT's Usage and Impact in Indian
Healthcare
- Authors: Aryaman Raina, Prateek Mishra, Harshit goyal, Dhruv Kumar
- Abstract summary: This study investigates the integration and impact of Large Language Models (LLMs), like ChatGPT, in India's healthcare sector.
Our findings reveal that healthcare professionals value ChatGPT in medical education and preliminary clinical settings, but exercise caution due to concerns about reliability, privacy, and the need for cross-verification with medical references.
General users show a preference for AI interactions in healthcare, but concerns regarding accuracy and trust persist.
- Score: 2.259877069661293
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This study investigates the integration and impact of Large Language Models
(LLMs), like ChatGPT, in India's healthcare sector. Our research employs a dual
approach, engaging both general users and medical professionals through surveys
and interviews respectively. Our findings reveal that healthcare professionals
value ChatGPT in medical education and preliminary clinical settings, but
exercise caution due to concerns about reliability, privacy, and the need for
cross-verification with medical references. General users show a preference for
AI interactions in healthcare, but concerns regarding accuracy and trust
persist. The study underscores the need for these technologies to complement,
not replace, human medical expertise, highlighting the importance of developing
LLMs in collaboration with healthcare providers. This paper enhances the
understanding of LLMs in healthcare, detailing current usage, user trust, and
improvement areas. Our insights inform future research and development,
underscoring the need for ethically compliant, user-focused LLM advancements
that address healthcare-specific challenges.
Related papers
- Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health [1.8772687384996551]
Large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI.
We showcase the power of LLMs in handling unstructured conversational data through four case studies.
arXiv Detail & Related papers (2024-06-19T16:02:04Z) - A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions [31.04135502285516]
Large language models (LLMs) have received substantial attention due to their impressive capabilities for generating and understanding human-level language.
LLMs have emerged as an innovative and powerful adjunct in the medical field, transforming traditional practices and heralding a new era of enhanced healthcare services.
arXiv Detail & Related papers (2024-06-06T03:15:13Z) - Healthcare Copilot: Eliciting the Power of General LLMs for Medical
Consultation [96.22329536480976]
We introduce the construction of a Healthcare Copilot designed for medical consultation.
The proposed Healthcare Copilot comprises three main components: 1) the Dialogue component, responsible for effective and safe patient interactions; 2) the Memory component, storing both current conversation data and historical patient information; and 3) the Processing component, summarizing the entire dialogue and generating reports.
To evaluate the proposed Healthcare Copilot, we implement an auto-evaluation scheme using ChatGPT for two roles: as a virtual patient engaging in dialogue with the copilot, and as an evaluator to assess the quality of the dialogue.
arXiv Detail & Related papers (2024-02-20T22:26:35Z) - 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) - Large language models in healthcare and medical domain: A review [4.456243157307507]
Large language models (LLMs) provide proficient responses to free-text queries.
This review explores the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications.
arXiv Detail & Related papers (2023-12-12T20:54:51Z) - 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) - ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences [51.66185471742271]
We propose ChiMed-GPT, a benchmark LLM designed explicitly for Chinese medical domain.
ChiMed-GPT undergoes a comprehensive training regime with pre-training, SFT, and RLHF.
We analyze possible biases through prompting ChiMed-GPT to perform attitude scales regarding discrimination of patients.
arXiv Detail & Related papers (2023-11-10T12:25:32Z) - A Survey of Large Language Models in Medicine: Progress, Application, and Challenge [85.09998659355038]
Large language models (LLMs) have received substantial attention due to their capabilities for understanding and generating human language.
This review aims to provide a detailed overview of the development and deployment of LLMs in medicine.
arXiv Detail & Related papers (2023-11-09T02:55:58Z) - Large Language Models Illuminate a Progressive Pathway to Artificial
Healthcare Assistant: A Review [16.008511195589925]
Large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning.
This paper provides a comprehensive review on the applications and implications of LLMs in medicine.
arXiv Detail & Related papers (2023-11-03T13:51:36Z) - The Design and Implementation of a National AI Platform for Public
Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
The Italian National Health Service is adopting Artificial Intelligence through its technical agencies.
Such a vast programme requires special care in formalising the knowledge domain.
Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
arXiv Detail & Related papers (2023-04-24T08:00:02Z) - Privacy-preserving machine learning for healthcare: open challenges and
future perspectives [72.43506759789861]
We conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare.
We primarily focus on privacy-preserving training and inference-as-a-service.
The aim of this review is to guide the development of private and efficient ML models in healthcare.
arXiv Detail & Related papers (2023-03-27T19:20:51Z)
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.