extended-abstract

Assessing healthcare software built using IoT and LLM technologies

Author:

Gabriele De VitoAuthors Info & Claims

EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering

Pages 476 - 481

https://doi.org/10.1145/3661167.3661202

Published: 18 June 2024 Publication History

Abstract

In the fast-paced world of healthcare technology, combining IoT devices with large language models (LLMs) offers a promising path to transform Clinical Decision-Support Systems (CDSS). This Ph.D. project is designed to tap into IoT’s extensive data collection ability and LLMs’ superior natural language processing skills. It aims to improve clinical decision-making and patient care through a sophisticated DSS that utilizes both technologies’ strengths. The project delves into the software engineering challenges and methodologies required to build an effective DSS. It investigates how to smoothly evaluate and integrate IoT and LLMs into healthcare environments, tackling significant issues like data complexity, privacy concerns, and the necessity for high accuracy in medical settings. It underscores the critical role of thorough evaluation and assessment in developing healthcare technologies.

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Cited By

Mishra SSiddiqui ISabale KAlkhayyat A(2024)A Hybrid LLM based Model for Calorie Tracker and Dietary Control2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)10.1109/ICEC59683.2024.10837518(1-5)Online publication date: 23-Nov-2024
https://doi.org/10.1109/ICEC59683.2024.10837518

Index Terms

Assessing healthcare software built using IoT and LLM technologies
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering

June 2024

728 pages

ISBN:9798400717017

DOI:10.1145/3661167

Copyright © 2024 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 18 June 2024

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Extended-abstract
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EASE 2024

EASE 2024: 28th International Conference on Evaluation and Assessment in Software Engineering

June 18 - 21, 2024

Salerno, Italy

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Overall Acceptance Rate 71 of 232 submissions, 31%

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Cited By

Mishra SSiddiqui ISabale KAlkhayyat A(2024)A Hybrid LLM based Model for Calorie Tracker and Dietary Control2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)10.1109/ICEC59683.2024.10837518(1-5)Online publication date: 23-Nov-2024
https://doi.org/10.1109/ICEC59683.2024.10837518

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