HEALTHCARE AI

Artificial Intelligence (AI) is increasingly being explored in healthcare to support access to information, patient engagement, administrativeefficiency, and selected clinical workflows. This paper proposes a Healthcare AI framework that organizes healthcare-related requests andprovides structured, understandable information through a digital interface. The framework combines a user interface, request classification,retrieval from approved healthcare information sources, privacy and safety checks, and a response-generation layer. It can support generalhealth education, service navigation, and appointment-related guidance where authorized services are available. The system is designed tocommunicate uncertainty, protect sensitive information, and direct users to qualified professionals when a request is outside its scope. It is notintended to diagnose conditions, prescribe treatment, or replace clinical judgement. The proposed design is modular and can be evaluatedthrough documented software, usability, source-quality, privacy, and safety tests

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-18
DOI
https://doi.org/10.5281/zenodo.23458225
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

HEALTHCARE AI

Tanuja Sakhare
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
article

HEALTHCARE AI

Tanuja Sakhare
article en

Abstract

Artificial Intelligence (AI) is increasingly being explored in healthcare to support access to information, patient engagement, administrativeefficiency, and selected clinical workflows. This paper proposes a Healthcare AI framework that organizes healthcare-related requests andprovides structured, understandable information through a digital interface. The framework combines a user interface, request classification,retrieval from approved healthcare information sources, privacy and safety checks, and a response-generation layer. It can support generalhealth education, service navigation, and appointment-related guidance where authorized services are available. The system is designed tocommunicate uncertainty, protect sensitive information, and direct users to qualified professionals when a request is outside its scope. It is notintended to diagnose conditions, prescribe treatment, or replace clinical judgement. The proposed design is modular and can be evaluatedthrough documented software, usability, source-quality, privacy, and safety tests

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 20%
Artificial Intelligence in Healthcare and Education
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HEALTHCARE AI — Tanuja Sakhare · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS