Page 137 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
have discussed the impact of AI-based triage systems on
emergency response time and accessibility to healthcare.
Liang Hu et al. (2021) highlighted how real-time symptom
analysis is used for prioritizing patients through machine
learning models to enhance emergency care. Ramesh Kumar
and Deepika Sharma, in their paper published in 2022,
demonstrated how AI-based predictive analytics might
detect the earliest signs of potentially fatal conditions such as
strokes and heart attacks, enabling interventions in time.
Medical imaging is another area that AI has captured much
attention. CNNs have been used extensively for anomaly
detection in X-rays, MRIs, and CT scans better than
traditional diagnostic methods. AI-powered chatbots and
virtual assistants have also been developed to provide
immediate medical advice and support, thereby reducing the
burden on healthcare professionals.
Despite these advances, the areas of ethical consideration,
algorithm bias, and insecurity of data become significant
challenges towards the adoption of AI in healthcare settings.
Future work should therefore pay attention to ensuring
accuracy in the use of AI, meeting clinical standards, and
implementation in current medical infrastructures.

More recently, studies discuss the role of AI in healthcare,
emergency response, the prediction of disease, and offering III. Proposed Work:
The proposed AI-integrated healthcare platform aims to
recommendations for more personalized treatment
approaches. Some articles discuss the improvement of improve emergency response and everyday health
emergency response time and availability of healthcare from management through real-time monitoring predictive
AI-based triage systems. analytics, automated triage systems
Data Collection:
For example, Liang Hu et al. (2021) have highlighted that
The system collects data from:
machine learning models are used to classify patients based
User inputs: Patient symptoms past medical history and
on real-time symptom analysis, thereby improving
lifestyle information. Electronic Health R
emergency care. Ramesh Kumar and Deepika Sharma (2022)
also showed how predictive analytics based on AI can Edge Detection: Identify the important area in medical scans
identify early precursors of potentially life-threatening
Histogram Equalization: Contrast enhancement in medical
conditions such as strokes and heart attacks, which can be
images
addressed in time.
CNNs have been used extensively for anomaly detection in X- Filtering Techniques: Removing noise from images to get
precise AI anal
rays, MRIs and CT scans better than traditional diagnostic
methods.AI-powered chatbots and virtual assistants have
also been developed to provide immediate medical advice
and support, thereby reducing the burden on healthcare
professionals.
Despite these advances, ethical issues, algorithmic biases,
and data security are some of the major challenges that will
hinder the adoption of AI in healthcare. Therefore, future
research should aim at enhancing AI accuracy, medical
regulation compliance, and the integration of AI into existing
infrastructures in healthcare.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 127