Artificial Intelligence and Machine Learning in Diagnostics and Treatment Planning DOI Open Access

Ankur Tak

Journal of Artificial Intelligence & Cloud Computing, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 6

Published: March 31, 2023

This paper explores how machine learning (ML) and artificial intelligence (AI) are transforming treatment planning diagnosis in the healthcare industry. These technologies, which make use of sophisticated algorithms computer models, have shown great promise for improving precision, effectiveness, customized nature medical therapies. When using AI ML diagnostics, large datasets from patient records to images must be analyzed. technologies facilitate prevention by enabling rapid exact illness identification through deep pattern recognition algorithms. Predictive modeling also makes it possible anticipate a disease will progress, preemptive plans possible. play major role optimizing therapeutic techniques during planning. aid development best based on distinct responses, genetic characteristics, other pertinent aspects evaluating data specific each patient. promotes more patient-focused paradigm minimizing side effects increasing efficacy. The study looks at difficulties moral issues surrounding application medicine. Notwithstanding encouraging results, is crucial underline necessity strong validation, openness, responsible technology deployment order guarantee these technologies' trustworthy contexts. In summary, combination has enormous potential transform diagnosis, presenting hitherto unheard-of chances precision medicine better outcomes. As develop further, way they fit into clinical workflows might completely change delivered usher new era tailored, data-driven treatments.

Language: Английский

Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment DOI Creative Commons
Mohammad Saleem,

Abigail E. Watson,

Aisha Anwaar

et al.

Biomolecules, Journal Year: 2025, Volume and Issue: 15(4), P. 589 - 589

Published: April 16, 2025

Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This study reviews the potential of artificial intelligence (AI) to optimize ICI therapy in by integrating various diagnostic tools. Through comprehensive literature review, we analyzed studies on AI applications immunotherapy, focusing predictive modeling, biomarker identification, and treatment response prediction. Key findings highlight efficacy improving outcomes. Machine learning models successfully identified prognostic cytokine signatures linked nivolumab clearance. The combination with RNAseq analysis had for development personalized ICIs. A machine learning-based approach was able assess risk-benefit ratio prediction immune-related adverse events (irAEs) using electronic health record (EHR) data. Deep algorithms demonstrated high accuracy tumor microenvironment analysis, including region identification lymphocyte detection. AI-assisted quantification tumor-infiltrating lymphocytes (TILs) proved prognostically valuable primary anti-PD-1 metastatic cases. Integrating multiple modalities, such as CT imaging laboratory data, modestly enhanced performance 1-year survival advanced cancers treated immunotherapy. These underscore AI-driven approaches refine prediction, stratification While promising, clinical validation implementation challenges remain.

Language: Английский

Citations

0
Advancements and Challenges in Personalized Therapy for BRAF-Mutant Melanoma: A Comprehensive Review DOI Open Access

Abdulaziz Shebrain,

Omer A. Idris,

Ali Jawad

et al.

Journal of Clinical Medicine, Journal Year: 2024, Volume and Issue: 13(18), P. 5409 - 5409

Published: Sept. 12, 2024

Over the past several decades, advancements in treatment of

Language: Английский

Citations

1
Investigating Skin Cancer Diagnosis Using a Webcam-based Microcontroller System DOI Creative Commons
Raymond Kim

Deleted Journal, Journal Year: 2024, Volume and Issue: 19(1), P. 37 - 47

Published: April 3, 2024

Skin cancer can spread fast to nearby tissue and other parts of the human body if it's not diagnosed early. Most are curable only skin is found treated in early stages. Therefore, essential seek a casual way diagnosis. This paper assesses prototype system for detection using an Arduino with ArduCam Mega 5MP, benchmarked against smartphone Bandpass filters capture images at red (650 nm), green (532 blue (450 nm) wavelengths, measuring reflectance values. The approach aims quantitatively determine melanin, oxyhemoglobin, deoxyhemoglobin levels, aiding various lesions' Evaluation involves comparing pixel values taken by smartphones 3D mesh grid. Applying modified Lambert-Beer law moles, pimples, scars, scabs, traces predicts relative levels components. shows 87% match standard, demonstrating high reliability. Further study might be needed clarify confirmation clinical cases.

Language: Английский

Citations

0
Artificial Intelligence and Machine Learning in Diagnostics and Treatment Planning DOI Open Access

Ankur Tak

Journal of Artificial Intelligence & Cloud Computing, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 6

Published: March 31, 2023

This paper explores how machine learning (ML) and artificial intelligence (AI) are transforming treatment planning diagnosis in the healthcare industry. These technologies, which make use of sophisticated algorithms computer models, have shown great promise for improving precision, effectiveness, customized nature medical therapies. When using AI ML diagnostics, large datasets from patient records to images must be analyzed. technologies facilitate prevention by enabling rapid exact illness identification through deep pattern recognition algorithms. Predictive modeling also makes it possible anticipate a disease will progress, preemptive plans possible. play major role optimizing therapeutic techniques during planning. aid development best based on distinct responses, genetic characteristics, other pertinent aspects evaluating data specific each patient. promotes more patient-focused paradigm minimizing side effects increasing efficacy. The study looks at difficulties moral issues surrounding application medicine. Notwithstanding encouraging results, is crucial underline necessity strong validation, openness, responsible technology deployment order guarantee these technologies' trustworthy contexts. In summary, combination has enormous potential transform diagnosis, presenting hitherto unheard-of chances precision medicine better outcomes. As develop further, way they fit into clinical workflows might completely change delivered usher new era tailored, data-driven treatments.

Language: Английский

Citations

0