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Artificial Intelligence in Health AI in medical diagnostics: A multi-disease approach

models to become more user-friendly and widely adopted in various aspects of medicine. AI’s ability to analyze
across hospitals and healthcare systems. Studies have vast amounts of medical data is improving diagnosis
shown that as AI becomes easier to implement and use, and treatment processes, offering faster, more precise
its adoption rates in healthcare will increase, leading to diagnoses, earlier disease detection, and more personalized
improved outcomes for patients. For instance, the use of treatment options. AI leverages DL, computer vision, and
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XAI to assist in real-time clinical decision support during sophisticated algorithms to interpret medical data, serving
surgeries or other medical interventions has already as an expert assistant to healthcare professionals.
shown promise in improving patient outcomes and AI is revolutionizing healthcare through its
preventing intraoperative complications. Furthermore, applications in medical imaging, surgery, drug discovery,
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as the research and implementation of AIM technologies and virtual health assistants. By detecting anomalies in
continue to evolve, it is essential to address the limitations scans, extracting insights from clinical notes, and offering
that currently hinder their full potential. Data siloing across diagnostic suggestions, AI enhances the accuracy and
hospitals and medical institutions, the lack of standardized speed of diagnosis. In fields such as radiology, pathology,
protocols for data sharing, and the need for greater cardiology, and dermatology, AI tools are aiding in the
collaboration between healthcare organizations are some detection of fractures, cancer cells, heart disease, and skin
of the primary challenges that must be addressed. Efforts conditions. This technology allows healthcare professionals
to encourage data exchange and collaboration among to detect subtle patterns that may go unnoticed by humans,
healthcare providers will facilitate the widespread use of AI reducing the likelihood of diagnostic errors and providing
tools, ensuring that AIM solutions reach their full potential a layer of impartiality and precision. AI’s strength lies in
in improving patient care. 76-78 However, in recent years, its ability to mimic human cognition, but with enhanced
many computer-aided diagnoses (CADs) have been used computational speed and learning capacity. By processing
to diagnose and classify breast cancer using traditional red extensive datasets, AI can identify trends and symptoms
green blue images that analyze the images only in three-color that are associated with various medical conditions,
channels. In CAD, a radiologist interprets mammograms improving its diagnostic accuracy over time. Its integration
that are also analyzed by a computer that detects potential into diverse medical fields has proven successful, especially
breast lesions or differentiates breast lesions as malignant in radiology, where it detects tumors and fractures with
or benign. Mammograms are commonly used to screen for high precision, and in cardiology, where it helps predict
breast cancer. If a screening mammogram finds something heart disease risk. Moreover, AI’s lack of fatigue and biases
concerning, another mammogram might be performed means it can work tirelessly, reducing the potential for
to look at the area more closely. This more-detailed errors.
mammogram is called a diagnostic mammogram and is
often used to closely examine both breasts. 79,80 AI also plays a critical role in personalized medicine.
Its integration with EHRs allows AI systems to analyze
The integration of AI into mHealth has shown immense a patient’s medical history, identifying risk factors and
promise in transforming the healthcare landscape, providing real-time insights to clinicians. This capability
particularly in the areas of remote patient monitoring, enhances diagnosis and treatment, offering tailored
chronic disease management, and preventative medicine. healthcare solutions. Furthermore, AI-driven drug
By leveraging AI techniques such as DL, FL, and XAI, discovery platforms accelerate the identification of
mHealth technologies can provide accurate, secure, and potential drug candidates, revolutionizing pharmaceutical
interpretable insights that improve clinical decision- research and making it more efficient.
making and patient outcomes. As the healthcare industry This is especially relevant in the development of
continues to evolve, further research and investment in personalized cancer treatments, where AI’s ability
AIM solutions will be crucial in ensuring their effective to analyze genetic markers leads to better treatment
deployment to enhance the quality of care and address options. 13-33 The benefits of AI in healthcare extend
critical healthcare challenges. beyond accurate diagnosis and personalized medicine.
7. AI in the realm of diagnosing medical AI streamlines diagnostic procedures, reducing the time
conditions and its impact on healthcare and effort required for analysis and interpretation. This
efficiency results in cost savings for healthcare systems
AI is transforming healthcare by enhancing medical by enabling early detection and intervention, which can
diagnosis through the use of ML, NLP, and other reduce hospitalizations and shorten treatment durations.
subdomains. With an expected annual growth rate of Real-world applications such as Google’s DeepMind
37.3% from 2023 to 2030, AI is becoming a key player algorithms, which predict acute kidney injury up to 48 h

Volume 2 Issue 3 (2025) 54 doi: 10.36922/aih.5173