Introduction
Artificial intelligence (AI) is a recent scientific field in which computer algorithms carry out tasks without human guidance. Artificial intelligence (AI) focuses on how computers imitate human thoughts. AI includes machine learning (ML), deep learning (DL), and artificial neural networks (ANN). The AI combined with high-performance computing allows us to predict disease risk based on individual patient data. Furthermore, AI platforms also translate this data into clinical knowledge. Predicting disease risk with accuracy has shown promising results on these platforms. AI can also help deepen the knowledge of the disease’s origin and course.
What Are the Areas of Artificial Intelligence Used in Disease Management?
Modern medicine faces the challenge of obtaining, interpreting, and applying knowledge to solve complex clinical problems. The development of AI can help the clinician in disease diagnosis, treatment planning, and prognosis (prediction of a disease outcome).
Machine Learning: Machine learning (ML) is the AI domain that uses existing data for disease prediction. In ML, the computer system learns from data and experience. In medicine, ML is used to build automated decision systems for disease. Most ML approaches fall into two types:
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Supervised Learning: Supervised methods use labeled data and perform disease classification. Examples are lung nodule detection from a chest X-ray and stroke classification in a patient.
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Unsupervised Learning: It does not mandate labeled data. It can identify hidden disease patterns and is used in data exploration and hypothesis formulation. For example, researchers used unsupervised learning for a clinical trial using novel medications in heart failure patients.
Neural Networks: It is a technology available since the 1960s. It has been well-established in healthcare research for several decades and used for determining whether a patient will acquire a particular disease. It is technically compared to processing brain impulses through neurons.
Deep Learning: Deep learning (DL) is a method for designing simple concepts built on each other to make numerous processing layers. Furthermore, DL analyzes massive data. It replaces the classical manual method of disease classification with an automated plan. It also allows the computer to decide the essential features of a disease.
What Is the Impact of Artificial Intelligence in Disease Management?
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Detection of the disease and its prevention requires continuous data checking and reviewing. A quick action based on correct data can impact people worldwide. The use of AI in disease management has improved the collection and processing of valuable data. At higher levels, it has also augmented surgical robot programming.
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AI describes the power of a computer to analyze how a human learns image and pattern recognition in a given situation. AI in health care has changed the collection, analysis, and development of information for patient care.
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AI is increasingly used in medicine to know about dangerous disorders and diseases. Since AI can interact with image data, it is increasingly being used in disease diagnosis and prediction.
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AI can enhance medical diagnosis in a short time. For example, it can detect advanced malignancy (cancer) in medical images and records. It allows pathologists an early diagnosis of the disease. As a result, the oncologist can promptly treat it instead of sending tissue samples to a lab for long-term investigation.
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Learning algorithms and data derived from medical records are the two most vital tools to implement AI methods in the healthcare system to improve disease diagnosis and classification, decision-making, and optimal treatment choices and help people live safely and prolongedly. Also, AI-based algorithms are effective in identifying undiagnosed, unencoded, and rare diseases.
How Does Artificial Intelligence Influence Disease Management?
AI models for disease prediction and treatment provide an opportunity for early diagnosis of patients. The application of ML and DL techniques to diagnose various diseases is increasing significantly.
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Heart Diseases: Due to the existence of computed tomography (CT), electrocardiography (ECG), and echocardiography, DL can be used accurately in the analysis and review of cardiovascular data. Coronary artery disease (CAD) is a prevalent cardiovascular disease that causes disability and has a high death rate. Early diagnosis through AI is highly effective and has a profound impact on its treatment.
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Brain Diseases: AI can also provide accurate prediction and detection of brain disorders. ML and DL algorithms can diagnose neurodegenerative diseases and brain cancer. One must note that these disorders are very difficult to detect in the early stages. AI can process and analyze brain signals to discover insights that are not obvious to the human eye.
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Breast Cancer: AI’s advanced algorithms assist in breast cancer prediction and detection in the early stages. Breast cancer is a deadly disease affecting females that causes millions of deaths annually. However, AI facilitates early diagnosis, which plays a crucial role in treating and controlling the disease. Various datasets are used by researchers to investigate ML methods in breast cancer diagnosis.
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Genetic Disorders: Neural networks predict and categorize genetic disorders from genetic data. Scientists face challenges regarding biomarkers for complex genetic conditions owing to diverse genotypes (genetic makeup of a person). ML and DL methods can enhance the prediction of genetic conditions.
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Dermatology: AI techniques also have broad dermatology applications. ML and DL can be guided based on skin disease sample data to diagnose, predict, and classify the characteristics. However, the field of dermatology is still accepting these advanced techniques. Detection in the initial stages is vital for effective skin cancer treatment. Also, young specialists cannot always detect skin cancer due to inexperience. So, developing automated systems can help them diagnose skin diseases to save lives and reduce financial costs. Researchers use ensemble learning (a subtype of ML) and DL to analyze skin cancer.
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Respiratory Infections: AI algorithms can assist healthcare providers in detecting and analyzing lung diseases. Studies show a DL-based model to analyze the respiratory data for chronic obstructive pulmonary disease (COPD, an obstructive lung disease) detection with 93 percent accuracy. Also, another framework model based on neural networks was proposed to diagnose Coronavirus disease (COVID-19) using X-ray images with an accuracy of about 96 percent.
Conclusion
To conclude, artificial intelligence provides a decision-support system to transform healthcare. Given the collaboration of artificial intelligence with imaging, most radiology and pathology images will be scanned by machines in the future. Furthermore, artificial intelligence algorithms will not replace human doctors in disease management but will potentiate their efforts in patient care. Therefore, AI has a positive impact on the healthcare sector.
