Artificial intelligence is on the verge of revolutionizing medical technology. The potential for AI to deliver more accurate, efficient, and effective therapies at precisely the right time in a patient’s care is essentially endless in nearly every aspect of treatment, from chronic diseases and cancer to radiography and risk assessment. Artificial intelligence is set to be the engine pushing innovations throughout the continuum of care as the amount of available data continues to expand at an alarming rate.
The advantages of AI over traditional analytics and clinical decision-making methodologies are numerous. Learning algorithms may become more precise and accurate as they interact with training data, allowing patients to get new insights into diagnosis, care processes, treatment variations, and results.
Let’s check out four major impacts of AI in modern-day healthcare system
Predictive analytics for early intervention
With heart disease, stroke, and cancer remaining the leading causes of mortality, it’s critical to make use of all available tools to aid in early detection, diagnosis, and treatment. Artificial intelligence allows for early detection of any potential danger indications in a patient’s behavior. Patients at high risk of stroke, for example, were identified using AI algorithms based on their reported symptoms and genetic traits; this level was movement-based, with every abnormal physical motion in the patient being recorded and triggering an alert.
This trigger warning enabled practitioners to expeditiously refer patients for an MRI/CT scan for disease assessment. The research found that the early detection alert had an accuracy of more than 85 percent in evaluating diagnosis and prognosis. Consequently, practitioners were able to initiate therapy more quickly and identify whether a patient faced a greater risk of future stroke. Similarly, machine learning was used to predict if a patient will have another stroke 48 hours later with a perdition accuracy of 70%.
Cancer is a complex and multidimensional illness with hundreds of genetic and epigenetic variations. Artificial intelligence-based algorithms have the potential to aid in the early detection of genetic changes and aberrant protein interactions. In the present period, biomedical research is also focused on bringing AI technology to clinics in a safe and ethical way. Artificial intelligence-assisted pathologists and clinicians could be a game-changer in terms of disease risk, diagnosis, prognosis, and therapy prediction. Artificial intelligence and machine learning clinical applications in cancer diagnosis and therapy are the future of medical advice, paving the way for more rapid mapping of a new treatment for each individual.
Using an AI-based system method, researchers can engage in real time and exchange information digitally, potentially treating millions of patients. By bridging the gap between biology and artificial intelligence, science is focusing on showing game-changing technologies of the future in clinics, and illustrating how AI-based support may aid oncologists in giving accurate therapy.
AI to provide more targeted diagnostics
With so much healthcare data out in the wild, AI needs to be able to successfully sift through it in order to “learn” and build a network. In the arena of healthcare data, there are two types of data that can be sorted: unstructured and structured. Machine Learning Strategies (ML), a Neural Network System, and Modern Deep Learning are three techniques used in structured learning. In any unstructured data, Natural Language Processing is applied (NLP).
Machine learning approaches are based on the use of analytical algorithms to extract specific patient characteristics, which includes all of the information obtained during a patient visit with a practitioner. The organised data collected includes symptoms, physical exam findings, drugs, basic metrics, disease-specific data, diagnostic imaging, gene expressions, and a variety of laboratory tests. Machine learning can then be used to predict patient outcomes. In one study, Neural Networking was utilised to correlate texture information from patients’ mammograms with 6,567 genes in a breast cancer diagnosis technique. This combination of genetic and morphological characteristics resulted in a more specific tumour diagnosis.
Artificial Intelligence has the Potential To Provide the Next Generation of Radiological Tools
Up to 35% of lung nodules go missed at the first inspection due to the limitations of human vision. In this case, artificial intelligence can assist by relieving physicians of some obligations and spotting lung spots that aren’t evident to the naked eye. According to a recent study published in JAMA Network Open, an artificial intelligence system trained to detect pulmonary nodules on chest radiographs may improve lung cancer diagnosis.
Radiology trainees and board-certified radiologists may improve their performance in suggesting chest CT scans for patients suspected of having lung cancer by employing artificial intelligence as a second reader in conjunction with chest X-rays. AI algorithm aids less-experienced readers in terms of sensitivity while aiding more-experienced readers in terms of specificity.
Current medical imaging technologies such as magnetic resonance imaging (MRI), computed tomography (CT), and X-rays provide non-invasive views into the workings of the human body. Many diagnostic methods, however, still rely on actual tissue samples obtained via biopsies, which come with risks such as the risk of infection in the patient. Artificial intelligence is expected to enable the creation of next-generation radiological technologies that are accurate and comprehensive enough to eliminate the need for tissue samples in some circumstances, according to experts.
AI and Telehealth
COVID-19 generated an urgent need for telemedicine to care for patients outside of the clinic or office environment, as well as to mitigate financial losses due to decreased ambulatory visits.
Despite the fact that artificial intelligence is being used on a larger scale for high-risk illnesses, telehealth solutions are being deployed in patients’ homes to help manage and prevent high-risk scenarios while also reducing hospital readmissions. Telehealth technologies allow for the collection, recording, and analysis of numerous parameters in the same way that a bigger AI system would. If a patient reports a high-risk trait, this technology may alert practitioners right away. Fast diagnosis and updated treatment plans save both the patient and the hospital time and money while allowing for more fast care. Artificial intelligence enables healthcare professionals to make more efficient and reasonable judgments, resulting in better patient care.