Four major impacts of artificial intelligence on healthcare

Four major impacts of artificial intelligence on healthcare
Jonathan Sharp is the CFO of Environmental Litigation Group, P.C., a legal firm devoted to helping individuals who have experienced serious injury resulting from occupational exposure to hazardous substances.

Medical technology is on the brink of being revolutionized by artificial intelligence (AI). In nearly every area of patient care, from chronic diseases and cancer to radiography and risk assessment, the potential of AI to deliver more accurate, efficient, and effective therapies at precisely the appropriate time in a patient’s care is almost limitless. As payment systems change, patients expect more from their providers, and the amount of accessible data continues to grow at an alarming pace, artificial intelligence is set to be the engine driving advances throughout the continuum of care.

AI has many benefits over conventional analytics and clinical decision-making methods. As they interact with training data, learning algorithms may become more exact and accurate, enabling patients to acquire new insights into diagnoses, care procedures, treatment variability, and outcomes.

1. Artificial Intelligence Integrated Into Major Disease Areas Enables Predictive Analytics for Early Intervention 

With cardiovascular, neurological, and cancer illnesses continuing to be the major causes of death, it is essential to use all available resources to aid in early detection, diagnosis, and treatment. Thanks to artificial intelligence, it’s possible to identify any potential danger signs in a patient’s behavior early on. For example, patients with a high risk of stroke were identified using AI algorithms based on their reported symptoms and genetic characteristics; this level was movement-based, with every atypical physical movement in the patient being recorded and generating 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 multifaceted and complicated disease characterized by hundreds of genetic and epigenetic variants. AI-based algorithms have the potential to pave the way for the early detection of these genetic alterations and abnormal protein interactions. Biomedical research in the modern era is also focused on bringing AI technology to clinics in a safe and ethical manner. AI-assisted pathologists and doctors may represent a quantum leap ahead in disease risk, diagnosis, prognosis, and therapy prediction. Clinical applications of artificial intelligence and machine learning in cancer diagnosis and therapy are the future of medical guidance, paving the way for more rapid mapping of a new treatment for each person. Researchers may interact in real-time and exchange information digitally by utilizing an AI-based system method, which has the potential to treat millions of patients. Science is concentrating on demonstrating game-changing technologies of the future in clinics by bridging the gap between biology and artificial intelligence, and explaining how AI-based support may aid oncologists in providing accurate therapy.

2. Artificial Intelligence and Machine Learning Can Provide More Targeted Diagnostics

With a vast volume of healthcare data out in the field, AI must effectively sift through it in order to “learn” and create a network. There are two kinds of data that can be sorted in the domain of healthcare data: unstructured and structured. Structured learning employs three techniques: Machine Learning Techniques (ML), a Neural Network system, and Modern Deep Learning. Natural Language Processing is used in any unstructured data (NLP).

The use of analytical algorithms in order to extract particular patient characteristics, which include all of the information that would be gathered during a patient visit with a practitioner, is the basis of machine learning methods. Symptoms, results of physical exams, medications, basic metrics, disease-specific data, diagnostic imaging, gene expressions, and a variety of laboratory tests are all included in the structured data that is collected. Patient outcomes may then be predicted using machine learning. In one research, Neural Networking was used to select 6,567 genes and match them with texture information from the patients’ mammograms in a breast cancer diagnosis procedure. This combination of both genetic and morphological features resulted in a tumor indication that was more specific.

Supervised learning is the most frequent form of Machine Learning in a healthcare context. Supervised learning makes use of the patient’s physical characteristics in conjunction with a database of information to give a more focused result. Modern Deep Learning is another kind of learning that is utilized and is thought to go beyond the surface of Machine Learning. When compared to Machine Learning, Deep Learning utilizes the same inputs, but it feeds them into a computerized neural network, which is a hidden layer that further processes the information in order to give a more simple output. This assists practitioner in narrowing down many potential diagnoses to one or two outcomes, enabling the practitioner to reach a more definite and concrete decision.

3. Artificial Intelligence Has the Potential To Provide the Next Generation of Radiological Tools

Based on the incidence, severity, and preventability of the illnesses, occupational lung diseases are the most common cause of occupation-associated disease in the United States. Exposure to organic and inorganic compounds as well as carcinogens in the workplace, over a lengthy period of time, may result in a variety of lung illnesses that can have long-term consequences even after the exposure ends.

Each year, new causes of respiratory damage emerge, leading to an increase in the number of workers who develop lung illness as a result of their jobs. During the early years of work, the majority of individuals who are frequently exposed to toxins on the job show relatively minor signs of lung problems. Lung cancer and other respiratory diseases have a long incubation period, which makes it difficult to establish a connection between occupational exposure and these illnesses for many years.

Due to the limits of human vision, up to 35% of lung nodules go unnoticed during the first checkup. Artificial intelligence can help in both cases by relieving physicians of some of their responsibilities and by detecting lung spots that aren’t visible to the naked eye. According to a recent study published in the JAMA Network Open, an artificial intelligence system taught to identify pulmonary nodules may enhance lung cancer diagnosis on chest radiographs.

By using artificial intelligence as a second reader in conjunction with chest X-rays, radiology trainees and board-certified radiologists may enhance their performance in suggesting chest CT scans for patients suspected of having lung cancer. According to the researchers’ findings, the AI algorithm helps less-experienced readers in terms of sensitivity while benefiting more-experienced readers in terms of specificity.

Magnetic resonance imaging (MRI), computed tomography (CT), and X-rays are examples of current medical imaging technologies that offer non-invasive views into the workings of the human body. However, many diagnostic procedures still depend on actual tissue samples acquired via biopsies, which are associated with hazards such as the likelihood of infection in the patient. Experts anticipate that artificial intelligence will allow the development of next-generation radiological tools that are accurate and comprehensive enough to obviate the need for tissue samples in some cases.

If scientists want the imaging to provide the same information as tissue samples, they’ll need to be able to obtain extremely tight registration so that the high accuracy for each pixel can be ascertained. Successful completion of this quest may enable physicians to get a more accurate knowledge of how tumors behave as a whole, rather than relying on the characteristics of a particular section of the malignancy to make treatment choices. Additionally, providers may be able to more accurately identify the aggressiveness of tumors and focus therapies accordingly. Artificial intelligence is assisting in the advancement of “virtual biopsies” and the cutting-edge science of radionics, which focuses on using image-based algorithms to describe the phenotypic and genetic characteristics of tumors.

4. Telehealth, the Artificial Intelligence on a Smaller Scale, Can Lower Healthcare Costs, Drive Up Efficiency and Provide Patients Better Access to Healthcare Services

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. According to new data, the number of patients utilizing telehealth rose from 11 percent in 2010 to 46 percent in 2020, with growth expected to continue. Telehealth may account for 20%, or $250 billion, of US healthcare expenditure in the near future.

Although artificial intelligence is utilized on a wider scale for high-risk illnesses, telehealth tools are being deployed in patients’ homes to help treat and prevent high-risk scenarios while simultaneously decreasing hospital readmissions. Telehealth technologies enable various parameters to be collected, recorded, and analyzed in the same way as a larger AI system would. This tool may immediately notify practitioners if a patient reports a high-risk trait. Fast diagnoses and an updated treatment plan save time and money for both the patient and the hospital while delivering more rapid care. Artificial intelligence allows practitioners to make more efficient and rational decisions, thus enhancing patient care. 

Respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, occupational lung diseases, and pulmonary hypertension are among the most common, underdiagnosed, disabling, fatal, and expensive to treat of the many chronic diseases. Connected respiratory care and telehealth provide access to services that were previously only available at medical facilities. They allow for effective and continuous monitoring, early intervention, and multidisciplinary team care, which may be especially important for patients with advanced disease, multiple comorbidities, or frequent exacerbations – in other words, patients who require more intensive management that addresses the complexities of their disease. Connected care also promises enhanced patient access, more efficient and effective use of health resources, a better patient experience, and reduced medical expenses.

References:

https://svn.bmj.com/content/2/4/230

https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality

https://svn.bmj.com/content/svnbmj/2/4/230.full.pdf

https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2770952

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