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The top 10 AI in Healthcare Use Cases that we all should learn something as great contributors

AI is reshaping healthcare, and its application is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP), and deep learning (DL) enable healthcare stakeholders and professionals to identify healthcare needs and solutions more quickly and with greater accuracy, utilizing data patterns to make informed medical or business decisions by using AI Uses Cases.

1. AI Supports Medical Imaging Analysis

AI is used as a case triage tool. It aids a clinician in the review of images and scans. This allows radiologists and cardiologists to identify critical insights for prioritizing critical cases, avoiding potential errors in reading electronic health records (EHRs), and making more precise diagnoses. A clinical study can generate massive amounts of data and images that must be verified.

2. AI can Decrease the Cost of Develop Medicines

Supercomputers have been used to predict which potential medicines would and would not be effective for various diseases based on databases of molecular structures. AtomNet was able to predict the binding of small molecules to proteins by analyzing hints from millions of experimental measurements and thousands of protein structures using convolutional neural networks, a technology similar to that used to make self-driving cars.

3. AI Analyzes Unstructured Data

Due to massive amounts of health data and medical records, clinicians frequently struggle to stay up to date on the latest medical advances while providing quality patient-centered care. EHRs and biomedical data curated by medical units and professionals can be quickly scanned by ML technologies to provide clinicians with prompt, reliable answers.

4. AI Builds Complex and Consolidated Platforms for Drug Discovery

AI algorithms can identify new drug applications while also tracing their toxic potential and mechanisms of action. This technology formed the basis of a drug discovery platform, allowing the company to repurpose existing drugs and bioactive compounds.

5. AI can Forecast Kidney Disease

Acute kidney injury (AKI) can be difficult for clinicians to detect, but it can cause patients to deteriorate rapidly and become life-threatening. With an estimated 11% of hospital deaths caused by a failure to identify and treat patients, early detection and treatment of these cases can have a significant impact on reducing life-long treatment and the cost of kidney dialysis.

6. AI Provides Valuable Assistance to Emergency Medical Staff

The time between a 911 call and the arrival of an ambulance during a sudden heart attack is critical for recovery. Emergency dispatchers must be able to recognize the symptoms of a cardiac arrest to take appropriate action, giving patients a better chance of survival. AI can analyze both verbal and nonverbal cues to make a diagnosis from a distance.

7. AI Contributes to Cancer Research and Treatment, Especially in Radiation Therapy

Radiation therapy may lack a digital database to collect and organize EHRs in some cases, making cancer research and treatment difficult. Oncora Medical developed a platform to help clinicians make informed decisions about radiation therapy for cancer patients. The platform collects relevant medical data from patients, evaluates the quality of care provided, optimizes treatments, and provides comprehensive oncology outcomes, data, and imaging.

8. AI Uses Data Collected for Predictive Analytics

By transforming EHRs into an AI-powered predictive tool, clinicians can be more efficient in their workflows, medical decisions, and treatment plans. NLP and ML can read a patient's entire medical history in real-time and associate it with symptoms, chronic affection, or an illness that affects other family members. They can use the data to create a predictive analytics tool that can detect and treat diseases before they become life-threatening.

9. AI Accelerates the Discovery and Development of Genetic Medicine

AI is also used to assist in the rapid discovery and development of medicine, with a high rate of success. Changes in molecular phenotypes, such as protein binding, favor genetic diseases. Predicting these changes entails predicting the emergence of genetic diseases. This is possible by gathering information on all identified compounds as well as biomarkers relevant to specific clinical trials. This data is processed, for example, by Deep Genomics' AI system.

10. AI Supports Health Equity

To deliver the best possible health outcomes, the AI and ML industry must design healthcare systems and tools that ensure fairness and equality are met, both in data science and clinical studies. The increased use of ML algorithms in various areas of medicine raises the possibility of health inequities.