How Will AI Change Healthcare?

How Will AI Change Healthcare

Many different businesses are being significantly impacted by artificial intelligence (AI), and healthcare is one of them too. By enhancing diagnosis and treatment and assisting patients in several ways, AI applications in healthcare have the potential to dramatically alter the lives of patients.

AI is now being used in the COVID-19 pandemic to find and delete false material about the virus from social media. AI is also assisting researchers in the creation of vaccines. The input of AI in Cancer research is also significant! Powered by AI, the Stem cell cancer treatment in Germany made breakthroughs that may change the concept of cancer treatment globally! 

AI could be used for various purposes in healthcare including Diagnosis and Treatment, Medical Imaging, Drug Discovery, Administrative Tasks, and inputs in drug design for Cancer and HIV. 

Many research labs and healthcare around the world are actively using AI or proposing to do so. However, the integration of AI in healthcare requires a huge amount of investment. Hence, not all countries have the privilege of using AI in healthcare. 

AI Is Transforming The Healthcare

This game-changing technology has the power to promote patient engagement and compliance, advance treatment alternatives, and enhance administrative as well as operational efficiency.

1. Improving Diagnostics

By evaluating symptoms, recommending individualized therapies, and assessing risk, AI technology can assist healthcare practitioners in diagnosing patients. Also, it can spot unusual outcomes.

Symptom analysis, personalized treatment recommendations, and risk assessment

Intelligent symptom checkers are already being used by many healthcare organizations and healthcare practitioners. By asking people a number of inquiries about their symptoms, this machine learning system can determine the best course of action for getting medical attention. Healthcare institutions are employing Buoy Health’s web-based, AI-powered health companion to prioritize patients who exhibit COVID-19 symptoms. Based on the most recent recommendations from the Centers for Disease Control and Prevention, it provides individualized advice and information (CDC).

2. Detecting Disease

For clinicians, imaging technologies can speed up the diagnosis procedure. Enlitic, a business established in San Francisco, creates deep-learning medical tools to enhance radiological diagnostics through the study of medical data. Clinicians are now better able to characterize and comprehend the aggressiveness of tumors thanks to these technologies. In some circumstances, these techniques can substitute “virtual biopsies” for tissue samples, assisting doctors in determining the phenotypic and genetic characteristics of malignancies.

It has also been demonstrated that these imaging techniques get more precise judgments than doctors. According to a 2017 JAMA research, 7 out of 32 deep learning algorithms could identify lymph node metastases in breast cancer patients more precisely than a group of 11 pathologists. Mobile phones and other portable gadgets could develop into effective diagnostic instruments that help the fields of dermatology as well as ophthalmology. In order to distinguish between malignant and benign skin lesions, medical AI for dermatology depends on image analysis and classification.

3. Advance Treatment

Medical AI is becoming a useful tool for patient care. People who have lost their capacity to talk and move might benefit from brain-computer interfaces. Moreover, this technology may enhance the quality of life for ALS, stroke, and spinal cord injury patients.

Machine learning algorithms have the potential to enhance the use of immunotherapy, to which 20% of patients now react. The development of new technologies may open up new possibilities for tailoring medicines to each patient’s particular genetic profile. Using machine learning and AI capabilities, businesses like BioXcel Therapeutics are attempting to discover novel treatments.

Also, by examining historical, present, and future patient data, clinical decision support systems (CDSSs) can enable healthcare professionals in making better medical decisions. To assist a healthcare professional in making a more knowledgeable and evidence-based clinical choice, IBM provides clinical support technologies. Lastly, AI has the ability to accelerate medication development by cutting down on the time and expense associated with discovery. AI technologies assist in data-driven decision-making, assisting researchers in determining which chemicals merit further investigation.

4. Boosting Patient Engagement 

Patients and professionals may monitor health and booking health check-ups with the use of wearables and tailored medical devices like activity trackers and smartwatches. By gathering and examining data on people, they can also advance the study of community health issues.

Also, these tools might be helpful in encouraging patients to follow medical advice. Patient adherence to therapy regimens may influence the result. The treatment plan may not succeed if individuals are not obedient and do not alter their behavior or take prescription medications as instructed. AI’s capacity to customize therapy may encourage patients to participate more actively in their treatment. AI technologies can be used to deliver patients notifications or information that is meant to elicit a response.

5. Supporting Operational Workflow

By automating some of the operations, AI can enhance the administrative as well as operational workflow in the healthcare system. Electronic health record note-taking and review consume between 34% and 55% of a physician’s time, calling it one of the main reasons for lost productivity. Natural language processing-based clinical documentation systems can assist decrease the time physicians spend on documentation for clinicians, giving them more time to concentrate on providing high-quality treatment.

Companies that provide health insurance can gain from AI technology. As insurers flag 80% of healthcare claims as erroneous or fraudulent, the present claim evaluation process takes a long time. Instead of taking days or months to find problems, insurers may use natural language processing techniques.

Three Phases Of Scaling AI In Healthcare

Our knowledge of AI and its maximum potential in healthcare, particularly in terms of how it will affect personalization, is still in its infancy. Considering the funnel of ideas and currently implemented solutions, there may be three stages to expanding AI in healthcare.

First: To optimize healthcare operations and boost adoption, solutions are likely to target the low-hanging fruit of repetitive, regular, and primarily administrative procedures including medical documentation that take up a lot of physicians’ and nurses’ time. 

Second: As patients assume an increasing amount of responsibility for their care, we anticipate seeing more AI solutions that facilitate the transition from hospital-based towards home-based care, including remote monitoring, AI-powered alarm systems, or virtual assistants. Also, during this phase, NLP solutions may be used more widely in healthcare facilities and at home, and AI may be applied to more disciplines, such as cancer, cardiology, or neurology, where improvements are already being achieved.

Third: With an increasing focus on enhanced as well as scaled clinical decision-support (CDS) equipment in a sector that has learned from prior endeavors to incorporate such tools into medical care and has modified its mindset, culture, and skills, we would anticipate seeing more Ai applications in clinical practice based on data from clinical trials.

Wrapping Up

The best part is that most major healthcare institutions are starting to employ AI in some capacity, if not all. Yet the process of discovering how to use artificial intelligence to improve healthcare is still in its early phases. We can only hope for the best as the companies like OpenAI are availing AI for public use. We could hope, other tech giants took a step ahead to share their AI technology with the health industry and researchers if not with the public. 

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