What is the future of AI in healthcare? From disease prediction to personalized treatments, AI is transforming medicine & public health.
Table of contents
The Pivotal Role of AI, NLP, ML in Personalized Healthcare
1. Focus Areas of AI in Healthcare
1.1. Patient Centricity
1.1.1. Virtual Care
1.1.2. Patient Engagement and Empowerment
1.2. Care Delivery
1.2.1. Preventive Care
1.2.2. Curative Care
1.2.3. Palliative Care
1.3. Disease Surveillance
1.3.1. Active Surveillance
1.3.2. Passive Surveillance
1.4. Research and Development
1.4.1. Drug Discovery
1.4.2. Clinical Trials
2. AI’s Transformative Guise in Shaping the Future of Healthcare Policies
The Pivotal Role of AI, NLP, and ML in Personalized Healthcare
We live in an ecosystem where we desire a personalized experience, from music to web series, and the products and services we purchase are often recommended to us based on the data that is collected by these websites or applications.
We can better comprehend our requirements and desires for a higher quality of life thanks to this capacity. What is the future of AI in healthcare? Given how quickly AI-driven solutions are developing, AI may transform healthcare by improving patient care, diagnosis, and treatment.
Similar to this, the healthcare sector uses artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) models and algorithms to monitor patient health and provide individualized care. This is known as AI in healthcare by tech and healthcare visionaries.
AI in healthcare is a promising collaboration, as it challenges the traditional way patients are treated by doctors and healthcare specialists to bring a futuristic clinical and administrative solution. Using modern-age technology, doctors, researchers, and other healthcare providers improve healthcare delivery in areas like preventive care, disease diagnosis and prediction, treatment plans, as well as care delivery and administrative work.
AI in healthcare is further helping recruiting companies contribute to consumer health swiftly. How can AI technology advance medicine and public health? AI applications in healthcare are optimizing clinical workflows, personalizing treatments, and improving early disease detection. Nowadays, the increasing use of AI in consumer wearables and other medical devices is providing value in monitoring and identifying early-stage heart diseases. This AI-powered integration of sensors and devices helps healthcare service providers observe and detect life-threatening diseases at an early stage.
Nevertheless, healthcare areas are plentiful. However, this article will focus on how AI has been implemented and what the future of healthcare policies looks like for the industry.
1. Focus Areas of AI in Healthcare
The introduction of AI in healthcare implements modern healthcare systems that are equipped to cure diseases at a rapid pace with greater accuracy, improving the quality of care through technological advancements.
The integral focus areas for artificial intelligence help in making the modern healthcare process and system more patient-centric, further fostering care delivery, strengthening disease surveillance mechanisms, and enhancing the drug discovery process.
Let’s see a few focus areas of AI in healthcare:
1.1. Patient Centricity
The concept of patient-centricity focuses on AI-based prescription medicine, which offers enhanced personal treatment by empowering patients and providing visual care.Â
1.1.1. Virtual Care
The shortage of doctors and staff in crucial healthcare specialties has amplified the urgency to counter the challenge of rendering high-quality healthcare services. Through the offer of telehealth solutions enabling physicians to base decisions on facts through real-time insights into the patient’s health and offering clinical recommendations to guide them, artificial intelligence (AI) can potentially become a game-changer to solve this problem and set a new standard of care.
To track patients’ vitals in an ambulance, healthcare providers use Internet of Medical Things (IoMT) sensors to achieve precise data, which is again communicated to physicians and other medical staff who analyze and plan for the treatment of the patients accordingly.
1.1.2. Patient Engagement and Empowerment
AI has enormous potential to increase the efficiency of the healthcare industry because it can be applied to administrative, clinical, and supply chain management activities. Healthcare organizations can boost patient retention rates and enhance patient-provider connections by implementing AI.
In order to ensure efficient allocations and administration of beds, oxygen cylinders, and medications based on availability and demands, healthcare service providers can create machine learning and artificial intelligence algorithms to predict service demand during peak occasions. Healthcare providers can enhance service quality and cut down on needless patient wait times by automating these procedures and putting in place communication systems.
1.2. Care Delivery
Care delivery usually focuses on making AI-driven clinical decisions for disease management for physicians and AI-based diagnostics to assist healthcare staff in making preventive, curative, and palliative care for ailing patients.
1.2.1. Preventive Care
AI-based preventive care represents a dynamic approach for patients in disease prevention. AI could predict the progression of disease by analyzing vast datasets and identifying early warning signs, allowing for timely interventions. With the help of AI predictive models, physicians, and healthcare staff can evaluate the social factors of healthcare to foresee and detect disease progression. For instance, the demand for ML and AI models accelerated the diagnosis of diseases, such as early diagnosis of cancer, with the help of the thermal image sensing model rather than the traditional self-examination model.
Other areas where AI-based image algorithms can be used are for detecting symptoms of diabetes-related eye problems. The image taken through AI is further processed through the algorithm of a convolutional neural network (CNN), which helps in understanding the difference between regular eye problems and diabetes-related eye problems.
1.2.2. Curative Care
Post-diagnosis treatment planning is a tedious process; however, ML techniques in AI can be implemented in various strategies to optimize the curative care process. AI-based curative care can identify and personalize patient treatments based on their symptoms and understanding of their needs just by analyzing the patient’s data.
AI-driven solutions streamline the post-diagnosis treatment process and produce solid planning that can assist doctors in developing novel therapies for sick patients. By evaluating patient information, genetic profiles, and clinical histories, AI could personalize cancer and surgical treatment. In order to train AI models to analyze medical situations and report on a better diagnosis, let’s look at the example of cancer patients, who typically obtain thorough imaging data during their treatment and checkups.
1.2.3. Palliative Care
Because of a shortage of money, staff, and facilities, palliative care is recognized to be a difficult undertaking, especially for patients who are near death. However, medical professionals can identify and classify patients’ needs for palliative care services and enhance electronic health records by using AI-based analytics.Â
With conversational AI, terminally ill patients can have sensitized discussions that might be difficult in the presence of a human being.
1.3. Disease Surveillance
1.3.1. Active Surveillance
A pervasive AI-enabled system captures the input of “critical patient-level information” from sources such as wearables, cameras, motion sensors, noise sensors, etc. to detect abnormalities in patients’ health. To detect such self-administration errors, a radio wave-enabled wireless sensed technology has been developed when the patient is taking medicines, injections, or an inhaler.
AI-integrated medical applications on smartphones or tablets can provide notifications to physicians to inform them about patient health and medication intake and to ensure timely medical advice during critical situations.
1.3.2. Passive Surveillance
Researchers and health officials can use AI’s search algorithm to learn more about new ailments. It can authenticate any legitimate open-source data to detect anomalies and new diseases early on.
AI-powered diagnostic models can also help in exploring and diagnosing new diseases and increase the accuracy of diagnosis compared to traditional methods of diagnosis, such as the use of mammography to detect breast cancer.
1.4. Research and DevelopmentÂ
Research and development help accelerate the process of discovering new drugs by implementing AI-based clinical trials and drug-resistant technologies.
1.4.1. Drug Discovery
The application of AI is necessary to support the healthcare ecosystem, especially in the case of streamlining drug discovery. The AI algorithms have the capability of high accuracy, which reduces the process and time of discovering new and updated drugs. The use of NLP is quite common in drug discovery, as this method involves data mining, which helps in predicting the effectiveness of potential treatments.
1.4.2. Clinical Trials
For accurate clinical research, the most essential step is to select the right protocol for clinical trials. Earlier, the patient selection process was conducted manually, which was a huge expense in the pharmaceutical industry, as the sampling might have been incorrect or the drug faced a higher failure rate due to patients’ ill health. However, with the help of predictive AI models, the patient recruitment process can be simplified and improved by furnishing effective samples that are selected perfectly for the population.
2. What is the future of AI in healthcare Policies?
The advent of AI technology has brought about significant implications for the development of both public and private health policy. What is the future of AI in healthcare? The integration of AI into policymaking is expected to refine healthcare regulations, ensuring equitable and efficient care delivery. AI proves to be a valuable tool for gaining insights by accessing vast datasets and advanced algorithms. The ability to quickly analyze vast amounts of data is one of the key benefits of using AI in health policy making as contrasted to traditional approaches. Trends, patterns, and possible areas for improvement can be found with the aid of real-time data systems. When it comes to using AI in policymaking, trust is essential. In creating this framework, patients, citizens, legislators, and healthcare professionals are essential stakeholders.
This feedback loop enables policymakers to access enormous volumes of data and develop AI-based policies, enabling them to base their choices on the truth rather than conjecture. Policymakers are becoming more conscious of the limitations and potential biases in their understanding of how to effectively leverage AI, in addition to devoting time to learning through training programs that require the knowledge and skills necessary to navigate the complexities of integrating AI into policy structures.Â
The future of AI in healthcare holds immense potential for helping shape public and private health policies. While prioritizing education and training initiatives and embracing this technology responsibly, custodians in the health tech industry can unlock the full potential for creating innovative and lasting solutions that address the relentless healthcare challenges.
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