Training Data to Employ AI in Healthcare

As artificial intelligence (AI) becomes an increasingly important tool in health care, it offers unprecedented opportunities for improving patient outcomes, reducing costs, and impacting population health. There are many examples, including automation, delivering a simple synthesis of complex health information to patients, families, and caregivers, and providing recommendations and visualizations for shared decision-making among patients, family members, and health professionals.

Need for AI Technology Adoption in Healthcare

As healthcare organizations realize the benefits of technology adoption as patient numbers rise and quality and affordable treatment become more important, they are adopting artificial intelligence. It is still early in the adoption of artificial intelligence in healthcare. The reason behind this is that many emerging economies lack the means of digitizing patient records. 

The increased number of diseases and the need for better understanding and diagnosis with the help of deep learning are some of the reasons for AI adoption in healthcare. In many healthcare organizations, AI has enabled them to treat more patients at lower costs and with improved efficiency. 

Need for Building Database for Healthcare AI

To effectively utilize AI in the healthcare industry, there is a need to build a database with all the historical data of patients, required to identify the patterns and accordingly use AI for diagnosis and therapy. If customized accurately, AI would not only be ‘intelligent’ but also highly user-friendly. 

It is also necessary to maintain records physically in certain countries in South Asia and Africa. However, the adoption of AI is relatively less challenging in developed countries such as the US and the UK, where the majority of healthcare data have already been digitized, and the technology has flourished. 

Significance of Data for AI Utilization in Healthcare 

In healthcare, data can be related to health conditions, reproductive outcomes, quality of life, and many similar life events determining an individual or an entire population. Health data includes clinical metrics along with environmental, socioeconomic, and behavioral information pertinent to health and wellness.

After getting to know the most important terms, definitions, and methods used in the field of artificial intelligence, we need to be familiarized with the current situation of data and data analytics in healthcare in order to be able to find out where artificial intelligence can come into play in healthcare; why and how we could make use of machine learning and smart algorithms – as the most important ‘base material’ for smart algorithms is data.

AI technologies that can Transform Healthcare

AI is based on technologies such as Natural Language Processing, Deep Learning, and Context-Aware Processing. The healthcare industry is undergoing a transformation as a result of these technologies. 

1. Natural Language Processing (NLP) 

In today’s world of complex data, AI plays a significant role in simplifying insights, and NLP makes the process more accessible to more people. The goal of Natural Language Processing (NLP) is to mimic the responses of humans when answering queries and holding conversations by utilizing algorithms. Using NLP healthcare providers can summarize narrative text by highlighting key concepts or phrases, such as those found in long journal articles or clinical notes. 

Data elements in EHRs, which are represented as unstructured text, can be transformed into structured, meaningful data by using Natural Language Processing. Using NLP, Intermountain Healthcare, for example, has identified the causes of illness in patients by mining their medical records in order to identify cases like strokes, cancer, heart failure, and venous thromboembolisms (blood clots in the leg’s deep veins).

2. Deep Learning 

By implementing AI in healthcare, diseases can be diagnosed and treated more efficiently. Medical data and images can be analyzed using deep learning, a component of AI, to enhance the ability of doctors to treat diseases. Visually challenged individuals can benefit from deep learning by narrating the text, identifying the facial expressions of nearby people, studying the surroundings, and describing the environment using computer vision, text-to-speech, and computer vision. 

Advanced neural network algorithms, GPUs that can process large amounts of data, and access to enormous amounts of internet data are all driving the deep learning revolution. A cognitive computing & deep learning approach is being used to train IBM Watson, for example, to assist doctors with medical diagnosis.

3. Context-Aware Processing 

As examples of AI applications that can be used in healthcare, Apple’s Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana can provide assistance based on the instructions provided by the programmer. Using AI chatbots in healthcare can eliminate the burden on medical professionals and reduce the time and cost of coordinating care and detecting issues or diagnosable health issues. 

Bots could be considered the next step in the transformation of health assistants. HealthTap, for instance, or Your.Md are Bots that use AI to diagnose the most common symptoms. Nevertheless, chatbots play an important role in directing patients to the right doctor for diagnosis and treatment. Doctors with experience would be able to rely on them to supplement their duties. 

4. Intelligent Robotics 

There is also the possibility of using AI with robotics. Healthcare facilities can be revolutionized with physical robots that can help people stay healthy and reduce the number of hospitalizations. In addition to AI, advanced humanoid designs are enabling robots to converse with and socially interact with elderly patients. With robots, surgery can be performed with more precision and smaller incisions since their flexibility and reach are greater. 

Certain robots can serve as social partners to treat mental health issues or alleviate loneliness. Blue Frog Robotics (developers of BUDDY), National Institute of Advanced Industrial Science and Technology, and AIST (developers of PARO) are among the companies that have expanded companion robots into healthcare.

Many technology companies are currently conceptualizing AI-powered surgical robots by leveraging the capabilities of machine learning applications like Google DeepMind, IBM Watson, and others. With AI-enabled robots, less damage will occur, precision will increase, and recovery will be faster.

Conclusion

With the growing amount of digital data and the complication of overseeing a large number of patient records, all end-users under the healthcare umbrella are embracing artificial intelligence. AI-enabled content analytics can enhance providers’ ability to administer effective and efficient treatment protocols by speeding up the diagnosis of patients’ conditions through the use of NLP tools.

Although AI has many proven applications in the healthcare sector, the extent of its implementation in medical practice is still at an initial stage. The healthcare industry is expected to undergo a transformational change as AI adoption increases. It is estimated that healthcare data diagnosis and treatment procedures will improve as a result of the expected growth in the future. The healthcare industry can reap numerous benefits from AI, which has a wide range of applications.

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Challenges to Successful AI Implementation in Healthcare

“Al will not replace doctors but instead will augment them, enabling physicians to practice better medicine with greater accuracy and increased efficiency.”

– By Benjamin Bell (Scottish Scientific Surgeon)

Artificial intelligence (AI) and machine learning (ML) have received widespread interest in recent years due to their potential to set new paradigms in healthcare delivery. It is being said that machine learning will transform many aspects of healthcare delivery, and radiology & pathology are among the specialties set to be among the first to take advantage of this technology.

Medical imaging professionals in the coming years will be able to use a rapidly expanding AI-enabled diagnostic toolkit for detecting, classifying, segmenting, and extracting quantitative imaging features. It will eventually lead to accurate medical data interpretation, enhanced diagnostic processes, and improved clinical outcomes. Advancements in deep learning (DL) and other AI methodologies have exhibited efficacy in supporting clinical practice for enhanced precision and productivity.

Hurdles to AI Integration into Healthcare

Though AI can empower healthcare and diagnostic processes with automation integration, some challenges exist. The lack of annotated data makes it difficult to train deep-learning algorithms. Moreover, the black-box nature leads to the opacity of the results of deep learning algorithms. Clinical practice faces critical challenges when incorporating AI into healthcare workflows.

The key challenges to successful AI implementation in the healthcare practice are as follows:

1. Ethical & Legal Issues for Data Sharing
2. Training Healthcare Practitioners and Patients to Operate Complex AI Models
3. Managing Strategic Change to Put AI Innovations into Practice

1- Ethical & legal Issues Hindering Access to High-Quality Datasets for AI Developers

Whether integrating artificial intelligence in medical imaging or employing deep learning technology to maneuver clinical diagnostic procedures, high-quality healthcare datasets are the key to success. As we tend to figure out the critical roadblocks to developing AI models for healthcare, it’s been found that ethical and legal issues have so far been the biggest hurdle to developing AI-powered machine learning models.

Since patients’ health information is protected by law as private and confidential, healthcare providers must comply with strict privacy and data security policies. However, it keeps healthcare practitioners under the ethical & legal obligation not to provide their data to any third party. Consequently, it hinders AI developers from accessing high-quality datasets to develop AI training data for healthcare machine learning models.

In addition to ambiguities in existing laws and challenges associated with sharing data between organizations, healthcare leaders also identified external conditions and circumstances as challenges. As a result of these challenges, uncertainties arose concerning responsibilities concerning the design and implementation of AI systems and what is permissible, resulting in legal and ethical concerns.

2- Training Healthcare Practitioners and Patients to Use Complex AI Models

Incorporating AI systems could improve healthcare efficiency without compromising quality, and this way, patients could receive better and more personalized care. Investigations, assessments, and treatments can be simplified and improved by using AI systems that are smart and efficient. However, implementing AI in healthcare is challenging because it needs to be user-friendly and procure value for patients and healthcare professionals.

AI systems are expected to be easy to use and user-friendly, self-instructing, and not require extensive prior knowledge or training. Besides being simple to use, AI systems should also be time-saving and never demand different digital operative systems to function. For healthcare practitioners to efficiently operate AI-powered machines and applications, AI models must be simple in terms of their features and functionality.

3- Managing Strategic Change to Put AI Innovations into Practice

The healthcare experts noted that implementing AI systems in the county council will be difficult due to the healthcare system’s internal capacity for strategic change management. For the promotion of capabilities to work with implementation strategies of AI systems at the regional level, experts highlighted the need for infrastructure and joint ventures with familiar structures and processes. Organizational goals, objectives, and missions needed to be achieved through this action to obtain lasting improvement throughout the organization.

Healthcare professionals only partially determine how an organization implements change since change is a complex process. In Consolidated Framework for Implementation Research (CFIR), we need to focus on organizational capabilities, climates, cultures, and leadership, which all play a role in the “inner context.” Maintaining a functioning organization and delivery system is part of the capacity to put innovations into healthcare practice.

Enhancing Healthcare by Integrating Artificial Intelligence in Medical Imaging through Data Annotation

An imaging technique that allows us to see inside the body without having to open the body up surgically is known as a medical imaging technique (MIT). The use of AI in clinical diagnostics has demonstrated some of its most promising applications, including X-ray radiography, computed tomography, magnetic resonance imaging, and ultrasound imaging.

Machine learning will improve the radiology patient experience at every step. Much of the initial focus for the application of machine learning in medical imaging has been on image analysis and developing tools to make radiologists more efficient and productive. The same tools will often enable more precise diagnosis and treatment planning or help reduce missed diagnoses, thus leading to improved patient outcomes.

AI & machine learning have a much broader role in radiology beyond clinical decision-making and can help improve the patient experience throughout the imaging process — all the way from the initial scheduling of the imaging examination to the end of diagnosis and follow-up.

Taking a look at the trends around the healthcare system, we can see machine learning has applications that go beyond diagnostic and medical imaging. It can enhance the data acquisition process to ensure the highest quality image for each examination and assist imaging departments in maximizing operational performance efficiently.

Conclusion

Since the medical industry is at the dawn of a new wave of AI-fueled tech innovation, it is time for health providers to establish a roadmap for incorporating AI into their clinical practice. As the global population continues to grow, healthcare practitioners must invest in technologies that can improve patient care and transform clinical workflows. The application of artificial intelligence to healthcare delivery is unquestionably at the top among technologies that can revolutionize clinical processes.

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AI in Healthcare Giving Accurate Result with Better Training Data

AI in Healthcare is improving its footprint by giving more accurate and reliable results. As per the recent studies, AI bots scored 90% for overall accuracy, compared to 64- 94% for the medical experts. The bot further also performed at 98% accuracy on conditions seen most frequently in primary care medicine, compared to a 52-99% range achieved by the physicians.   

The machine and diagnosis system backed with AI technology now getting better with day-by-day improvements to diagnose the various types of critical diseases and provide timely and effective medical treatments to patients. The AI-based healthcare system is improving due to more quality deep learning data used to train such devices to get more accurate results.   

Significance of AI Training Data in Healthcare

 

                                                                             AI in Healthcare

The new age of human civilization is severely stricken by the fast-paced life system and healthcare is not the odd way out in the array. The increasing global population has also burdened the healthcare system with a load of inclusive treatment for all, i.e., the treatment that is accessible and available on the demand. The challenge which is seemingly not conquerable is possible with AI as the AI-based automated systems can potentially boost the treatment and diagnostic procedures.

With the growing importance of AI and machine learning in the healthcare sector, the quality training data has become equally significant in medical processes. It, in all sincerity, is the accuracy and quality of data, i.e., the training datasets, matters in developing AI training data for automation-powered machine learning systems. Training data development companies have an important role to play in the course when it comes to developing high-quality training data in line with industrial processes automation. 

How Training Data Companies can Assist Implementation of AI in Healthcare

The AI training data companies, i.e., the data annotation and labeling experts in the AI space around can significantly assist the implementation of AI in healthcare with high-quality training data. Cogito, Anolytics, and other such training data companies of the same league are known to work with the high-end medical training data development tools to produce quality annotated and labeled data for AI implementation in healthcare. 

The AI-powered healthcare applications and automated systems developed on the back of quality training data can facilitate medical practitioners and patients with advanced medical treatments reducing the mortality rates among humans. The training data companies can also bring medical imaging solutions at disposal of healthcare facilities to help them with pattern recognition of various kinds of medical reports ensuring the quality of treatment and diagnostics.