2 posts tagged with "healthcare"

Computer vision is transforming healthcare by enabling advanced imaging analysis to aid in diagnosis, treatment, and patient care.

Half of the world's population does not have access to quality healthcare, and many people are driven into poverty. Over \$140 billion annually would be invested to achieve health-related sustainable development goals. There is a significant financing space for health IT, digital IT, and AI to help close the healthcare gap in developing countries.

As much as \$2 billion was invested in 2018 by health startups and IT businesses specifically to use AI technology. These funds account for a significant chunk of the total capital allocated to artificial intelligence projects.

This series focuses on how computer vision and deep learning are being used in industrial and business environments on a grand scale. This article will discuss the benefits, applications, and challenges of using deep learning methods in healthcare.

Benefits of Computer Vision and Machine Learning for Healthcare Innovation#

Unlocking Data for Health Research#

Plenty of new data is becoming readily available in the healthcare industry. This opens up vast opportunities for study and improvement. Mining and properly analyzing this data may improve clinical outcomes, earlier illness identification, and fewer preventable missteps.

However, getting enough high-quality, well-structured data is complex, especially in developing countries. Businesses use analytics and data cleansing methods to increase data interoperability. Also, this helps them to pave the way for valuable predictions that improve medical outcomes and decrease related issues.

Besides organizing data for analysis, using ML in large data settings can better connect patients. However, a business can accelerate the development of new drugs and pinpoint the most successful treatments in the life sciences.

Healthcare Efficiency#

SaaS businesses automate numerous activities. This includes arranging follow-up appointments and using patient data like consultation notes, diagnostic images, prescription prescriptions, and public information. This software-as-a-service (SaaS) offerings are revolutionizing developing countries by addressing problems like a need for qualified medical professionals and an absence of information about the quality of treatment.

Reaching Underserved Communities#

Emerging countries use digital health technologies for health information, diagnosis, and treatment. Digital healthcare solutions can efficiently assist marginalized people, particularly in rural areas.

Machine learning may diagnose and suggest a specialist using public data and customer information. After reviewing the specialist's qualifications and user reviews, the patient may schedule a chat or call and pay online. In rural and low-income regions with few 3G-4G access and smart devices, SMS healthcare advice is a game-changer.

Applications of Computer Vision and Machine Learning#

1. Medical Research in Genetics and Genomics#

AI may help medical researchers discover drugs, match research studies, and find successful life-science remedies by analyzing important, complex information. AI can help researchers find disease-causing variations in genes and predict therapy outcomes.

By identifying patterns, AI can help us understand how human physiology reacts to drugs, viruses, and environmental variables. Machine learning algorithms may also analyze DNA sequences to predict the possibility of a disease based on data trends.

2. Medical Imaging and Radiology#

Machine learning and deep learning have improved radiology breast cancer diagnosis and CT colonography polyp identification. Deep learning algorithms can automatically extract and classify pictures rapidly, helping neuroimaging methods like CT and MRI diagnose strokes.

AI algorithms based on super-resolution methods may improve scan quality, which is generally inadequate owing to time restrictions in stroke patient management. AI can automatically identify tumors and enhance TB detection using X-ray and MRI data. AI can also use PET data to diagnose Alzheimer's early.

3. Pathology#

Digital pathology has created large volumes of data that may be utilized to teach AI frameworks to recognize trends and ease the global pathologist shortage. AI can automate hard and time-consuming activities like object quantification, tissue categorization by morphology, and target identification, helping pathologists.

AI may also compute personalized therapies, reduce the chance of misdiagnosis and drug errors, and encourage telepathology by permitting remote consultation with specialized pathologists. Finally, AI can identify visible signs like tumor molecular markers.

4. Mental Health#

Mental health management needs interaction between patients and providers. To enhance this connection, NLP and machine learning can collect and adapt to new facts. Virtual assistants, chatbots, and conversational agents can simulate human-like presence and help in searching online support communities, diagnosing major depressive disorder, and delivering cognitive behavioral therapy to individuals with depression and anxiety.

Moreover, virtual agents can serve as moderators of online communities for youth mental health when human moderators are unavailable. These agents can analyze participant posts' sentiments, emotions, and keywords to suggest appropriate steps and actions.

5. Eye Care#

Point-of-care diagnostics using AI can replace visual software. Deep learning distinguishes healthy and AMD-afflicted eyes. It automatically predicts cardiovascular illness from retinal fundus images, evaluates age-related macular degeneration, checks for glaucoma, and diagnoses cataracts.

Some Challenges Faced While Using AI in Healthcare#

The following are the key risks and challenges associated with using AI in the healthcare industry:

• Data privacy and security concerns.
• The effectiveness of AI may be limited for data that are difficult to obtain or rare.
• AI systems typically operate as black-box decision-makers, making it challenging or even impossible to understand the underlying logic that drives the outputs generated by AI.
• The system's insensitivity to impact means prioritizing making accurate decisions, even if it results in missed or overdiagnosis.
• Legal and regulatory challenges.
• Integration with existing healthcare systems.
• Limited accessibility to AI-based healthcare solutions for underserved communities.
• Technological limitations and the need for continuous monitoring and maintenance.

Hence, healthcare businesses must keep these issues in mind while integrating AI into their regular systems.

Conclusion#

Significant investments are being pumped into the health technology and artificial intelligence industries to fill the gaps in healthcare services in growing countries. Artificial intelligence has shown some encouraging outcomes in several medical sectors, including radiology, medical imaging, neurology, diabetes, and mental health.

AI may assist in drug development, match patients to clinical trials, and uncover successful life-science solutions, all areas in which the medical research community can benefit. AI does this by analyzing and recognizing patterns in big and complicated datasets.

However, some challenges must be overcome to integrate AI successfully into the healthcare industry.

Introduction#

In the field of healthcare technology, we are at the start of a high-tech era. AI technology, cloud-based services, the Internet of Things, and big data have all become popular topics of conversation among healthcare professionals as a way to provide high-quality services to patients while cutting costs. Due to ambitions for global application, the fifth generation of cellular technology, or 5G, has gotten a lot of interest. While the majority of media attention has centered on the promise of "the internet of things," the ramifications of 5G-enabled technologies in health care are yet to be addressed (Zhang and Pickwell-Macpherson, 2019). The adoption of 5G in healthcare is one of the elements that is expected to have a significant impact on patient value. 5G, or fifth-generation wireless communications, would not only provide much more capacity but also be extremely responsive owing to its low latency. 5G opens up a slew of possibilities for healthcare, including remote diagnostics, surgery, real-time surveillance, and extended telemedicine (Thayananthan, 2019). This article examines the influence of 5G technology on healthcare delivery and quality, as well as possible areas of concern with this latest tech.

What is 5G?#

The fifth generation of wireless communication technology is known as 5G. Like the preceding fourth generation, the core focus of 5G is speed. Every successive generation of wireless networks improves on the previous one in terms of speed and capability. 5G networks can deliver data at speeds of up to 10 terabytes per second. Similarly, while older networks generally have a delay of 50 milliseconds, 5G networks have a latency of 1–3 milliseconds. With super-fast connection, ultra-low latency, and extensive coverage, 5G marks yet another step ahead (Carlson, 2020). From 2021 to 2026, the worldwide 5G technology market is predicted to grow at a CAGR of 122.3 percent, reaching $667.90 billion. These distinguishing characteristics of 5G enable the possible change in health care as outlined below.

5G's Importance in Healthcare#

Patient value has been steadily declining, resulting in rising healthcare spending. In addition, there is rising concern over medical resource imbalances, ineffective healthcare management, and uncomfortable medical encounters. To address these issues, technologies such as the Internet of Things (IoT), cloud technology, advanced analytics, and artificial intelligence are being developed to enhance customer care and healthcare efficiency while lowering total healthcare costs (Li, 2019). The healthcare business is likely to see the largest improvements as a result of 5G's large bandwidth, reduced latency, and low-power-low-cost. Healthcare professionals investigated and developed several connected-care use cases, but widespread adoption was hampered by the limits of available telecommunications. High-speed and dependable connections will be critical as healthcare systems migrate to a cloud-native design. High data transfer rates, super-low latency, connection and capacity, bandwidth efficiency, and durability per unit area are some of the distinctive properties of 5G technology that have the potential to assist tackle these difficulties (Soldani et al., 2017). Healthcare stakeholders may reorganize, transition to comprehensive data-driven individualized care, improve medical resource use, provide care delivery convenience, and boost patient value thanks to 5G.

5 ways that 5G will change healthcare#

• Large image files must be sent quickly.
• Expanding the use of telemedicine.
• Improving augmented reality, virtual reality, and spatial computing.
• Remote monitoring that is reliable and real-time.
• Artificial Intelligence

Healthcare systems may enhance the quality of treatment and patient satisfaction, reduce the cost of care, and more by connecting all of these technologies over 5G networks (Att.com, 2017). 5G networks can enable providers to deliver more tailored and preventative treatment, rather than just responding to patients' illnesses, which is why many healthcare workers joined providers during the first round.

Challenges#

As with other advances, many industry professionals are cautious about 5G technology's worldwide acceptance in healthcare, as evidenced by the following significant challenges:

• Concerns about privacy and security - The network providers must adhere to the health - care industry's stringent privacy regulations and maintain end-to-end data protection across mobile, IoT, and connected devices.
• Compatibility of Devices - The current generation of 4G/LTE smartphones and gadgets are incompatible with the upcoming 5G networks. As a result, manufacturers have begun to release 5G-enabled smartphones and other products.
• Coverage and Deployment - The current generation of 4G/LTE smartphones and gadgets are incompatible with the upcoming 5G networks. The present 4G network uses certain frequencies on the radio frequency band, often around 6 GHz; however, such systems are available exclusively in a few nations' metro/urban regions, and telecom carriers must create considerable equipment to overcome this difficulty (Chen et al., 2017).
• Infrastructure - As part of the 5G network needs, healthcare facilities, clinics, and other healthcare providers/organizations will need to upgrade and refresh their infrastructure, apps, technologies, and equipment.

Conclusion#

5G has the potential to revolutionize healthcare as we know it. As we saw during the last epidemic, the healthcare business needs tools that can serve people from all socioeconomic backgrounds. Future improvements and gadgets based on new 5G devices and computers can stimulate healthcare transformation, expand consumer access to high-quality treatment, and help close global healthcare inequities (Thuemmler et al., 2017). For enhanced healthcare results, 5G offers network stability, speed, and scalability for telemedicine, as well as catalyzing broad adoption of cutting-edge technologies like artificial intelligence, data science, augmented reality, and the IoT. Healthcare organizations must develop, test, and deploy apps that make use of 5G's key capabilities, such as ultra-high bandwidth, ultra-reliability, ultra-low latency, and huge machine connections.

References#

• Att.com. (2017). 5 Ways 5G will Transform Healthcare | AT&T Business. [online] Available at: https://www.business.att.com/learn/updates/how-5g-will-transform-the-healthcare-industry.html.
• Carlson, E.K. (2020). What Will 5G Bring? Engineering.
• Chen, M., Yang, J., Hao, Y., Mao, S. and Hwang, K. (2017). A 5G Cognitive System for Healthcare. Big Data and Cognitive Computing, 1(1), p.2.
• Li, D. (2019). 5G and Intelligence Medicine—How the Next Generation of Wireless Technology Will Reconstruct Healthcare? Precision Clinical Medicine, 2(4).
• Soldani, D., Fadini, F., Rasanen, H., Duran, J., Niemela, T., Chandramouli, D., Hoglund, T., Doppler, K., Himanen, T., Laiho, J. and Nanavaty, N. (2017). 5G Mobile Systems for Healthcare. 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).
• Thayananthan, V. (2019). Healthcare Management using ICT and IoT-based 5G. International Journal of Advanced Computer Science and Applications, 10(4).
• Thuemmler, C., Gavras, A. and Roa, L.M. (2017). Impact of 5G on Healthcare. 5G Mobile and Wireless Communications Technology, pp. 593-613.
• Zhang, M. and Pickwell-Macpherson, E. (2019). The future of 5G Technologies in healthcare. 5G Radio Technologies Seminar.