Cloud Computing, Artificial Intelligence and Machine Learning in Healthcare. The Future of Patient Care  

In recent years, Artificial Intelligence (AI) and cloud computing have come together to transform the healthcare industry, redefining the patient care landscape. Healthcare brings exceptional challenges to technology. The clinical data on diseases, individuals, and pharmaceuticals is large and complex – according to RBC Capital Market, approximately 30% of the world’s data originates from the healthcare sector.

Scientists desire secure access to extensive data to enhance medical knowledge and continue to make new discoveries to improve health protection. Consumers, in turn, seek user-friendly mobile solutions and telemedicine services. The answer to this need is an alliance between cloud computing and AI, which has the potential to transform the way medical data is managed. 

Cloud Solutions, AI, and Machine Learning in Healthcare  

As opposed to traditional on-site data centers or storing data on private computers, the cloud creates a solid, accessible, and collaborative environment for patients and professionals offering on-demand resources for data collection, processing, protection, and storage. 

In the article “Cloud’s trillion-dollar prize is up for grabs” McKinsey analysts indicate that cloud solutions have the potential to bring value of up to $170 billion to healthcare companies by 2030. 

AI, in turn, offers the healthcare industry a number of possibilities, revolutionizing the way we approach patient care, diagnostics, disease management, and medical research via machine and Deep Learning solutions.   
 
AI has huge potential in terms of the automation of repeatable tasks. The WHO projects that by 2030, demand for medical assistants will exceed 18 million across Europe. That means that the existing supply of 8 million medical assistants will not be sufficient to fulfill the forecasted needs in the future.   

The fusion of ML, AI and cloud computing creates a healthcare ecosystem where data-driven algorithms are combined with the limitless capabilities of the cloud, all with the goal of improving individual health and the advancement of medical science. In this article, we will present the most important advantages of cloud computing for healthcare organizations and key AI applications that can change the future of healthcare.  

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The Key Benefits of Cloud Computing for Healthcare Organizations:  

• Security/patient safety: Data security in healthcare is a principal priority. According to IBM’s Cost of a Data Breach Report 2023, in the healthcare sector, the average cost of a data breach is estimated at almost $11 million (compared to nearly $6 million in the financial sector). But let’s remember that the stakes are much higher. The irreversible loss of medical data could not only cause serious legal and medical problems for healthcare facilities, but also affect the lives of patients. By using cloud-based tools, organizations avoid the high risks inherent in using local data storage methods. Cloud resource providers such as Azure meet institutional requirements for compliance, security, and reliability, offering privacy standards like HIPAA and GDPR. Cloud systems can further ensure data protection by means of network firewalls, customer-managed encryption, battling against malware, and identifying vulnerabilities open to potential threats. Thanks to backups on servers located in diverse geographic locations, the cloud strategy offers quick and easy data recovery in case of, e.g., natural disaster, human error, or system failure.   
• Reduced costs: Health companies face a complex set of costs associated with data management such as hardware (including servers and storage infrastructure), software (licensing fees, backup and recovery systems, security measures and compliance regulations, insurance, scaling), or space expenses. Keeping and maintaining the whole infrastructure in-house comes with huge costs, which includes hiring dedicated IT staff. Switching to a pay-as-you-go subscription model offered by cloud services allows for cost savings as healthcare institutions pay only for the resources they use. Additionally, systems updates are regularly carried out by software vendors.   
• Data storage with increased scalability and flexibility: The amount of data in the medical sector is constantly growing, and with the introduction of new methods, new data types are often presented. Cloud services provide an unlimited capacity for all types of data and can dynamically scale the storage up or down as needed. What’s more, there are a variety of models, applications, and services of cloud platforms available tailored to the specific requirements of enterprises.  
• Accessibility: Cloud-based applications with storage systems containing EHRs (Electronic Health Records) allow doctors, nurses, and other healthcare workers to gain a smooth insight into detailed patient information and critical data at any time, and from any location or device with Internet access. Authorized specialists can enter the data on medical treatments or appointments which is updated in real time. This creates a mutual benefit for the entire medical organization, and consequently for patients. They obtain insight into up-to-date information on prescriptions and online appointments without having to come to the facility in person. Furthermore, it allows for more precise diagnostics as physicians have insight into all historical medical records.   
• Collaboration: In the vast majority of cases, more than one specialist is responsible for a patient’s health, and providing updated data without an information exchange system is difficult, time-consuming, and laborious. Cloud services enable effective cooperation across departments and between healthcare professionals, which comes with the important benefit of improved diagnostics. It also minimizes the risk of misinterpretation. Cloud systems provide a secure environment for storing sensitive data and help the decision-making process run smoothly by facilitating access to the existing treatment history, previous doctors’ opinions, and medical records without involving the patient. Additionally, the cloud overcomes geographic and logistical barriers, enabling remote conferencing among professionals. All of this helps people work together toward a common goal of ensuring the best possible quality of healthcare and ultimately making people’s lives better.  
• Interoperability: Most medical applications or platforms function separately. The cloud provides an integrated system that consolidates various medical applications, reducing the need to switch between different systems and interfaces. The use of a single environment ensures that data remains consistent across various applications. This decreases the risk of data discrepancies and errors, which can have critical implications for patient safety. Additionally, the interoperability of systems is desirable to healthcare professionals, who do not need to learn so many systems since the data is integrated in the cloud. This leads to shorter training periods, the simplification of everyday work, and quicker onboarding for new staff members. A unified environment can scale more efficiently to adapt to the changing needs of a healthcare organization, whether it’s adding new departments, locations, or services. Finally, integrated systems can leverage the full spectrum of healthcare data for analytics, improving insights into patient test results, treatment outcomes, and resource utilization.   
• Speed: In the world of healthcare, speed is crucial to diagnostics, as any delays can result in disease progression or complications. Cloud-based medical applications can provide real-time updates on patient conditions and test results. Medical imaging, such as MRI and CT scans, can be processed and transmitted swiftly to specialists for timely diagnosis and treatment planning. This ensures that healthcare providers always have the latest information at hand.  Cloud technology also plays a pivotal role in telemedicine, dramatically improving the healthcare level by remotely monitoring vital signs through cloud-connected wearable devices and providing high-speed connections that make remote consultations possible.  

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Machine Learning and Artificial Intelligence in Healthcare  

For the above-mentioned reasons, cloud computing is commonly used for Big Data and AI applications. This kind of software development project often requires significant computational power and storage capacity. Cloud providers offer scalable resources, allowing organizations to expand their infrastructure quickly and easily as data and AI workloads grow. Many cloud providers offer high-performance computing (HPC) options, including GPU and TPU instances, which are essential for AI tasks such as Deep Learning and for processing Big Data sets. In cloud resources, there are AI services, such as pre-trained Machine Learning models and development tools, which accelerate AI development and deployment.  

AI and ML healthcare use cases 

Within the healthcare sector, Artificial Intelligence has a significant impact on patient treatment, diagnostics, operational effectiveness, and the advancement of medical science. However, there are also several potential challenges associated with its implementation including data protection and quality, ethical concerns, or risk management. Apart from that, AI-powered diagnostic tools can analyze medical images and patient data with exceptional accuracy and speed; therefore, this technology can be used in the healthcare industry in numerous ways, such as:  

• Medical Imaging Analysis: AI algorithms can analyze medical images, such as X-rays, MRIs, and CT scans, to assist radiologists and other healthcare professionals in identifying diseases and abnormalities. For instance, Google’s DeepMind developed an AI system based on optical coherence tomography (OCT) images to detect eye diseases. The research, conducted in 2022, uses Deep Learning models trained with MRI images to make an accurate classification of brain tumors.  
• Virtual Health Assistance: AI-driven medical chatbots and virtual health assistants provide personalized symptom analysis, medical advice, and recommendations (Healthily, Ada Health).  
  The GYANT chatbot can direct the patient to a suitable specialist or connect patients and medical professionals, gathering symptom-related information and passing it on to healthcare professionals, who can then use it to suggest treatment plans and write a prescription. Youper and Woebot are AI-powered digital mental health applications which aim to fight anxiety and depression through conversation and support. 
• Decision Support Systems: AI-powered decision support systems are integrated into EHRs and assist healthcare providers in making informed decisions when prescribing medications. These systems analyze patient data, including medical history, current medications, allergies, and lab results, to provide recommendations on appropriate drug choices, dosages, and potential drug interactions. For instance, MedAware’s AI model continuously analyzes EHRs and patient data in real time. It identifies potential prescription errors, such as incorrect dosages, drug interactions, or medications that may not be suitable for a patient’s medical condition.   
• Predictive Genomic Analysis: AI algorithms are used to analyze vast amounts of genomic data generated through techniques such as whole-genome sequencing. These algorithms can efficiently align and map sequenced DNA reads to a reference genome, identifying variations and mutations. AI accelerates drug discovery by analyzing vast datasets to predict potential drug compounds and explore the toxicity of new drug candidates. For example, the startup Atomwise identifies new drug candidates by forecasting the interaction between small molecules and target proteins. AI can also recognize biomarkers and genetic mutations to detect diseases and guide the development of targeted therapies, which maximizes treatment efficacy and minimizes side effects (Deep Genomics). 
• Natural Language Processing (NLP): NLP techniques are used to extract valuable insights from unstructured clinical notes, research papers, and patient communications. Companies like BlueDot use Natural Language Processing systems and Machine Learning Algorithms, working in over 60 languages to create an international early detection system for infectious diseases. The algorithm monitors news outlets and official healthcare reports around the world, noting whether they mention high-priority diseases like COVID-19 or others such as HIV/AIDS or tuberculosis. Linguamatics is an NLP-based text-mining platform with solutions for pharma, biotechnology, healthcare specialists, and governments. Their drug-label exploration solution captures and extracts relevant information from regulatory and compliance documents, which brings dynamically updated data for labeling teams.   
• Personalized treatment plans: AI algorithms can analyze patient data, including genetic predispositions, lifestyle choices, medical histories, and treatment responses, to create personalized treatment plans. AI assesses a patient’s risk factors for various diseases based on their unique health data – for instance, the risk of heart disease, diabetes, or certain cancers. K Health is one example of a healthcare technology company that uses AI to provide personalized healthcare insights and treatment recommendations to patients.   

Cloud computing and AI in healthcare – summary  

In conclusion, the fusion of  Cloud Computing, Artificial Intelligence, Machine Learning, and Deep Learning has massive potential to reshape the future of patient care and medicine. The power of AI to personalize treatment plans, improve diagnostic accuracy, improve patient care, and enhance decision-making means it is well-positioned to revolutionize healthcare service delivery. Together with the advantages of cloud computing such as data security, cost reduction, speed, scalability, and accessibility, these technologies empower healthcare providers to deliver more precise, efficient, and patient-centered care, offering the potential for improved therapies and a more agile healthcare ecosystem. 

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