What is AI in healthcare?
Artificial intelligence (AI) transforms every aspect of healthcare—from research and new medicine development to patient care, operations, and healthcare data management. This guide explores how healthcare organizations can utilize AI to introduce efficiencies and improve outcomes for patients and healthcare professionals across the sector.
Healthcare organizations face numerous challenges as they strive to deliver better patient care experiences. As demand for quality healthcare delivery increases, so do medical costs, regulatory concerns, and operational bottlenecks. Often, medical professionals find themselves pressured to optimize the resources they are provided to improve patient outcomes while maintaining medical integrity.
AI, specifically generative AI, can help healthcare organizations address their challenges. Generative AI is adept at analyzing data at scale and identifying complex patterns that humans often miss. In the healthcare sector, AI technologies help process the vast and diverse data volumes that medical facilities collect, introducing various innovative use cases. Medical staff can use AI apps to augment their workflow for more accurate and efficient delivery. Likewise, medical research, billing, prescription, and other healthcare-related processes benefit from the data-driven insights that AI systems deliver.
Initially, healthcare providers hesitated to adopt AI due to infrastructure costs, ethical risks, and data security concerns. However, as AI evolves, it becomes better supported by cloud providers, resulting in a cost-effective, compliant, and secure AI-enabled environment. For example, organizations use Amazon Bedrock to build healthcare AI applications with popular AI models and enjoy pay-as-you-go pricing.
What are the applications of AI in healthcare?
AI technologies allow healthcare providers to overcome problems related to population health management, research, and patient care.
Medical research
Drug discovery, gene research, and clinical trials are critical to advancement in medical practices. Yet, these disciplines require careful study, experiments, and validation that often span years. During the phases, medical researchers must consolidate vast datasets, verify their accuracy, and identify patterns leading to new hypotheses. On their own, researchers are at risk of data inconsistencies, which can delay research outcomes.
AI can identify, categorize, and analyze clinical datasets more rapidly. With AI, researchers can formulate new drugs at a fraction of the time they previously took. AI also supports gene research, where scientists spend time on multiomic and multimodal analysis. For example, when conducting cancer research, Roche uses AWS HealthOmics to reduce analysis time from 1 year to 3 months. With AWS HealthOmics, they derive insights from genomic, transcriptomic, and other omics data to develop better therapeutics. You can also use AWS HealthOmics to accelerate drug development and clinical trials by automatically assessing the drug candidate's efficacy.
Diagnosis
The global population is at risk of contracting preventable diseases due to changing lifestyles. Medical professionals at the frontline of the healthcare establishment are tasked with promptly consulting, diagnosing, and treating patients. However, conventional medical technologies are not always efficient. As such, doctors are burdened with administrative tasks instead of tending to the patient's needs.
When integrated strategically, AI helps streamline diagnosis and free up valuable time for medical professionals. Generative AI and computer vision technologies can also identify tumors, fractures, and other anomalies for timely medical intervention. For example, lab technicians use AWS HealthImaging to store large volumes of medical images on the cloud, which doctors can retrieve later. HealthImaging supports DICOM P10 and reduces storage costs by up to 40% with advanced file compression technologies.
Besides reducing disease diagnosis time, healthcare AI technologies are equally helpful in treating patients. Doctors can leverage AI to design a treatment plan by analyzing a patient's medical history, current diagnosis, and other possible risks. Nurses can remotely monitor patients with AI-powered telehealth technologies.
Healthcare data management
Clinicians access and share EHR for diagnosis, treatment, billing, and other medical purposes. With AI, they can search for appropriate patient records or other clinical data more effortlessly. AI automation systems break down data silos, allowing medical staff to retrieve the information they need in an instant. Teams can share EHR and administrative data more effortlessly between departments and organizations. This way, the patient's rehabilitation is more coordinated, and they receive remedial measures based on real-time observations.
For example, doctors can use Amazon HealthScribe, powered by AI models, to convert conversations they had with patients into medical notes instead of manually transcribing them.
While generative AI democratizes data access in healthcare establishments, healthcare stakeholders must take appropriate measures to ensure patient privacy, data security, and compliance with healthcare laws. AWS Wickr is a cloud messaging service enabling medical staff to communicate patient information securely. When developing a telehealth system for the US Army Telemedicine & Advanced Technology Research Center, Deloitte integrated Wickr with the military network, enabling medics to provide critical care to wounded warfighters with minimal safety risks.
Clinical chatbot and virtual assistant
Doctors are often overwhelmed with mundane tasks that rob them of time they can dedicate to improving patient care. For example, they might need to retrieve a diagnosis result from a different department, which they summarize later when formulating treatment options.
AI models excel in understanding and responding to everyday conversation. Integrating an AI chatbot with the clinical process helps doctors make swift decisions and accelerate treatment. For example, doctors use Amazon Comprehend Medical to extract specific medical terms from prescriptions, procedures, or diagnoses.
Similarly, patients can enjoy a more personalized and positive experience when interacting with an AI-powered assistant. For example, instead of calling a clinic to schedule an appointment, they can state their appointment details to the virtual health assistant.
Administrative workflow automation
Healthcare AI technologies support the administrative functions of medical facilities. From onboarding patients to billings and insurance claims, AI solutions can improve operational efficiency by automating repetitive tasks and consolidating healthcare data. For example, healthcare staff can leverage AWS Intelligent Document Processing (IDP) to extract, process, and classify information from medical records. IDP uses AI to summarize large volumes of health data and turn it into actionable insights.
Remote patient care
Sometimes, patients require continuous care after they leave a medical establishment. This creates operational and logistical challenges for medical teams, particularly when monitoring the patient's condition. To support such efforts, healthcare providers deploy Internet of Things (IoT) devices, which patients wear when they leave the facility. The device continuously sends health data to a secure cloud server, which AI models then analyze. For example, BioT, a medical IoT device provider, uses AWS IoT Core to develop a more connected remote patient monitoring system. AWS IoT Core connects medical devices to the cloud, allowing them to exchange data securely.
Healthcare robotics
Robotic systems have proven to be a reliable assistant in medical procedures. With AI, healthcare robots can further boost clinical workflows. For example, an AI robotic arm can assist in surgery procedures or analyze tissue samples extracted in biopsies.
Even in day-to-day operations, AI-powered robotics has proven useful. Diligent Robotics created Moxi, an AI robot that fetches stuff for frontline clinicians. The robot, developed using AI models from Amazon SageMaker, relieves nurses of non-patient-facing workloads. Amazon SageMaker provides tools to build AI applications and analyze data in a unified platform.
How do organizations get started with AI in healthcare?
Generative AI benefits the healthcare industry in various ways. However, responsible use of AI is essential to protect the interests of doctors, patients, and other healthcare stakeholders. We share several points of consideration when implementing AI in healthcare.
Collect and store health data
Healthcare AI applications collect, store, and share medical data across various departments to ensure medical teams share the same consensus about a patient's well-being. The immense volume of patient data transferred between AI tools poses data security, privacy, and compliance challenges for medical establishments. For example, healthcare providers operating in the US are compelled by the Health Insurance Portability and Accountability Act (HIPAA), which highlights the responsibility of organizations to protect health information. Thus, healthcare providers need to establish a secure data storage and exchange mechanism to reap the full benefit of AI.
AWS HealthLake is a HIPAA-eligible service that allows healthcare providers to store and analyze medical data at scale. With AWS HealthLake, you can consolidate health data into a scalable, secure cloud storage that authorized medical staff can access. For example, Cortica, which serves children with autism, uses AWS HealthLake to securely store patients' medical histories, behavioral assessments, and lab reports.
Implement RAG workflows
Generative AI learns from public datasets, allowing the model to respond to questions on broad subjects. However, AI models cannot answer questions about services, products, or information exclusive to an organization unless trained with specific health data. Training a new AI model from scratch requires substantial effort, time, and costs, which some healthcare providers are not prepared for.
Instead, organizations can use retrieval augmented generation (RAG) to achieve similar outcomes. RAG is a technique that allows the AI model to access an organization's knowledge base. When the AI model receives a query, it searches the knowledge base to provide an up-to-date and accurate response.
Amazon Kendra is a highly accurate enterprise search service that allows developers to add search capabilities so end users can discover information from across data sources. Amazon Kendra GenAI Index is a new index in Kendra designed for RAG and intelligent search to help healthcare organizations implement AI models more efficiently. For example, Orion Health uses Amazon Kendra to give customers quick and precise access to health information through conversational queries.
Validate AI output
AI models can produce less accurate answers that seem plausible to the user. Such inaccuracies can affect the clinical care experience and patients' well-being in healthcare. Therefore, when implementing AI healthcare systems, proper safeguards are required. For example, the LLM-as-a-judge approach helps healthcare data scientists analyze and ensure an AI model's response is helpful, correct, complete, and coherent.
Instead of relying solely on AI, healthcare experts should be involved in clinical decision-making. This way, all decisions are vetted by an authorized professional before application in diagnosis, treatment, and other healthcare workflows.
Healthcare organizations can use Amazon Bedrock Guardrails to implement appropriate safeguards in alignment with responsible AI practices. It filters hallucinations from AI responses and helps you build and customize privacy and truthfulness safeguards within a single solution. With an advanced automated reasoning feature, Amazon Bedrock Guardrails can verify and explain to clinicians why the AI model produces a specific response.
How can AWS support your AI in healthcare needs?
From enabling early intervention to reducing clinical workloads, AI healthcare adoption is gaining momentum. AI technologies transform patient care delivery, streamline healthcare workflow, accelerate medical research, and more. Both healthcare workers and patients benefit from the near-limitless potential that generative AI offers. However, healthcare AI implementations must be accompanied by ethical safeguards, data security, and compliance checks.
AWS Generative AI in Healthcare & Life Sciences offers solutions that help healthcare organizations innovate, deploy, and scale AI applications securely to improve patient care experiences.