Artificial intelligence (AI) and machine learning (ML) are currently experiencing significant global hype, especially since the launch of generative AI models like ChatGPT in 2022. As this hype grows, ethical considerations have come to the forefront, particularly in the medical domain where human health and well-being are paramount.
Despite the existence of nine generative AI models in healthcare and 24 distinct applications of AI, such as providing health-related insights and aiding in disease diagnosis and prediction, key challenges remain. These include the generation of inaccurate or fictional content, uncertainty regarding information sources, and decreased accuracy in answering queries.
According to McKinsey, artificial intelligence, traditional machine learning, and deep learning could result in net savings of $200 billion to $360 billion in healthcare spending. However, this potential can only be realized if trust is built among healthcare professionals and patients, and if AI is used accurately and intelligently.
AI and machine learning applications offer numerous benefits, not only financial. They also contribute to better healthcare quality, increased access to care, and greater satisfaction for both patients and doctors.
Applications of Machine Learning in Healthcare industry
Here are the applications of Machine Learning (ML) as a subtype of Artificial Intelligence (AI) gaining popularity in healthcare:
- Accurately collect patient history and provide family history
- Improve healthcare services and operational efficiency
- Enhance the treatment process and diagnostic accuracy
- Enable robotic surgeries and image-guided therapies
- Automate messaging warnings
- Detect diabetes and other health problems
- Support timely decision-making
- Handle online appointment scheduling and healthcare informatics
- Analyze patient data and improve clinical studies
- Facilitate early-stage medication development and clinical trials
- Assist with psychological difficulties, patient diagnosis, and therapy
- Advance medicine discovery and radiology
- Develop novel medical procedures
- Forecast diseases and detect minute defects
- Aid in treating blood cancer
- Enhance healthcare systems and quality
- Manage risks effectively
The application of machine learning (ML) improves the organizational aspects of the industry by streamlining processes like claims processing and revenue cycle management. It also has the potential to automate clinical documentation and records administration. Corporate use cases for ML are increasingly making headlines, highlighting its vast potential and advancements in healthcare.
The Importance of Back Office and Front Office in the Healthcare Industry
As patient access to medicine and their satisfaction with medical services are crucial in healthcare, many organizations prioritize investing in technologies that can improve these areas. Considering that AI, machine learning, and generative AI technologies have high investment potential, we can envision how the combination and integration of these technologies will be promising in the coming years.
According to the McKinsey report "Digital Transformation: Health Systems’ Investment Priorities," virtual health and digital front doors are the top investment areas for 70 percent of healthcare executives, who anticipate these areas will have the greatest impact. Additionally, 88 percent of respondents recognize a high potential impact of AI.
Importance of AI and Machine Learning in the Digital Front Door
Effective communication between doctors and patients is crucial. As the WHO states, "while technology and innovations can enhance health service capabilities, human interaction remains a key element of patients’ well-being." It is essential for medical professionals and organizations to maintain a human touch even as they leverage technological advancements.
Here are some areas where AI and machine learning can be implemented in the digital front door of healthcare:
- Virtual Health Assistants
- Telemedicine Platforms
- Appointment Scheduling and Management
- Patient Triage and Symptom Checkers
- Personalized Health Recommendations
- Remote Patient Monitoring
- Patient Engagement and Education
- Predictive Analytics for Patient Outcomes
- Automated Documentation and Workflow Management
In the digital realm, maintaining human interaction while leveraging technology is crucial. Since calls between doctors and patients are a key component of the digital front door in healthcare, we at JetBase have implemented AI to bridge the gap between them, saving time and increasing satisfaction. We would like to discuss our AI implementation use case in more detail with you.
Our integration goes beyond just doctor-patient interactions; it also includes workflow and data management. We are continuously implementing new features from AI providers. Continue reading to learn how AI and machine learning can be applied in real healthcare scenarios.
Telemedicine and AI use case
In our healthcare development project, which leverages data from medical devices, patients and doctors can engage in calls that can range from just a few minutes to several hours. For patients, the experience remains seamless — doctors are attentive and available for consultation.
However, for medical professionals, the benefits are substantial. With numerous patients and interactions to manage, it can be challenging to keep track of all the details. This is where AI technologies come to the rescue. By analyzing key points from each interaction, AI helps reduce data overload and streamline information management. To illustrate how this works, we’ll provide examples from our healthcare case study.
Use Case: Implementation of AI and Automation Services by AWS
In our healthcare development project, we have integrated AWS AI services. This integration has enabled us to gain insights from data and automate the daily routines of medical practitioners.
Amazon Bedrock
Amazon Bedrock provides access to a wide range of foundation models (FMs) from leading AI startups and Amazon through a unified API. It ensures total security and privacy while facilitating easy experimentation and evaluation of top models. With features like fine-tuning, Retrieval Augmented Generation (RAG), and the ability to create agents that interact with your enterprise systems, Amazon Bedrock supports customization and efficient data processing.
We have been using Amazon Bedrock for the past 2-3 months to generate prompts from medical device data. Doctors act as agents in this AI ecosystem, applying specific parameters to these prompts. The processed prompts are sent back to the service, which returns ready-to-use commentary that doctors can edit if needed.
Amazon Transcribe
Amazon Transcribe is an automatic speech recognition service that converts audio to text using machine learning. It can be used independently or integrated into applications for speech-to-text capabilities. It offers language customization for improved accuracy, content filtering for privacy, multi-channel audio analysis, and speaker partitioning. You can transcribe media in real-time (streaming) or from files stored in an Amazon S3 bucket (batch).
For the past 6 months, we have utilized the batch transcription option. We also evaluate conversations using three categories: satisfied, not satisfied, and neutral. We have added an option to visualize these evaluations with emojis for better user insight.
AWS HealthScribe
AWS HealthScribe is a HIPAA-eligible service that integrates generative AI capabilities without requiring management of the underlying ML infrastructure or training of healthcare-specific large language models (LLMs). It assists in recognizing medical speech for automating preliminary clinical documentation. Using a single API, AWS HealthScribe identifies speaker roles, classifies dialogues, extracts medical terms, and generates detailed preliminary clinical transcripts and notes, expediting implementation by eliminating the need for separate AI services. This service was launched in early 2024, and we are currently transitioning to it after several months of using Amazon Transcribe.
Amazon Forecast
Amazon Forecast is another AWS service that aids in intelligence-driven decisions using machine learning (ML). It simplifies the use of various data levels for business metrics analysis.
We are still in the process of implementing Amazon Forecast into our business processes within our healthcare application and will provide updates on our progress as we advance.
What You Need for AI and Machine Learning Implementation in Healthcare
You might wonder what is required to implement AI services in your healthcare application using AWS. The advantage of AWS is that it offers a team of experts and AI partners. From our perspective, we needed only clear data and an AI expert to navigate the implementation process and address specific challenges.
Clear Data
In our AI use case, we currently manage 20-30 million data points from medical device readings. This data allows us to train algorithms and employ predictive analytics. One of our goals is to use this data to forecast revenues for clinics and other healthcare entities involved in the project, as well as to enhance patient engagement. While data filters can help purify input data, working with large datasets still requires substantial time and effort from human resources.
There is no such thing as too much data in machine learning. What’s more important is to have clear, well-organized data.
AI Experts and Human Involvement
Selecting and preparing data for machine learning is a time-consuming task that requires dedicated human effort. You need a team comprising an AI engineer, doctors, and medical scientists to filter data, test algorithms, and label data systematically.
To Sum Up
AI and machine learning in healthcare are promising fields that hold potential for significant advancements from both AI engineers and medical experts, as well as substantial investments. We continue to learn more about AI's capabilities and technological innovations in medicine, which can enhance medical workflows, improve patient experiences, and expand access to healthcare globally.
At JetBase, we currently rely on AWS services for our healthcare project, eliminating the need to develop our own AI solutions and machine learning ecosystem. These services have already proven valuable for transcribing doctor-patient calls and creating prompts from extensive medical device data. However, we remain open to deepening our project based on its goals and client needs. We are committed to implementing any innovative technological ideas to benefit both doctors and patients.
We hope our insights into AI and machine learning in healthcare have inspired you and provided new ideas for your own use case. If you need expertise in AWS services and healthcare industry web development, feel free to contact JetBase for a free consultation.
