Artificial intelligence is finding widespread acceptance in the healthcare sector. Research projects based on AI in medicine surpassed the investment in AI projects within any other industry of the global economy in 2016.
The rising accumulation of healthcare data coupled with the availability of powerful computing tools has laid the perfect foundation of application of AI in healthcare.
AI algorithms powered on machine learning and natural language processing capabilities is skyrocketing applicability and implementation.
However, as the current applications of AI in health continue to soar, the rising privacy and security concerns raise red flags too. While there is massive excitement for the scope and future applications of artificial intelligence in medicine, there is also rising skepticism at the inflated expectations from the same.
In this piece, we’ll be discussing the current applications of AI in healthcare and what the future may hold.
Introduction to Artificial Intelligence in healthcare
Artificial intelligence refers to a set of software algorithms that allow the computer to perform tasks that require human intelligence and cognitive abilities. This includes speech recognition, visual perception, and analysis of data for making intelligent decisions.
Artificial intelligence and machine learning algorithms in medicine have the ability to extract information and clinical insights from the structured and unstructured data. The structured data may be in the form of diagnostic images, lab reports, genetic data and physiological data recorded by connected devices. The unstructured data includes the clinical notes and other literature.
Natural language processing converts the unstructured data into structured data which can be further processed with ML algorithms to assist in clinical decision making. NLP also forms the basis for voice-based or textual conversations with software in the form of voice assistants and chatbots.
AI algorithms provide techniques for uncovering complex associations and deducing solutions based on the input data in a manner similar to the approach of a clinician. They have the ability to weigh the evidence data and reach conclusive insights.
However, unlike humans, AI algorithms have the ability to process multiple inputs simultaneously, thus saving significantly on time and effort involved.
Applications of AI in the field of medicine
From chatbots that patients can converse with to voice assistants that assist in logging the clinical notes, from radiological and pathological image analysis for quicker diagnosis to self service robots streamlining operations, the applications of AI in the clinical settings are multi-fold.
Incorporating AI and ML techniques into healthcare promises to:
- Improve the clinical efficiency and quality of care
- Enhance patient engagement in their treatment and streamline patient access to care
- Accelerate the timeline and bring down the cost involved to develop new pharmaceutical treatments
- Gain macro-level disease insights by processing huge amounts of data simultaneously
- Personalize medical treatments by leveraging analytics to mine significant, previously untapped stores of non-codified clinical data
Artificial intelligence in healthcare can be broadly classified into three distinct categories.
- Patient oriented AI
- Clinician oriented AI
- Administrative or operational oriented AI
Application of AI algorithms in the field of healthcare can help uncover a treasure trove of information buried under heaps of data. Here are the promising applications of AI in the field of healthcare delivery
1. Virtual assistants and chatbots
AI is boosting the level of care that patients are receiving. Conversational healthcare chatbots deliver stellar support during the treatment and boost the levels of patient engagement.
With 24×7 support and responses to patient queries made possible as a result of technology, chatbots like Florence and ADA are taking patient care to the next level.
AI powered apps that are capable of processing data from their large database are becoming the first stop for patients who want to get an initial consultation.
Based on patient interactions, the chatbots in applications like your.md and buoy health give you the next actionable steps which may include setting up an appointment with the clinician.
Patients with Alzheimer are using voice assistants like Alexa for setting reminders for themselves and regaining control over their life and routine.
2. Computer aided detection and diagnosis
By analyzing image data studying how clinical abnormalities appear on imaging studies and correlating image data and associated clinical information, AI algorithms are making a breakthrough in the radiological and histopathological diagnosis.
Training the algorithms on a sufficiently large datasets of radiographs, CT scans and MRI’s has resulted in AI being successful in giving differential diagnosis in case of diseases like lung cancer and breast cancer.
Computer aided detection (CAD) is also finding its applicability in dermatology where pictures of skin lesions are used to determine the risk of dermatological lesions of turning malignant.
Computer aided diagnosis is beneficial for the providers who are able to utilize technology as an initial screener for diseases, saving precious time and efforts.
AI for diagnosis is also beneficial for the patients as it helps increase efficiency and lowers wait times. Patients identified as low risk receive instant reassurance without having to wait longer for diagnosis while high risk patients would experience lower referral waiting times because clinics would only be receiving selected cases.
3. Patient self service check-in
Patient self-service can help streamline provider operations. By routing patients straight to an appointment or directing them to visit the registration desk, based upon predefined rules, technology can allow the registration team to focus on a select group of patients who have a greater need for their value-added services.
The administrative and operational benefits of patient self-service using ML and NLP in the hospital operations. Self-service check-in has resulted in significant reduction in traffic at the front desk and bringing down the wait times associated at check-ins.
4. Comprehensive patient profiles
With healthcare data revolution heralded by the growth in connected devices, the amount of data being generated is growing exponentially.
AI is proving instrumental in collating insights from this data and drawing actionable conclusions regarding the overall health at an individual level. By giving patients control over their data, AI also boosts patient engagement levels.
Along with clinical notes and data from medical and remote patient monitoring devices, a significant amount of health data is unstructured. Data like that in paper-based notes can be converted into structured data using natural language processing.
The health researchers can dive into this treasure trove of data and take medicine to the next level with the help of AI and ML algorithms, conduct risk analysis, predict disease patterns and predilections in the general population.
5. Reduced drug development timelines
A lot of time, money and efforts go into conducting clinical drug trials. From patient selection to retention and from ensuring compliance to the drug regimen to accelerated analysis of data, artificial intelligence is a huge asset to the pharma industry.
The role of AI in medicine is evident right now as the world battles against coronavirus. Insilico Medicine used its AI system to identify thousands of molecules for potential medications for COVID19 in just four days. Technology is also factoring in as we race towards a vaccine in the near future.
AI is streamlining the process of drug discovery while bringing down the costs associated with it. By allowing for faster analysis of huge amounts of data through predictive analytics and machine learning, AI is bringing down drug development timelines and accelerating the process.
Future of AI in medicine
Integrating these systems into clinical practice necessitates building a mutually beneficial relationship between AI and clinicians, where AI offers clinicians greater efficiency or cost-effectiveness and clinicians offer AI the essential clinical exposure it needs to learn complex clinical case management.
Medical professionals must also prioritize patient privacy and security when considering AI applications. Despite the growing presence of AI in healthcare, the practitioner-patient relationship still forms the foundation of a successful healthcare practice.
At the end of the day, AI will not be a replacement for healthcare practitioners but will gradually evolve into the perfect assistive technology that would enhance healthcare efficiency.
By automating routine tasks that do not require a high degree of specialization, artificial intelligence will allow medical professionals to focus their efforts on areas which require their immediate attention.