We are heading towards a new reality from "Alexa, play Despacito" to "Alexa, what is my diagnosis?".
Did you ask Siri or Alexa to play your favourite songs today? Well, it isn't a far-fetched reality that soon, AI might help your doctor surgically remove a tumor or help you self-diagnose your illness.
Artificial Intelligence has come a very long way indeed, but its future scope in healthcare is still immense. AI redefines and reignites contemporary healthcare through technologies that can anticipate, interpret, learn, and act like humans. Whether it's employed to uncover new linkages between genetic codes or to control surgery-assisting robots, let us do a deep dive into the future of AI in healthcare.
Artificial intelligence (AI) is a broad field of computer science concerned with creating sophisticated computers that can do activities that would generally need human intelligence.
With the onset of COVID 19, we have seen a massive acceleration in the adoption of AI in healthcare. From government to private firms, everyone has employed AI in predicting the onslaught of the infection, results on how the vaccine is performing, and even to decide when it would be a good time to loosen restrictions. Here are the results from the 2021 reports by Grand View Research, and The Global Newswire
1. Covid challenges: Following the pandemic, artificial intelligence in the healthcare industry is projected to evolve a lot. The worldwide health architecture has noticed that computational technologies such as artificial intelligence are essential to build and maintain a sustainable healthcare structure.
2. The push from Research & Development: Many research institutes and governments have actively contributed to developing robust AI technologies that help healthcare professionals operate more efficiently even when resources are scarce.
3. Simultaneous healthcare and AI growth: With both sectors set to almost double their current worth in the next few years, it is no surprise that an effective merger will take place. Healthcare start-ups focusing on AI have been sprouting up recently, and this is a great sign.
Even though all the factors point to rapid future growth in the sector, the adoption rate is still looking very slow. Let’s see what that is:
Though higher in companies and governments, the adoption rate is understandably lower in the everyday person or doctors. We still live in a world where many people do not know how to use smartphones or computers. Even the ones that are well-versed with everyday tech find it difficult to fully accept it as a reliable tool for something as primary as health.
Possible apprehensions and potential solutions
Not all skepticism is terrible. It is important to question things when they deal with something as critical as our health. Let's see an instance.
Consider IBM's Watson for Oncology, an AI-powered supercomputer that aims to transform the management of 12 diseases that account for 80 percent of all cancer cases globally. Watson for Oncology, as per a STAT investigation, has failed to live up to its claims. STAT discovered that three years after IBM started offering this technology, Watson was still having trouble distinguishing between various types of cancer. And physicians outside the US were complaining that treatment recommendations were geared toward American patients.
Even though IBM advertised Watson for Oncology as a cancer-care solution, no scientific articles demonstrated how the technology improved doctors' and patients' experiences. To further add to the patients' skepticism and misunderstanding, the computer couldn't explain why the advised course of therapy was trustworthy because the machine learning algorithms were too sophisticated for the typical user to grasp.
How to resolve it: Being transparent about what a product can and can't do will instill more confidence in a patient or doctor. That way, it would only be applied towards applications that are either speculative or low-risk.
Regulating something so technical would need a whole new framework built from scratch to address this alone.
How to resolve it: The US Food and Drug Administration (FDA) has been gradually updating its regulatory framework to keep up with the fast-evolving digital health sector over the last several years. Select software-as-a-medical-device (SaMD) developers have also been enrolled in the FDA's Digital Health Software Precertification (Pre-Cert) Pilot Program. The Pre-Cert pilot aims to assist the FDA in determining the essential criteria and performance parameters necessary for product precertification. It also wants to explore methods to simplify the approval process for programmers and accelerate health care innovation.
Data accessibility is critical for the development and usage of AI systems. As a result, data interoperability is one of AI's most pressing issues in healthcare.
How to resolve it: Some initiatives, such as the effort to create a European Electronic Health Record interchange standard for EU members, are encouraging, but much more has to be done. Some other data-related problems, such as who owns hospital patient databases, are just as significant. To pave the path for an AI-based future, database owners should be well-versed in data's possibilities, not simply its conventional applications, to foster more innovation.
This essentially means that an AI can simply come to a conclusion after looking at specific data and not justify its credibility. Let's say there is a breast tissue found in a mammogram, and the AI diagnoses it as malignant. It is difficult for an oncologist to understand how it came to this conclusion because doctors and physicians cannot understand the complexity of machine learning algorithms. This also feeds into their mistrust of the technology itself.
How to resolve it: While attempts to unlock the "black box" are ongoing, AI's most useful function in the clinical environment during this early stage of adoption may be to assist doctors in decision-making, rather than to replace them. Most doctors may not trust a black box. But if they are the final arbitrator, they will utilize it as a support system.
Machine learning (ML) is a form of artificial intelligence (AI) that allows software programs to improve their prediction accuracy without being expressly designed to do so. To anticipate new output values, machine learning algorithms use past data as input. Machine Learning (ML) has been helping in a variety of healthcare scenarios. Currently, it assists the evaluation of hundreds of different data points and predicts outcomes, and gives immediate risk scores and accurate resource allocation, among other things.
It is feasible to create data-driven predictions in a matter of seconds using modern algorithms, IT systems, and data, all without the need for human participation. Predictive analytics examines a large amount of data and analyses it to anticipate individual outcomes using statistical technologies and methods. In healthcare, these projections might range from hospital readmission rates to drug reactions, among other things. It can determine the likelihood of sickness, forecast infections, calculate future health, and so on. Predictive analytics in healthcare can reveal dangerous medical problems ahead of time when historical data and real-time data are combined.
NLP shows how artificial intelligence algorithms acquire and evaluate unstructured input from human language to identify patterns, understand the meaning, and create feedback. This aids the healthcare business in making the best possible use of unstructured data.
It essentially has two use-cases:
1. Understanding human (doctor/patient) speech patterns and deciphering them.
2. Making sense out of unstructured data in data systems and records. They do this by mapping out this data in a digestible form for physicians to perform data analytics and aid in decision-making.
For ease of understanding and organization, let's divide this into two categories:
Everything that falls outside the purview of a hospital is non-clinical. For instance:
With the help of IoT and context-awareness, you can now send sensor data to a remote server, aka, to your doctor. This can manifest in various products or services:
Doctors, nurses, and other clinical personnel can use wearable gadgets to keep track of patients, whether they're in the hospital or at home. Hospitals can free up beds by remotely measuring patients' physiology in real-time. Clinicians can keep an eye on patients' vitals regardless of geographic location, improving overall treatment efficiency.
The identification of eye disorders is being automated using machine learning. When patients with diabetes see their primary care physician, they are commonly referred to an ophthalmologist, who can examine their eyes for symptoms of diabetic retinopathy. The illness affects the retina, a light-sensitive tissue at the back of the eye. It is a significant cause of visual impairment in adults in the United States, with up to 25,000 cases reported each year. However, if the condition is detected before symptoms occur, it can typically be treated, and the worst-case scenario avoided. "We know how to treat it, but we don't catch it early enough," says one expert.
With all this data at our fingertips, it only makes sense to automate some aspects of medical conversations. Today doctors and patients can access this repository of data through chatbots and hospital quality reporting programs.
Today, virtual assistants (like Siri or Alexa) help you solve some niche healthcare problems. For instance, Digital John Kirwan (or DJK) can be found via the Mentemia app, where he may be reached in only a few taps. His expertise assists people in getting a better night's sleep, which is very important for our mental and physical health.
DJK may assist users in creating a sleep-improvement plan, as well as provide advice on how to obtain a better night's sleep and answer a variety of questions about their sleeping habits. DJK's assistance is supported by science since he draws on thousands of information sources and the clinical and psychological expertise of the in-app physicians and specialists.
In another example, we have a Cardiac Coach. Cardiac Coach is being developed in collaboration with the Centre for Digital Business to give 24/7 assistance to those recuperating from cardiac problems. Patients may ask questions about their health, medicine, food, therapy, and more in a natural dialogue with the Coach at any time.
Given the burden on physicians, nurses, and support staff worldwide, UneeQ's Cardiac Coach relieves some of their daily responsibilities, allowing them to focus on higher-priority medical issues.
IBM CareDiscovery® Quality Measures is an essential measure reporting tool utilized by many hospitals in the United States to meet CMS compliance and core measure reporting obligations. It has a track record of submitting correct, on-time data, making it a leader in the CMS Hospital Inpatient Quality Reporting Program (HIQRP) or Joint Commission certification (TJC).
With AI, we are now exiting from the mass pay-per-charge consultations and moving into a more value-added service with multiple follow-ups and check-ups along the treatment trajectory. This means there are much more complicated payment structures in place to make that happen. AI helps the service providers connect with the payer by simplifying the payment gateways and estimating a rough expense to the payer before they avail of the service.
Administrative process automation helps hospital staff, physicians, nurses, and assistants save time on mundane activities while prioritizing fundamental problems.
One example of Clinical Admin workflow assistance is Olive (An Ohio-based start-up). Olive's AI platform is meant to automate the most time-consuming operations in the healthcare business, allowing administrators to focus on higher-level duties. Everything, including eligibility checks, unadjudicated claims, and data transfers, is automated by the platform. This will enable workers to focus on providing a superior patient experience.
Olive's AI-as-a-Service connects seamlessly with a hospital's existing software and tools, avoiding costly integrations and outages.
Precision medicine techniques discover patient phenotypes with less common treatment responses or unique healthcare demands. AI uses advanced computing and inference to create insights, allowing the system to think and learn while empowering physician decision-making.
Information acquired from sequencing the human genome has sparked a revolution in health care. Since then, the discipline has understood how the confluence of multi-omic data, medical records, social/behavioral factors, and environmental information accurately describes patient states, disease states, and treatment choices for impacted persons.
Medical Imaging & Image Analysis
Artificial intelligence will shorten the time it takes to detect and respond to aberrant medical images. This is especially essential in imaging the chest and brain, where time is of the essence. According to GE Healthcare, medical imaging accounts for over 90% of healthcare data, and over 97 percent of medical pictures are not analyzed.
Proscia is a digital pathology platform that detects patterns in cancer cells using artificial intelligence. The firm's software helps pathology laboratories minimize data management roadblocks and connects data points that assist cancer diagnosis and therapy using AI-powered image analysis.
It checks symptoms with artificial intelligence algorithms and querying and then directs them to the right medical provider in three easy steps:
In the fall of 2017, researchers at Maastricht University Medical Centre in the Netherlands utilized an AI-assisted surgical robot to repair tiny blood veins with diameters of up to.08 millimeters. While this technology is still in its infancy, it can help surgeons achieve better results.
Understandably, automation is one of AI's most significant benefits as it shifts the non-intuitive aspects of treatment management to sophisticated algorithms that improve over time.
With Deep Learning, we see a much better application of AI as we have a more extensive database to work with. As a result, big hospitals that handle massive patient records can put that data to work and help with their decision-making.
One of the best things about employing a good ML architecture across the board is understanding what the existing data says about the future outcomes of the line of treatments.
Being able to assess one's health through technology reduces the workload of doctors. It also helps them treat you better by having access to your vitals from anywhere in the world through wearable devices.
AI is helping make healthcare more accessible to the reluctant and marginal groups of society. By tying a doctor's visit with a smartphone, we connect the world with top-notch healthcare solutions because smartphones are one of the most accessible pieces of modern technology.
A virtual assistant wouldn't cost you as much as a physical doctor visit. It will also take less time because you wouldn't have to travel to a doctor. Moreover, with AI-assisted treatments, doctors can save a lot of time in cases where time is crucial.
Wherever there is manual work, there is the scope of automation (or at least AI-assisted operations.) With automation, we reduce the costs, increase the accuracy, and move the manual workforce to a place where they are better utilized. This will also ensure that manual labour gets more valuable with time.
Innovation is intersectional. For instance, IoT needs to be integrated with deep learning and context-awareness for it to be genuinely fool-proof. Similarly, NLP is used in Chatbots and virtual health assistants. Eventually, these isolated technologies will have to work together to amplify each other's effectiveness and fill in the shortcomings.
Disease management has not yet been automated or optimized to its full potential. Once we get the current AI to a stage where professionals are comfortable relying on it to an extent, we have the cat in the bag. This would be the first milestone in smoothening out the standard process of disease management.
Medicine trials would become much more straightforward by identifying and automated assessing the candidates based on specific performance indicators.
New medication applications can be identified using AI algorithms, which can then be traced back to their hazardous potential and mechanisms of action. With this technique, a firm can build a drug discovery platform to repurpose existing medicines and bioactive molecules.
To find more about the latest trends in AI in healthcare, you can read it here
Health is a high-risk thing that cannot be played with. Hence legislation must catch up with technologies to regulate the standard for AI solutions in healthcare. This ensures the safety of the patient and encourages the familiar person to participate in this. As we get more people into the loop, we will soon have to deal with a lot of data.
This will pose new privacy concerns. As healthcare data is susceptible, it needs the best that cybersecurity has to offer. A simple firewall and encryption with a hash code wouldn't cut it anymore. Getting these creases straightened would be the first step in the acceptance of AI in medical care.
AI is a black box. Hence, unless we solve that, we wouldn't be able to identify the shortcomings in the algorithm. This can lead to uncertain outcomes, as discussed earlier.
Moreover, the cost of setting up a new administrative/healthcare software would need a lot of money and, potentially, some downtime. Even integration of this software into the existing system is very complex and time-consuming.
Many machine learning research papers are proof-of-concept papers showing what can be done in an idealized and restricted setting. This means that effective implementation of the tech is a significant challenge.
The lack of confidence in the findings given by an ML system and the necessity to satisfy strict criteria continue to be barriers to AI adoption in healthcare. The preliminary application of AI in healthcare, on the other hand, has already provided several benefits to healthcare stakeholders.
Patients, funders, researchers, and healthcare professionals can all benefit from the use of AI in healthcare by improving workflows and processes, assisting medical and nonmedical staff with routine work, assisting users in finding faster answers to questions, and creating innovative treatments.