Being aware of AI challenges in healthcare can help healthcare providers to build appropriate strategies and quickly implement AI solutions in a risk-free manner
AI is transforming healthcare in many ways. Healthcare organizations are implementing AI for robotic surgery, nursing assistance, accurate diagnosis, and precision medicines. In fact, a survey by ‘KPMG says that 53% of executives believe that healthcare is leading in the adoption of AI. Although leading in the adoption of AI, not all healthcare organizations have implemented AI. Challenges faced while deploying AI solutions are still holding back some healthcare organizations from leveraging the technology to its full potential. In such a scenario, it becomes necessary for healthcare businesses to understand AI challenges in healthcare and their solutions.
Solving AI challenges in healthcare
To solve AI implementation challenges in healthcare, it is essential to be aware of them. Once health organizations are aware of the challenges, they can find ways to overcome them.
Gathering data
AI systems require a massive volume of data. And the collected data has to be from reliable sources. Gathering data from unreliable sources can adversely affect outputs of AI solutions. Hence to get accurate outputs, hospitals must collect training data from reliable sources. They can find reliable data from patients’ historical and current medical records as every patient in healthcare is a source in themself. Healthcare organizations also need to prepare datasets for machine learning algorithms precisely. But data preparation challenges are often hard to overcome. Hence, it comes as no surprise that 96% of organizations are hindered because of data-related issues in achieving AI success. To prepare precise datasets, hospitals need to identify the desired outcome at the earliest and prepare data accordingly. Healthcare organizations also need to make sure that the data is consistent with the processes it is built for. They can make their data compatible by cleansing data to minimize missing values and eliminate irrelevant data.
Maintaining compliance
Every patient is a reliable source of data. But what if these sources deny providing their data to build AI systems? Yes, that is possible. No one wants their data to be exploited for negative means. And to avoid such situations and build trust among patients, governments and leading healthcare organizations create regulatory acts that every hospital has to comply with. For instance, The Healthcare Information Portability and Accountability Act (HIPAA) was passed to mandate standards for the confidential handling of patient data. Another example is The Health Information Technology for Economic and Clinical Health Act (HITECH), which purports to standardize the maintenance of electronic health records (EHR) in today’s digital era. Such regulatory acts enable patients to feel free to share their data, which can be used for training AI systems.
Healthcare organizations also need to make sure that the data collected is secured to enhance privacy and security. But in today’s world, where we often hear news of cybersecurity breaches, securing data isn’t very straightforward. That’s where healthcare organizations can leverage blockchain. The amalgamation of AI and blockchain can together revolutionize several industries. And healthcare is one of these industries. Blockchain will enable secure transmission and storage of patients’ data to enhance privacy and security. It will also provide transparency to patients so that they can view where their data is stored and how it is used.
Identifying use cases
Most businesses can operate with the help of a few machines. But, unlike most companies, healthcare organizations need several tools for diagnosis and treatment. For instance, there are various types of equipment like defibrillators, ventilators, scanners, X-ray machines, and ECG machines used for diagnosing and treating different medical conditions. It may become complex for healthcare organizations to identify the appropriate use case for IoT. Hospitals have to understand the complexity of different machines to determine the correct use case. They also need to consult with their vendors about how easily and quickly an AI solution can be integrated with a specific machine. Health organizations need to select among the AI vendors available wisely. There are various factors to consider before choosing an AI vendor. Factors like generic or vertical solutions, alignment with objectives, and cost-effectiveness can majorly influence AI vendor selection. Identifying the appropriate use case and selecting the correct vendor based on needs will help hospitals to build AI solutions that can be easily integrated with existing equipment and workflows.
Eliminating black box
AI systems are developed to replicate human brains. Hence, just like our brains, they receive inputs and arrive at outputs. But, we cannot know how an AI system arrives at a conclusion; all we know is the final output. And without understanding how an AI system arrived at a conclusion, improving them becomes difficult. This challenge of AI systems is referred to as the black box problem. Solving the black-box AI challenge is essential for almost every industry, but for healthcare, it is of utmost importance. That’s because of the adverse impact it can have on the healthcare industry. Trusting AI solutions blindly can put patients’ lives at risk. For instance, according to IBM’s internal documents reviewed by STAT, IBM’s Watson recommended unsafe treatment procedures for cancer patients. And abiding by the faulty recommended procedure can put cancer patients’ lives at risk. Hence it becomes necessary for the healthcare industry to eliminate the black-box of AI.
But, how do we eliminate the black-box of AI? The answer is “ by using explainable AI.” Explainable AI helps researchers to understand the outputs of AI systems by bringing transparency in these systems. It brings transparency with the help of a post-hoc method, which is developed around four critical components, namely, targets, drivers, explainable family, and estimators. One of the most common methods used to explain AI output is the backpropagation method. Backpropagation is a widely used AI algorithm for supervised training of feedforward neural networks. Implementations of such explainable AI methods will ensure patients’ and doctors’ trust in AI conclusions.
Educating staff and patients
Leveraging AI solutions provides numerous benefits, but using them is complicated. The lack of awareness of the potential of AI and how to leverage it can lead to skill gaps in organizations. And healthcare organizations need to bridge the skill gaps by educating their staff about AI systems and their capabilities. Hospitals and individual experts can organize training sessions for different departments to train staff on how to use AI systems.
AI implementation in healthcare cannot be successful until the patients it will treat are ready to embrace AI-based treatment. Hence, patients also must be aware of AI’s potential so that they can trust AI-based treatment. For instance, robotic surgery offers several benefits like shorter hospital stays, reduced pain, minimal scarring, and lower levels of blood loss. But, due to a lack of awareness and trust, patients might fear being operated upon by AI robots. Healthcare organizations should create awareness about the benefits of robotic surgery among patients. They can also educate patients about AI robotic surgery procedures before operating on them. Educating patients and staff about AI solutions will ensure to increase their trust in AI systems.
Every health organization would want to deploy AI systems. Successful implementation of AI solutions begins right from building the right strategy. But how to create one? It is all about tackling the AI challenges in healthcare mentioned above. The awareness of these challenges and solutions will help healthcare organizations to build appropriate strategies for their specific use cases. And when examples of successful AI implementations come into the limelight, hospitals will be more motivated to deploy and scale their AI solutions.
