It has been said enough times but AI is proving to be a game-changer in the healthcare industry. From being just passive participants in the healthcare chain, patients are now taking charge of their health through airtight AI-powered patient monitoring systems, wearable devices, visualized insights of their conditions, and more. From doctors’ and healthcare providers’ perspectives, AI is paving the way for robotic arms, sophisticated analysis and diagnostic modules, assistive surgical bots, predictive wings to detect genetic disorders and concerns, and more.
However, as AI continues to influence healthcare aspects, what’s equally rising is the challenges associated with generating and maintaining data. As you know, an AI module or system can only perform well if it has been trained precisely with relevant and contextual datasets for a prolonged period of time.
In the blog, we will explore the unique challenges experts and healthcare specialists face when the use cases of AI in healthcare keep increasing in terms of their complexities.
1. Challenges in maintaining privacy
Healthcare is a sector where privacy is crucial. From the details that go into the electronic health records of patients and data collected during clinical trials to data that wearable devices for remote patient monitoring transmit, every inch in the healthcare space demands utmost privacy.
If there’s so much privacy involved, how do new AI applications that are deployed in healthcare get trained? Well, in several cases, patients aren’t generally aware that their data is being used for study and research purposes. Regulations mentioned by HIPAA also imply that organizations and healthcare providers can use patient data for healthcare functions and share data and insights with relevant businesses.
There are tons of real-world examples for this. For basic comprehension, understand that Google sturdily maintains a 10-year research understanding with Mayo Clinic and shares limited access to data that is anonymized or de-identified.
While this is quite blatant, several AI-based startups that work on rolling out predictive analytics solutions in the market are generally quite mum about their sources for quality AI training data. This is obviously due to competitive reasons.
Being such a sensitive topic, privacy is something that veterans, experts, and researchers are increasingly keen on an ongoing white hat. There are HIPAA protocols for data de-identification and clauses for re-identification in place. Going forward, we’ll have to work on how seamlessly privacy can be established while simultaneously developing advanced AI solutions.
2. Challenges in eliminating biases & errors
Errors and biases in the healthcare segment could prove lethal to patients and healthcare organizations. Errors stemming from misplaced or misaligned cells, lethargy, or even carelessness could alter the course of medication or diagnosis for patients. A report released by the Pennsylvania Patient Safety Authority revealed that around 775 problems in EHR modules were identified. Out of this, human-bound errors numbered around 54.7% and machine-bound errors numbered close to 45.3%.
Apart from errors, biases are another serious cause that could bring undesirable consequences in healthcare companies. Unlike errors, biases are more difficult to spot or identify because of inherent inclination to certain beliefs and practices.
A classic example of how bias could be bad comes from a report, which shares that algorithms used to detect skin cancer in humans tend to be less accurate on darker skin tones because they were mostly trained to detect symptoms on fair skin tones. Detecting and eliminating biases is crucial and the only way forward for reliable use of AI in healthcare.
High-quality Healthcare/Medical Data for AI & ML Models
3. Challenges in establishing operating standards
Data interoperability is an important word to remember in healthcare. Like you know, healthcare is an ecosystem of diverse elements. You have clinics, diagnostic centers, rehab centers, pharmacies, R&D wings, and more. Often, more than one of these elements requires data to work on their intended purposes. In such cases, data that is collected has to be uniform and standardized in a way that it looks and reads the same no matter who looks at it.
In the absence of standardization, there will be chaos with each element maintaining its own version of the same record. So, whoever looks at a dataset from a fresh perspective is automatically lost and requires the assistance of the concerned authority to understand the contents of the dataset.
To avoid this, standardization has to be made more effective across entities. Meaning, specific formats, conditions, and protocols have to be laid out clearly for mandatory adherence. Only then that data could be seamlessly interoperable.
4. Challenges in maintaining the security
Security is another crucial concern in healthcare. This is what will prove the most expensive when aspects related to data privacy are taken less seriously. Healthcare data is a treasure chest of insights for hackers and exploiters and of late, there have been tons of instances of cybersecurity breaches. Ransomware and other malicious attacks have been carried out around the world.
Even amidst the Covid-19 pandemic, close to 37% of the respondents to a survey shared that they had experienced a ransomware attack. Cybersecurity is key at any given point in time.
Data challenges in healthcare are not just restricted to these. As we comprehend advanced integration and working of AI in healthcare, the challenges only get more intricate, overlapping, and intertwined.
Like always, we would find a way to tackle the challenges and give way for sophisticated AI systems that promise to make healthcare AI more accurate and accessible.