The Role of AI in Healthcare

With the increasing amount of data generated by the healthcare system, equipping the sector with advanced Artificial intelligence now stands as a strategic imperative.

With time, it has evolved as a game-changer in this field, the field of healthcare, transforming the way medical services are delivered to patients. The result is augmented patient outcomes, accelerated medical discoveries, and faster product-to-market, among others.

In this article, we will delve into the transformative power of AI in healthcare.

What is Artificial intelligence?

AI blends science and engineering that leverages algorithms to help develop highly intelligent machines. These machines execute and automate various tasks by emulating human cognitive functions, for example, problem-solving. Leveraging advanced predictive analytics, AI-powered systems can predict computational problems and help solve them intelligently and adaptively.

AI does this by identifying patterns and correlating multimodal and multidimensional datasets. For instance, in the healthcare system, AI can dig deeper into the entire medical history, records, and data points – symptoms, test results treatments taken, or medical imaging – in a single go and translate them into a single numerical value that helps with disease diagnosis.

Types of AI Relevance to Healthcare

AI is not a single technology; rather, it is a combination of advanced technologies that have a wide range of implications for healthcare. So AI-based technologies widely used to reshape the healthcare sector are:

Machine Learning

Machine learning, in tandem with deep learning and neural networks, has been increasingly used in healthcare. One of the notable applications of this AI-based technology is precision medicine. In precision medicine, conventional ML technology is leveraged to drill down massive volumes of datasets with known outcomes to personalise interventions and anticipate their effectivity based on the patient’s unique characteristics.

Neural networks – one of the most advanced and complex forms of ML-based technology – are widely used by the healthcare sector to categorise and classify tasks such as the likelihood of a patient being infected by a particular disease. 

On the other hand, deep learning comes with multi-layered neural network models to accurately anticipate patient outcomes. Deep learning, and ­radiomic feature extraction are increasingly used in designing cancer-specific predictive modeling. 

Natural Language Processing (NLP)

NLP is widely used to create, interpret and categorise clinical documents in healthcare. It can dig deeper into a massive trove of unstructured patient data, help, etc. 

Robotic Process Automation (RPA) and Physical Robots

RPA is used to streamline and automate a slew of structured administrative tasks in the healthcare sector, for example, billing and prior authorisations.

Next up, one of the most AI and ML-based technologies increasingly used in healthcare is robots. Currently, they are deployed to facilitate surgeries. 

Application of Artificial Intelligence in Healthcare

AI is getting increasingly sophisticated, driving transformative changes in the healthcare industry. Through a range of applications, AI aims at augmenting accuracy, efficiency, accuracy, and personalized care delivery in the healthcare sector. Let’s go through the application and benefits of AI in healthcare. 

Routine Information Collection

Generative AI can streamline repetitive tasks such as reporting and data consolidation. It does so by translating patient data into a single summarised form for healthcare professionals, optimising patient interactions, and troubleshooting any issues faced by the patients and healthcare leaders. The result is augmented patient engagement.

AI-enabled systems can retrieve critical patient records from a range of Health Information Exchanges (HIEs), and dig deeper into them to analyse and ask relevant questions based on the gathered information.

Thus healthcare providers can get tailored insights into a patient’s health status that strengthen their decision-making. This capability of cross-referencing a patient’s current health status with the ongoing treatment helps healthcare providers predict if introducing any new medication would conflict with the existing treatment. 

Disease Detection and Diagnosis

AI is already playing a critical role in helping detect chronic health conditions, for example, malignancy, before the symptoms appear. 

The American Cancer Society reported that around half of the 12.1 million mammograms performed yearly in the USA yield false positives, ending up telling one in two healthy women that they have Malignant Breast Neoplasm (breast cancer). However, using AI-powered software can enable more accurate and 30 times faster translation of mammograms, helping physicians avoid prescribing 20% of unneeded biopsies.

In addition, AI, in tandem with advanced sensors and wearables, can be used to monitor a patient’s health status – cardiovascular, neurological, and pulmonary systems, heart rate, blood pressure, etc. As a result, any potential health issues can be diagnosed at their early and treatable phases.

For cases having adequate data to feed to the system, AI shows immense potential to help with disease diagnosis. 

While diagnosing rare diseases with limited data representation, even AI tools find it challenging to dodge biases and show accurate results – less the data available on diseases, the less accuracy AI can provide, which leads to suboptimal performance.

Even for cases with enough data to drill down, it should be ensured that the AI system a healthcare organisation uses has access to it. Nonetheless, generative AI systems like ChatGPT should be used as a complement to physicians, not as their replacement when it comes to disease diagnosis. 

Treatment

AI not only excels at scanning medical records or helping physicians detect health conditions, it also facilitates disease management. With AI-enabled tools, it becomes effortless for physicians to comply with long-term treatment protocols.

For example, robots have been playing a critical role in the medical sector for the last 30 years, streamlining operations in a wide range of subsectors. From performing common laboratory chores to assisting in complicated surgeries, robots have been used in almost all healthcare processes.

Even though AI promises to help enable more effective treatment processes, a slew of factors – liability and accountability issues, lack of patients’ trust, and practical and technological constraints – make its implementation challenging. 

In addition, to replace the high-risk activities an expert physician performs, AI needs to be equipped with more advanced technologies. Patients often need individualsed treatment management. However, AI’s capability of executing repetitive and low-risk tasks fails to provide personalised treatment.

In short, AI is designed to assist physicians in their activities and augment their decision-making. Abiding by the treatment protocols and regulations ultimately depends on the physicians. 

Population Health Management

HIEs and electronic health records (EHRs) equipped with AI and predictive analytics can enable streamlined population health management. These systems help physicians efficiently specify at-risk patients who need on-time treatments and drugs to evade critical health outcomes. In addition, these high-end systems can be used to design individualised care programs to provide them with tailored services based on the prediction made on their unique health status and medical history.

Post-Treatment Patient Monitoring

The increasing use of advanced wearables and sensors facilitates patient monitoring in remote healthcare settings. AI, when fed with these datasets gathered from these smart devices, can help enable real-time patient monitoring in the post-treatment stage, individualise interventions, and design more customised patient care programs. 

Now that we have a comprehensive knowledge about AI’s application in healthcare, let’s look into some policies healthcare leaders and those who develop AI should abide by.

Policy Recommendation

In today’s dynamic healthcare landscape, AI plays a critical role in augmenting patient care, streamlining healthcare outcomes, and improving treatment management. However, to steer the challenges of AI systems in healthcare to success, everyone involved, including the AI developers, must adhere to the following policy recommendations:

Transparency and Accountability

Enabling the effective integration of AI in healthcare settings requires transparency and uninterrupted collaboration between AI developers and healthcare providers. This synergy can ensure the suggestions an AI system provides are convincing enough, precise, and include minimal error and zero bias.

Informed Consent

Keeping patients well-informed about how AI processes their clinical data is critical. In addition, they should know about AI’s role in their entire treatment process, the privacy policies the healthcare system implements to safeguard their data, and the sources their health data is gathered from to feed into the AI systems.

Data Privacy

Effective use of AI in the healthcare sector requires the stakeholders, physicians, and everyone involved to comply with regulations while consolidating patient data. Currently, policies that regulate patient healthcare data collection include the European General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), etc. 

That being said, penalisation or hefty charges should be imposed for failing to protect a patient’s sensitive healthcare data.

Challenges with the Use of AI in Healthcare

The application of AI can help accelerate effective healthcare delivery. Healthcare providers widely use AI to improve their decision-making and treatment management. However, AI applications in healthcare poses some challenges:

Data Privacy and Security

For effective implementation of AI in health care, ensuring the system has access to adequate patient records is pivotal. However, handing sensitive medical records over to an AI system increases the likelihood of data breaches, making patients more reticent to the technology. 

Bias

The decisions AI technology makes or the treatments it recommends are solely based on the data it’s fed on. In a clinical setting, the administration is liable to create and calibrate AI algorithms that process their records. It implies that any unethical practice or code tempering can result in bias, often leading to social and healthcare inequalities. For example, the underrepresentation of minority groups in clinical studies can cause AI to provide wrong predictions for that particular group and vice versa. 

That being said, for AI to provide unbiased decisions, supplying it with an accurate representation of patient demographics is required. 

Automation

According to the participants in a study, healthcare involving AI could be empathic. AI’s capability of task automation is anticipated to replace up to 35% of jobs in the UK within the next one or two decades. However, AI’s high-end capabilities are expected to complement the expertise of physicians and augment the efficiency of the healthcare sector.

Harnessing The Promise Of AI

Ai demonstrates tremendous potential to transform healthcare. However, proactive collaboration and effort in collaboration, in tandem with advanced AI technology, is critical to harness the benefits of this technology ethically and equitably.

Patsy Todd