AI in Healthcare – Challenges and Solutions

AI in healthcare!

Artificial intelligence (AI) can play a significant role in the healthcare industry for predicting diseases, identifying diseases and providing 24-hour services. Using patient images and data, AI can assist doctors, nurses, and other healthcare workers in their daily work by producing accurate diagnoses and treatments. For instance, AI can help to find early symptoms in patients with infectious diseases by combatting epidemics and pandemics such as COVID-19.

According to the World Health Organization (WHO), there is a shortfall of 15 million healthcare workers, including doctors, nurses, and midwives. AI can help to meet this issue. Besides that, it is estimated that by 2050, one in four people will be above age 65 with more sophisticated needs means it requires accurate, fast diagnosis AI tools. Additionally, AI preventative care can assist people in staying healthy with better understanding by providing better feedback, guidance and support.

AI Challenges in Healthcare

Artificial Intelligence is a new tool deployed in healthcare; there are many challenges that AI has to overcome to offer its full potential to better health services globally. Some of them are:

Injuries and errors: AI systems may cause patient harm by making wrong predictions of diseases that can impact the patient’s life. For instance, misdiagnosing cancer or wrong drug prescription can take the lives of thousands of patients. Such mistakes could cause substantial monetary and reputation loss to a healthcare provider.

Lack of trust: A study by the Center for the future of work states that 80% of the organisations developing or evaluating AI applications in healthcare struggle to implement AI due to a lack of confidence. Due to the complex nature of AI and lack of clarity on the decision-making process. The adoption of AI in healthcare will remain questionable until the AI trust issues are not resolved.

Lack of explainability: Most advanced AI models’ black box nature made it difficult for health workers and patients to use AI. Explainable AI (XAI) can help patients to understand the process of making decisions along with the limitation that can allow patients and healthcare workers to make an informed decision.

Lack of transparency: Lack of transparency within AI applications causes various biases and risks. According to the Rotterdam School of Management, Erasmus University survey, consumers prefer less use of AI in healthcare applications due to its complexity and black box model. For example, a lack of transparency in AI systems and proper oversight can impact a large population like millions of black people affected by US-based healthcare algorithms preventing them from getting better healthcare access.

Steps to overcome the AI challenges in healthcare

In a study by healthcare information and management systems (HIMSS), 93% of workers in healthcare think healthcare workers’ own biases can be transferred into AI algorithms. Also, even after having accurate representative data, AI still could be biased due to AI system developers’ (conscious or subconscious) bias. Below we list a few ways to overcome AI challenges in health sectors:

• Explainable AI: Artificial Intelligence applications should provide detailed information on the rationale behind their decisions to all AI stakeholders, including patients and healthcare workers. Explainable AI can help achieve this by explaining and justifying the decision. Furthermore, explainability bridges the gap between and mistrust between AI and humans in the healthcare sector.

• Responsible AI: Explainable AI starts is crucial when it is part of responsible AI practices. You can explain a wrong decision, but it doesn’t make it a good one. So, to make good decisions and be able to explain them, organisations should adopt responsible AI.

• High-quality medical data: One of the essential aspects of building a good model is having high-quality data. However, it is challenging to get good-quality data due to its sensitive nature and ethical consideration. Data compliance standards could give confidence to patients to share, change, and erase personal information to overcome this challenge. Also, securing the patient’s information can encourage patients to share their knowledge and contribute to getting good-quality data. 

• AI Fairness: Bias and inequalities pose significant challenges in the health sector, especially to minority and unrepresentative groups. Diversity in data and validation can ensure fairness by representing the dataset and its performance for target populations. Further,  European Commission guidelines for trustworthy AI promote fair and responsible AI in all sectors.

• Awareness about biases in AI: Awareness could play a vital role in identifying and dealing with biases in health sectors. Further, it is aware of the potential source of biases, which reflects historical and social-economic inequalities and ways to deal with such biases.

• Accountability: Track and monitor an AI application from design and development to in-market operations. Organisations should have tools to audit their AI applications at any stage of their lifecycle. Also, organisations should be able to identify the root cause of an issue in human and machine activity (in the context of the AI application).

Regulatory compliance with EU AIA and FDA can address AI’s safety and effectiveness in the health sector. Using AI in healthcare can benefit by identifying and predicting new diseases. The uses of AI in the health sector need to be promoted by using accurate, diverse representative data with fewer errors. The Healthcare sector should use available fairness tools and evaluation metrics to strengthen the role of AI in the medical industry and in building trust in AI. So, AI can provide accurate results to patients and healthcare workers that they can understand, trust and be confident about – creating a fairer and healthier future.

Building a Fair, Explainable and Accountable AI Solution for Healthcare!