AI in Healthcare: Opportunities and Challenges

Key opportunities while implementing AI in healthcare

1. Medical research

Medical research has always been crucial, and the application of AI in this field has gained significant attention in recent years, enhancing research opportunities. For instance, IBM’s Watson Health branch heavily relies on AI to offer advanced solutions, including drug discovery, clinical trials, and patient care. Another New York-based startup, AiCure , utilizes AI and advanced data analytics to monitor patient behavior and facilitate remote patient engagement in clinical trials, utilizing video, audio, and behavioral data to enhance the understanding of the connection between patients, diseases, and treatments.

2. Drug Discovery And Development

The Drug Discovery and Development stage is a crucial and costly process. It involves identifying novel drugs, designing them, refining them, and testing them. AI plays a vital role in enhancing and expediting the discovery and development of drugs by analyzing drug efficacy and potential side effects, as well as conducting gene sequencing.

3. Patient engagement

Patients can greatly benefit from proper guidance and personalized care, and incorporating AI technology can enhance patient engagement even further. There are numerous ways in which AI can improve patient engagement, with one of the most crucial being medication adherence. Patients may frequently forget or discontinue taking their prescribed medication, but AI-generated timely reminders can assist them in their journey to improved health. Studies have demonstrated that using the AiCure application can increase medical adherence by 17%.

Livongo , a mission-driven organization based in the United States, has successfully revolutionized how people access and experience healthcare by utilizing AI technology to provide coaching and support for patients. This coaching is highly effective in improving patient outcomes, with hospitalizations reduced by 58%. In addition to medication adherence, AI can be leveraged to educate patients, manage chronic conditions, and provide remote patient monitoring, all of which contribute to improving patients’ quality of life.

4. Streamlining Administrative Tasks

In 2017, the healthcare industry’s administrative expenses amounted to $812 billion, according to a study by the Center for American Progress. The McKinsey Global Institute also found that administrative tasks account for up to 36% of a physician’s workday, leading to burnout. To alleviate this burden, AI can automate tasks and reduce administrative expenses, improving job satisfaction for healthcare professionals.

AI can be used to streamline various administrative tasks such as appointment scheduling, medical coding, billing, authorization, and clinical documentation. For example, Zocdoc found that 50% of Americans struggle to understand insurance, and by utilizing AI, they were able to help over 6 million patients schedule appointments with healthcare providers. Serkan Kutan, CTO of Zocdoc said, , “As a consumer-facing tech company operating in healthcare, we are eager to bring data-driven innovations to improve the patient experience. Our search process uses multiple algorithms to parse a patient’s intent and match their needs to the right specialist.”

Overall, by leveraging AI-powered platforms, healthcare providers can reduce administrative expenses, streamline tasks, and improve the overall efficiency of healthcare delivery, resulting in better job satisfaction for healthcare professionals and better outcomes for patients.

5. Robotics in healthcare

Performing surgeries is undoubtedly one of the most critical tasks, as it gives patients a chance at a healthy life. However, surgical procedures can sometimes fail due to various reasons. Leveraging AI technology can transform the field by increasing surgical precision, improving patient outcomes, reducing complications, and enhancing efficiency.

According to a CNN report, , Singapore’s Changi General Hospital has developed over 50 robots to assist its staff. Hospital officials believe that robotics has the potential to make people’s lives more meaningful by eliminating the need for them to perform degrading, demeaning, dirty, dangerous, or driving tasks – the five Ds.

Key challenges while implementing AI in healthcare

1. Data Privacy And Security

Healthcare data is particularly sensitive and the implementation of AI in healthcare can pose risks to data privacy and security. Improper protection of healthcare data can result in data breaches and misuse. Furthermore, a lack of transparency can lead to ethical concerns.

To address these issues, the UK has implemented the General Data Protection Regulation (GDPR), also known as The Data Protection Act 2018, which regulates the use of personal information by organizations. In 2019, the Royal Free Hospital in London breached this regulation when it gave the personal data of which regulates the use of personal information by organizations. In 2019, the Royal Free Hospital in London breached this regulation when it gave the personal data of 16.6 million patients to DeepMind, a British artificial intelligence research laboratory.

To mitigate these risks, hospitals must implement strict policies and robust data protection measures. Regular security audits should be conducted, and patients should be kept informed about how their data is being used.

2. Regulatory compliance

Organizations that implement AI in healthcare must comply with various regulatory compliance requirements that govern data sharing, processing, storage, and collection. These requirements include HIPAA Compliance , GDPR, , FDA approval , and Ethical considerations. Non-compliance can lead to strict action being taken against the organization. To ensure compliance, organizations must undertake strict privacy measures and closely monitor their actions.

For instance, in 2020, CHSPSC LLC, a subsidiary of Community Health Systems, was fined fined $2.3 million by the US Department of Health and Human Services (HHS) for violating HIPAA regulations. They failed to comply with security measures, resulting in a data breach affecting six million patients.

Therefore, organizations must adhere to regulatory compliance requirements when implementing AI in healthcare. They must take measures to ensure compliance, such as implementing strict privacy measures and regularly monitoring their actions to avoid penalties and protect patient data.

3. Cost and resource constraints

Although AI has the potential to revolutionize the healthcare sector, it comes at a cost that varies depending on factors such as the algorithm to be implemented, the amount of data to be processed, the scope and size of the project, and the level of intelligence required. According to Analytics Insight, custom AI implementation can cost an organization between US$20,000 to US$1,000,000.

Additionally, building AI solutions requires sophisticated hardware, software, and algorithms, which can add to the overall cost of implementation. Another challenge is the shortage of skilled professionals to manage and deploy these solutions effectively.

4. Clinically inapplicable performance metrics

There is a critical stage between ideation and fruition where many projects fail, known as the “AI chasm,” where development falls short. While we have made significant progress in AI, researchers suggest that there is still room for improvement. Studies indicate that AI models for health prediction may not have sufficient evidence to show improvements in patient outcomes.

One of the major challenges of implementing AI in healthcare is the use of performance metrics that are not relevant in a clinical context. AI models are trained using metrics such as accuracy, precision, recall, and F1 score, which may not have clinical applicability. For example, if an AI model has high accuracy in predicting a medical condition but is unable to explain how it arrived at the prediction, it may not be useful in clinical decision-making.

Another challenge is the lack of representation of the patient population in some AI models, which can lead to disparities in healthcare outcomes. Additionally, some AI models lack transparency and interpretability, making it difficult for clinicians and researchers to trust the results. To address these challenges, it is necessary to thoroughly evaluate the performance of AI models in a clinical context and ensure that they are transparent, interpretable, and representative of the patient population.

Read more: : https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1426-2