AI is a growing field that is increasingly making its way into all areas of our lives. And the healthcare industry is no exception.
In an industry on the brink of a major technological revolution (if not already), the integration of AI with existing technology platforms offers the power to bring significant transformation to how healthcare is delivered. Using data to make objective and informed recommendations, and helping ensure that scarce resources are allocated as efficiently as possible, AI has the potential to significantly improve healthcare by enhancing decision-making and patient outcomes.
AI in healthcare can be a game-changer.
A refresher: what are Artificial Intelligence and Machine Learning?
AI (or Artificial Intelligence) refers to the ability of machines to perform tasks that would normally require human intelligence, such as learning, problem-solving, decision-making, and language understanding. It involves creating intelligent algorithms and computer programs that can mimic human cognitive functions.
ML (or Machine Learning) is a subset of AI that involves teaching machines to learn from data and improve their performance over time without being explicitly programmed. ML algorithms can identify patterns in large datasets, make predictions, and take actions based on data input. ML is used in various applications, including natural language processing, image recognition, and predictive analytics.
A key (and timely) example of AI already in action includes ChatGPT – an artificial intelligence trained to assist with a variety of tasks and designed to produce human-like text and converse with people, hence the ‘Chat’ in ChatGPT. This AI platform can help humans with a wide range of tasks, from undertaking detailed research to coding for a new website!
Ways AI can transform healthcare delivery
- Early Diagnosis and Personalised Treatment
One of the biggest advantages of AI in healthcare is early diagnosis and personalised treatment. AI algorithms can detect patterns in data that are too complex for humans to recognise.
A recent article in the National Library of Medicine looked at how artificial intelligence and machine learning can help healthcare systems respond to COVID-19 as a specific example, identifying personalised patient management and treatment plans as positive by-products:
As a result, this means that diseases (including COVID-19), can be detected earlier, and personalised treatments can be provided to patients based on their unique health data, leading to better outcomes and fewer side effects.
When different devices and systems are integrated into a healthcare setting, AI can further enhance the benefits of this connection with additional data and insights.
- Predictive Analytics
Another significant advantage of AI in healthcare is predictive analytics, which can be used to predict disease outbreaks, identify high-risk patients, and improve the accuracy of diagnoses.
This means that healthcare providers can take proactive measures to prevent diseases from spreading and provide timely interventions to those who need them most.
By analysing large amounts of data, AI algorithms can identify trends and patterns that could indicate an outbreak or an individual’s susceptibility to a particular disease.
- Improved Decision-Making
AI can provide healthcare organisations with more accurate and comprehensive data analysis, allowing for improved decision-making. This is a massive advantage, given how many decisions healthcare professionals are required to make on a daily basis!
This comprehensive data can greatly assist healthcare providers to make better-informed decisions about patient care, as well as automating certain tasks such as medication or test alerts, resulting in improved patient outcomes.
When you pair this with real-time data sharing, something we offer here at Olinqua, the opportunities are endless.
- Cost-Effective Healthcare
AI can also reduce healthcare costs. By automating routine tasks and providing more accurate diagnoses, healthcare providers can reduce the need for expensive medical tests and procedures that may be unnecessary. This means patients can receive high-quality care at a lower cost.
And the lowered costs don’t just end with the patient. AI also offers the advantage of managing limited healthcare resources more efficiently, with ML being especially suited for merging various sources of data to issue accurate predictions regarding hospital resources and asset tracking.
- Efficient Patient Monitoring
AI can improve patient monitoring by providing real-time data analysis and alerting healthcare providers to any changes in a patient’s condition. This means that healthcare providers can intervene quickly to prevent complications and improve patient outcomes.
Are there any risks involved?
Like any new (or evolving technology), there are always risks and challenges to be mindful of. The healthcare industry is no exception. What matters, though, is awareness and implementing the right tools to mitigate these risks.
Below, we explore three points:
- Legal and Ethical Issues
The use of AI in healthcare can raise a number of legal and ethical considerations, including concerns regarding privacy and data security. AI algorithms may be used to analyse sensitive patient data, and there is a risk that this information could be used inappropriately or accessed by unauthorised parties.
In addition, there are concerns about the potential for AI to replace human decision-making in healthcare, which could raise ethical questions about the responsibility and accountability of those involved in the decision-making process.
These risks can be mitigated by ensuring data privacy and security, establishing ethical guidelines, involving relevant stakeholders in the development and implementation of AI systems, monitoring and auditing, and establishing clear lines of accountability for the use of AI in healthcare.
- Bias in AI Algorithms
Another risk of AI in healthcare is the potential for bias in AI algorithms. This can occur when the algorithms are trained in biased data or when the algorithms themselves contain biases. For example, an AI algorithm used to predict patient outcomes might be biased against certain groups of patients, such as those from lower socioeconomic backgrounds. This could result in these patients receiving suboptimal care.
To mitigate this risk, it is important to carefully evaluate AI algorithms and the data they are trained on to ensure they are free from bias.
- Lack of Transparency
Another risk of AI in healthcare is the lack of transparency in how algorithms are developed and how they make decisions. This can make it difficult to assess the reliability and accuracy of AI systems and to identify and correct biases or errors in their decision-making.
In order to mitigate this risk, it is important for developers of AI systems to be transparent about how their algorithms are developed, how they make their decisions, and how they can be audited and improved over time.
Yes, AI in healthcare is still in its early stages, but the potential benefits are enormous. The future of healthcare has kicked off.