The future of A.I in healthcare: The Possibilities

The future of A.I in healthcare: The Possibilities

Artificial Intelligence is inevitable in healthcare though few might argue that it equally presents risks to patient safety, health equity and data security. With its numerous advantages, A.I in healthcare can succeed if doctors take an active role in its development. Furthermore, not only do old IT infrastructures require overhauling and more interoperability but the quality of health data needs consideration. Additionally, health workforces will need training on its value and the need for accuracy and healthcare organizations.  So, what is the future of A.I in healthcare and what is currently possible?

What is A.I?

Essentially, artificial intelligence (A.I) in the simplest sense describes a discipline of techniques that allow computers to perform tasks that usually to require human reasoning and problem-solving skills. Consequently, sometimes referred to as machine learning, A.I follows rules, algorithms and logic specified and programmed by humans. A.I in healthcare isn’t really a new concept and has been used to develop healthcare software since the 1970s. Though the A.I industry has more recently seen huge technological developments in the field of machine learning and artificial neural networks. Thus increasing the opportunities for A.I in healthcare. Furthermore, the increase in health data has deepened the capabilities of training machines to carry out health tasks.

 How A.I in healthcare works

Ultimately, many fear that A.I may replace human judgment in certain health care functions. Though contrary to belief, it will indeed supplement doctor knowledge bases and assist clinicians in decision-making. In retrospect, the large amounts of health data now available in the 21st century will facilitate the development of more sophisticated mathematical algorithms. These ‘trained’ algorithms could identify disease patterns in bacteria, improve surgical procedures or provide new insights to support clinical practice. With health data a key ingredient, learning algorithms will become more precise and accurate as they interact with training data. In practice, provide unprecedented insight into diagnostics, care processes, treatment variability, and patient outcomes.

The second way A.I in healthcare could work is through ‘Artificial Neural Networks’ are a common type of machine learning. Inspired by animal brain workings, neural networks progressively improve their ability to perform a particular task by considering examples. For instance, early image recognition software was taught to identify images that contain a face simply by analyzing example images that have been manually labelled as ‘face’ or ‘no face’. Generally, over time, with a large enough data set and powerful enough computer, they got better and better at the task, thus independently finding correlations in data.

Benefits of A.I in healthcare.

A.I algorithms could standardise medical assessment and treatment according to up-to-date clinical guidelines. This could raise minimum standards and reduce unwarranted medical errors. Furthermore, Artificial intelligence could improve access to quality healthcare and decision support in rural areas. In essence, providing advice locally and in real-time to patients or clinicians for radiology or telemedicine. Or whilst identifying red flags for medical emergencies like sepsis. Additionally, A.I can help the critically ill, whether on the ward or particularly on the ICU or HDU. Basically, ensuring clinicians are aware of whom to prioritise and make sure they receive optimal and timely treatment. Ideally, AI is rapidly developing and becoming more complex hence the probability of errors and unforeseen consequences. A.I has a priority to first support clinicians, rather than replace clinical judgement.

The future is near

In the past, health care primarily focused on delivering evidence-based care, cure of diseases, and the provision of health products. The current decade is changing from this traditional viewpoint and rapidly moving to results-based care based on real-time data collected from various healthcare platforms and handheld devices.  At the World Medical Innovation Forum (WMIF), leading researchers presented the twelve technologies and areas of the healthcare industry most likely to become more ‘artificial’ over the next few years. This cemented the fact that more health innovators will eventually depend on A.I. In retrospect, Artificial intelligence will be the engine that drives improvements throughout the care continuum. Artificial intelligence will fundamentally change the way we diagnose and treat a disease over the coming years. Furthermore, the co-existence of Big Data, machine learning and natural language will benefit medical research efforts.

Data is king.

Generally, intelligent medical solutions are only achievable with the combination of big data, AI and robotics. Though the foundation is data. Data is collected from a wide range of sources, from Electronic health record systems, to imaging or laboratory test, or notes written at care transitions. But this is only possible if data is securely kept hence why some governments require health organizations to save their clinical data in commercially available clouds. Specifically, neural networks need training on a huge amount of accurate and reliable data. Since inaccurate or misrepresentative data could lead to poorly performing systems. In retrospect, machine learning algorithms sift through terabytes of data to find patterns and correlations to perform tasks.

Examples of A.I in healthcare.

As the world evolves, machine learning is currently applied to automate medical research using ‘text mining’ to analyse trial reports. Furthermore, a company called Human Dx has built an online A.I platform for crowdsourcing advice from thousands of physicians on specific medical cases. Change Healthcare, a healthcare technology company, offers software, analytics and solutions to improve the healthcare system utilising A.I. They specifically include artificial intelligence in multiple products and recently launched the artificial intelligence for the Claims Lifecycle. Additionally, Clarify Health Solutions uses machine learning and an analytics platform to solve many of the complex challenges facing healthcare professionals and to personalize and optimize patient care trips. Lastly, Siemens Healthineers enhances the value of healthcare providers by helping them to grow their precision medicine, transform service delivery and enhance the patient experience with the A.I and digitization of health care.

Conclusion

Lastly, who is responsible for any possible harm due to AI mistakes – the engineers, the biotech company, the regulators or doctors? Furthermore, should doctors have automatic rights to over-rule a machine’s diagnosis? Will the doctor become a second opinion or an interpreter? Additionally, in terms of safety, are machines much better at recognising things like rare diseases? The latter might be true simply because machines work from a bigger dataset, so you could argue that some patients will be significantly safer. Essentially, trust will be one of the most essential steps to the development of AI in healthcare. AI has huge potential to support doctors and enable them to spend more time with patients. However, we must not get carried away or think the AI applications developed so far can replace a fully trained and qualified doctor!

 

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