AI in Healthcare - A Game-Changer

The healthcare system is on the cusp of a new era. Deep learning is the process by which computer algorithms continuously improve based on the ‘experience’ that they gain. This subsect of artificial intelligence is designed to revolutionize fields which generate massive amounts of data. Machine learning’s ability to optimize the efficiency, interpretation, and storage of data has primed the healthcare sector for a paradigm shift.

The healthcare industry cultivates a tremendous amount of information every day in the form of electronic health records (EHR), X-rays, ECGs, lab reports, and any other patient specific test. The issue arises in the analysis of this data: simply put, there is too much data for humans to handle. This is where machine learning enters the picture. The data generated by the healthcare system can be entered into algorithms which are used to train an AI system to develop its own ‘logic’ and better integrate the vast amount of healthcare related data. By creating a machine learning platform designed to centralize, stratify, and interpret data, medical personnel could reach new levels of understanding in all facets of healthcare. We are seeing this concept in action today: AI has helped healthcare in integrating statistical analyses, fast and accurate diagnoses, and development of critical life-saving applications.

Medical research is not the only realm completely overhauled by machine learning; day-to-day tasks in the clinic are also seeing rapid changes. A centralized machine learning based healthcare system can help streamline repetitive data entry tasks, drastically reduce the number of diagnostic errors, eliminate the mismanagement of prescriptions, assist in the diagnosis of rare diseases, and that is just the beginning of the to-do list. With the rising global burden of disease and increasing patient volume, AI has arrived at the perfect time to relieve healthcare personnel of time-consuming tasks and enable them to more efficiently deliver their services.

Combating Specialist Scarcity – The Collective Radiologist

In recent years, a worldwide shortage of radiologists has caused an increase in the morbidity and the mortality of cancer patients, mainly due to a delay in diagnosis. This has led to an AI radiologist space-race; billions of dollars have been invested in creating a machine learning algorithm on par with a world-class radiologist. As an example, researchers at the Artificial Intelligence Research Centre for Neurological Disorders Tiantan Hospital in Beijing developed BioMind, an AI system which beat a team of top Chinese radiologists in diagnosing brain tumors quickly and accurately. It is true that most applications today are developed for a somewhat controlled environment. In this case, the brain tumor recognition software was designed for a niche diagnosis, intracerebral hematoma expansion, and still needs to cover a lot of ground before BioMind can compete in non-specific scans.

This instance of building an expert medical AI has led to the discussion of combining the skills of man and machine. A hybrid diagnosis, bridged by the intelligence of both a physician and AI, could lead to higher diagnostic precision and an increase in safety standards. AI systems could serve as decision support backups, facilitating diagnoses, and reducing physician burnout.

That said, there are still large obstacles to overcome. The successful implementation of AI in the healthcare system is a massive and slow undertaking. The adaptation of machine learning in medicine needs to be precision tailored as there should be no room for error in healthcare. Regulatory barriers, patient privacy assurance, and data extraction from handwritten EHRs have proven to be cumbersome checkpoints for the implementation of AI platforms in healthcare. Alongside, the method of data storage and access, availability of unbiased health data, and the significant investment needed to startup and run these techniques are also limiting factors. The work to realize of the full potential of AI in medical research, clinical practice and drug discovery is on the horizon and it is imperative that we stay current with these groundbreaking discoveries.