According to Frost & Sullivan, AI systems are projected to be a $6 billion dollar industry by 2021[1]. Integrating AI into the healthcare ecosystem allows for a multitude of benefits, including automating tasks and analyzing big patient data sets to deliver better healthcare faster, and at a lower cost.  Current technological applications of AI in healthcare are listed below:

  • Medical diagnostics: the use of Artificial Intelligence to diagnose patients with specific diseases. Also, a report AI platform was announced in March 2019 which is expected to help identify and anticipate cancer development.
  • Drug discovery: There are dozens of health and pharma companies currently leveraging Artificial Intelligence to help with drug discovery and improve the lengthy timelines and processes tied to discovering and taking drugs all the way to market.
  • Clinical Trials: Automation is known to produce better insights into certain clinical matters, and certainly a lot faster than through any human-related process.
  • Pain management: by leveraging virtual reality combined with artificial intelligence, we can create simulated realities that can distract patients from the current source of their pain. Another great example of where AI and VR meet is the Johnson and Johnson Reality Program, in short, J&J has created a simulated environment which used rules-based algorithms to train physicians in a simulated environment to get better at their job.
  • Improving patient outcomes: Patients outcomes can be improved through a wide variety of strategies and outcomes driven by artificial intelligence.

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Big Data & AI in Healthcare

Recent advancements in AI have fueled discussion of whether AI doctors will replace human doctors in the future. AI can help human physicians to make better decisions. In certain areas of healthcare like radiology, it can replace human judgment entirely.

There has been a rapid development in big data analytic methods, and so much healthcare data is available. Using this data, a lot of clinically relevant information hidden in a large amount of data can be unlocked by powerful AI techniques. This will help in making better clinical decisions.

The ability of AI to use sophisticated algorithms and learn features from a massive amount of data is truly commendable. With the help of these algorithms, insights for assisting clinical practice can be obtained. AI can be equipped with self-correcting and learning abilities which help the system get better accuracy based on the feedback it receives. Therefore, it gets better with time.

These AI systems can help physicians in many ways. Since they are armed with a lot of information, they can assist in clinical decision making. Also, diagnostic errors and therapeutic errors can be minimized.

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Benefits, Problems, Risks & Ethics of AI in Healthcare

Integrating AI into the healthcare ecosystem allows for a multitude of benefits, including automating tasks and analyzing big patient data sets to deliver better healthcare faster, and at a lower cost.

According to Business Insider Intelligence, 30% of healthcare costs are associated with administrative tasks. AI can automate some of these tasks, like pre-authorizing insurance, following-up on unpaid bills, and maintaining records, to ease the workload of healthcare professionals and ultimately save them money.

AI has the ability to analyze big data sets – pulling together patient insights and leading to predictive analysis. Quickly obtaining patient insights helps the healthcare ecosystem discover key areas of patient care that require improvement.

Wearable healthcare technology also uses AI to better serve patients. Software that uses AI, like FitBits and smartwatches, can analyze data to alert users and their healthcare professionals on potential health issues and risks. Being able to assess one’s own health through technology eases the workload of professionals and prevents unnecessary hospital visits or remissions.

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How is AI used today in healthcare?

Below are a few examples in use today:

  • Radiology – AI solutions are being developed to automate image analysis and diagnosis.  This can help highlight areas of interest on a scan to a radiologist, to drive efficiency and reduce human error. There is also opportunity for fully automated solutions – to automatically read and interpret a scan without human oversight – which could help enable instant interpretation in under-served geographies or after hours. Recent demonstrations of improved tumour detection on MRIs and CTs are illustrating the progress towards new opportunities for cancer prevention.
  • Drug Discovery – AI solutions are being developed to identify new potential therapies from vast databases of information on existing medicines, which could be redesigned to target critical threats such as the Ebola virus. This could improve the efficiency and success rate of drug development, accelerating the process to bring new drugs to market in response to deadly disease threats.
  • Patient Risk Identification – By analysing vast amounts of historic patient data, AI solutions can provide real-time support to clinicians to help identify at risk patients. A current focal point includes re-admission risks and highlighting patients that have an increased chance of returning to hospital within 30 days of discharge. Multiple companies and health systems are developing solutions at present based on data in the patient’s electronic health record, driven in part by increasing push back from payers on covering hospitalisation costs associated with re-admission. Other recent work has demonstrated the ability to predict risk of cardiovascular disease based purely on a still image of a patient’s retina.
  • Primary Care/Triage – Multiple organisations are working on direct to patient solutions to triage and give advice via a voice or chat-based interaction. This provides quick, scalable access for basic questions and medical issues. This could help avoid unnecessary trips to the GP, reducing rising demand on primary healthcare providers – plus, for a subset of conditions, provide basic guidance that otherwise wouldn’t be available for populations in remote or under-served areas. While the concept is clear, these solutions still need substantial independent validation to prove patient safety and efficacy.

What are the challenges of AI in healthcare?

In order for an AI solution to be successful, it requires a vast amount of patient data to train and optimise the performance of the algorithms. In healthcare, getting access to these datasets poses a wide range of issues:

  • Patient privacy and the ethics of data ownership – accessing personal medical records is strictly protected. In recent years data sharing between hospitals and AI companies has generated controversy, highlighting several ethical questions:
    • Who owns and controls the patient data needed to develop a new AI solution?
    • Should hospitals be allowed to continue to provide (or sell) vast quantities of their patient data – even if de-identified – to 3rd party AI companies?
    • How can patients’ rights to privacy be protected?
    • What are the consequences (if any) should there be a security breach?
    • What will be the impact of new regulations, like the General Data Protection Regulation (GDPR) in Europe – which includes a person’s right to have their personal data deleted in certain circumstances, with non-compliance generating what could be multi-million dollar penalties?
  • Quality and usability of data – in other industries, vast amounts of data are generally reliable and accurately measured – e.g. aircraft engine sensors or car location and velocity data to predict highway traffic.  In healthcare, data can be subjective, and often inaccurate – with issues including:
    • Clinician’s notes in electronic medical records are unstructured and can be difficult to interpret and process;
    • Data inaccuracy – a patient may be listed as a non-smoker, but were they just reluctant to admit they had not been able to quit?
    • Data sources are siloed across many services providers – making it difficult to capture a full profile and range of determinants for a patient’s health.

The future outlook for AI

The best opportunities for AI in healthcare over the next few years are hybrid models, where clinicians are supported in diagnosis, treatment planning, and identifying risk factors, but retain ultimate responsibility for the patient’s care.  This will result in faster adoption by healthcare providers by mitigating perceived risk and start to deliver measurable improvements in patient outcomes and operational efficiency at scale.

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[1] Frost & Sullivan, Artificial Intelligence & Cognitive Computing Systems in Healthcare, 2016