How artificial intelligence is transforming the future of healthcare one step at a time
AI in healthcare refers to the use of complex algorithms designed to mimic human cognition and perform certain tasks in an automated fashion at a fraction of the time and cost.
Projected a few years ago to be a $150 billion industry by 2026, Artificial Intelligence (AI) systems are radically transforming industries around the world and healthcare is no exception to this development. New AI applications are being developed and experimented with to streamline administrative and medical processes, enhance clinical decision making and support, manage long-term care - all of which are showing great promise.
AI in healthcare refers to the use of complex algorithms designed to mimic human cognition and perform certain tasks in an automated fashion at a fraction of the time and cost. Simply put, when data is injected into the platform, algorithms, and machine learning solutions kick in, working with the data, using deep data analytics, and delivering outcomes and reports which would be as accurate if not more than human interventions.
From making more accurate diagnoses, finding links between genetic codes to powering surgical robots, maximising administrative efficiency, and understanding how patients will respond to treatment plans, there are limitless opportunities to leverage AI in healthcare.
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Using machine learning in precision medicine can help predict what treatment protocols are likely to succeed based on a patient’s attributes, treatment history, and context, allowing more accurate and impactful interventions at the right moment in a patient’s care.
Similarly, the use of voice-activated Electronic Medical Records (EMRs) can go a long way towards optimising a doctor’s efficiency by reducing hours spent on clerical work and administration.
How AI is being used today in healthcare
Current use cases are already exhibiting AI’s transformational impact in healthcare and future potential uses offer astonishing possibilities.
Here are some broad use case scenarios for current AI use:
Improving Diagnostics: It is one of AI's most exciting healthcare applications. AI solutions are helping automate image analysis and diagnosis, removing the possibility of human error in readings.
Drug Discovery: AI is being harnessed to identify new therapies from vast databases of information on existing medicines. This could help improve lengthy timelines and processes tied to discovering and taking drugs.
Predictive Patient Risk Identification: At-risk patients can be swiftly identified by algorithmic analysis of vast amounts of historic patient data. Cohesive health ecosystems that help organize and maintain patient records can play a vital role. This will also help with reducing cost and time in manual drudgery of procedures and optimising healthcare resources.
Primary Care: Direct-to-patient solutions via voice or chat-based interaction are helping provide quick, scalable access for basic medical issues. AI-based voice-to-text technologies save countless hours taken to type memos. The doctor and the patient can speak freely while a voice-enabled assistant listens in and puts down the text into EMRs, streamlining the drudgery of manually scribing patient history and easing out the problem of missing medical records.
AI Robot-Assisted Surgery: It is another area that is being explored to help with everything from minimally-invasive procedures to open-heart surgery. Working with doctors, robots have already been able to carry out complex procedures successfully with precision, flexibility, and control that goes beyond human capabilities.
The use of AI is certainly surging in healthcare, however, it is still early days, and adoption of AI in healthcare is not without challenges that may impede its momentum.
For any AI solution to be successful, it requires a vast amount of patient data. Getting access to private medical records, however, poses the all-important issues of data privacy and ethics. Privacy is expected and enforced especially strongly when it comes to private medical data. There is room for some work around protecting patient data privacy and the answers may lie in cohesive healthcare ecosystems that will have to weave in cybersecurity as an essential component of their world view.
Regulation—this is another challenge with additional geolocation implications. Different nations will adopt different guidelines around levels of transparency in automated decision-making. Informed consent also poses questions especially when participating individuals in some cases may not be physically or mentally equipped to give consent.
Although hard to establish its parameters, transparency is vital to medical AI. A doctor needs to be able to understand and explain why an algorithm recommends a procedure or line of treatment at least until the machine itself learns to come up with more intuitive and transparent prediction-explanation tools.
Quality and usability of data is also a challenge because health data can be subjective, fragmented, and often inaccurate. While the subjectivity issue may need a cultural change, the fragmentation in legacy data can be rectified at an ecosystem level, wherein different stakeholders with access to data ingest it into a central repository.
User adoption at both patient and practitioner ends is another significant challenge. Doctors’ decisions are based on training, experience, and intuition, as well as problem-solving skills. For doctors to consider suggestions from machines can be difficult. Similarly, the human touch of interacting with a doctor can be lost with these types of tools. Patients may be reluctant to trust a diagnosis from an algorithm rather than humans.
Future outlook of AI
Evolving healthcare ecosystems will have to balance the use and perception of AI for both clinicians as well as patients. They must develop and use AI in hybrid models. It should be seen as an aid or amplifier of medical knowledge and not as a replacement for doctors. AI should be used and perceived as supporting diagnosis, treatment planning, and identifying risk factors, but clinicians retain final charge for a patient’s care. The hybrid model will help in accelerating the adoption of AI by healthcare practitioners while delivering measurable and scalable improvements in health outcomes.
Artificial intelligence is certainly pushing the envelope towards making game-changing improvements in healthcare. While efforts and advances need to be made before AI solutions can be deployed in a safe and ethical way, AI does open up limitless possibilities to accelerate the move of healthcare into a seamless ecosystem-based model that promises to drive improvements across the care continuum.
This article has been written by Aneesh Nair, Co-Founder and CIO, MyHealthcare