Artificial Intelligence (AI) in Healthcare ..... Part 01

Applications of AI in Healthcare.

Artificial intelligence has many applications across various industries, and healthcare is one that could be transformed by the technology. The potential for AI in healthcare is tremendous as it increasingly becomes integrated into the healthcare ecosystem. The processing power of AI has applications far beyond providing simple diagnoses. It can be used to help health professionals identify the severity of what is wrong with the patient and provide insight as to why they are experiencing certain symptoms. This additional information helps doctors decide on the most effective treatment. Also, AI is transforming the way doctors deliver cost-effective, high-quality diagnostic and treatment services to their patients. For example, the technology can identify patterns and anomalies in diagnostic data from medical scans at a speed and volume that humans are simply unable to replicate. Some more applications of AI, are covered below:

• AI offers a number of advantages over traditional analytics and clinical decision-making techniques.  Learning algorithms can become more precise and accurate as they interact with training data, allowing humans to gain unprecedented insights into diagnostics, care processes, treatment variability, and patient outcomes.  

BCI for a locked-in syndrome patient.

• Neurological diseases and trauma to the nervous system can take away some patients’ abilities to speak, move, and interact meaningfully with people and their environments.  Brain-computer interfaces (BCIs) backed by artificial intelligence could restore those fundamental experiences to those who feared them lost forever. Brain-computer interfaces could drastically improve quality of life for patients with Amyotrophic Lateral Sclerosis (ALS), strokes, or locked-in syndrome, as well as the 500,000 people worldwide who experience spinal cord injuries every year.

• Artificial intelligence is helping to enable “Virtual Biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.

• Shortages of trained healthcare providers, including ultrasound technicians and radiologists can significantly limit access to life-saving care in developing nations around the world. Artificial intelligence could help mitigate the impacts of this severe deficit of qualified clinical staff by taking over some of the diagnostic duties typically allocated to humans.For example, AI imaging tools can screen chest x-rays for signs of tuberculosis, often achieving a level of accuracy comparable to humans.  This capability could be deployed through an app available to providers in low-resource areas, reducing the need for a trained diagnostic radiologist on site. However, algorithm developers must be careful to account for the fact that disparate ethnic groups or residents of different regions may have unique physiologies and environmental factors that will influence the presentation of disease.

Electronic Health Records.

• Electronic Health Records (EHR) have played an instrumental role in the healthcare industry’s journey towards digitalization, but the switch has brought myriad problems associated with cognitive overload, endless documentation, and user burnout. EHR developers are now using artificial intelligence to create more intuitive interfaces and automate some of the routine processes that consume so much of a user’s time. Antibiotic resistance is a growing threat to populations around the world as overuse of these critical drugs fosters the evolution of superbugs that no longer respond to treatments.  Multi-drug resistant organisms can wreak havoc in the hospital setting, and claim thousands of lives every year. Electronic health record data can help to identify infection patterns and highlight patients at risk before they begin to show symptoms.  Leveraging machine learning and AI tools to drive these analytics can enhance their accuracy and create faster, more accurate alerts for healthcare providers.

• Smart devices are taking over the consumer environment, offering everything from real-time video from the inside of a refrigerator to cars that can detect when the driver is distracted. In the medical environment, smart devices are critical for monitoring patients in the ICU and elsewhere.  Using artificial intelligence to enhance the ability to identify deterioration, suggest that sepsis is taking hold, or sense the development of complications can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties. Inserting intelligent algorithms into these devices can reduce cognitive burdens for physicians while ensuring that patients receive care in as timely a manner as possible.

• Immunotherapy is one of the most promising avenues for treating cancer.  By using the body’s own immune system to attack malignancies, patients may be able to beat stubborn tumors.  However, only a small number of patients respond to current immunotherapy options, and oncologists still do not have a precise and reliable method for identifying which patients will benefit from this option. Machine learning algorithms and their ability to synthesize highly complex data sets may be able to illuminate new options for targeting therapies to an individual’s unique genetic makeup.

Smart Wearable Devices for Health Monitoring.

• Almost all consumers now have access to devices with sensors that can collect valuable data about their health.  From smartphones with step trackers to wearables that can track a heartbeat around the clock, a growing proportion of health-related data is generated on the go. Collecting and analyzing this data and supplementing it with patient-provided information through apps and other home monitoring devices can offer a unique perspective into individual and population health. Artificial intelligence will play a significant role in extracting actionable insights from this large and varied treasure trove of data.  

• Continuing the theme of harnessing the power of portable devices, experts believe that images taken from smartphones and other consumer-grade sources will be an important supplement to clinical quality imaging  especially in under served populations or developing nations. The quality of cell phone cameras is increasing every year, and can produce images that are viable for analysis by artificial intelligence algorithms.  Dermatology and ophthalmology are early beneficiaries of this trend. Using smartphones to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects may be able to help under served areas cope with a shortage of specialists while reducing the time-to-diagnosis for certain complaints.

• As the healthcare industry shifts away from fee-for-service, so too is it moving further and further from reactive care.  Getting ahead of chronic diseases, costly acute events, and sudden deterioration is the goal of every provider and reimbursement structures are finally allowing them to develop the processes that will enable proactive, predictive interventions. Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers in to problems long before they might otherwise recognize the need to act.

• AI can provide earlier warnings for conditions like seizures or sepsis, which often require intensive analysis of highly complex data sets. Leveraging AI for clinical decision support, risk scoring, and early alerting is one of the most promising areas of development for this revolutionary approach to data analysis.  By powering a new generation of tools and systems that make clinicians more aware of nuances, more efficient when delivering care, and more likely to get ahead of developing problems, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.

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