AI applications in healthcare

Artificial-intelligence-in-healthcare
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introduction

AI, computer vision and machine learning systems proved that machines are better and faster than humans analyzing big data. Today, organizations have large datasets of patient data and insights about diseases through techniques like Genome Wide Association Studies (GWAS). Using AI, healthcare providers can analyze and interpret the available patient data more precisely for early diagnosis and better treatment. Today, it is possible to say whether a person has the chance to get cancer from a selfie using computer vision and machine learning to detect increased bilirubin levels in a person’s sclera, the white part of the eye.

Several applications of artificial intelligence in healthcare have been identified and are listed below:

Patient Care

    • Assisted or automated diagnosis & prescription: AI audit systems minimize prescription errors and give a chance to find accurate disease.
    • Pregnancy Management: Monitor mother and fetus to reduce mother’s worries and enable early diagnosis
    • Real-time prioritization and triage: Prescriptive analytics on patient data to enable accurate real-time case prioritization and triage.
      • Jvion: The Cognitive Clinical Success Machine precisely and comprehensively foresees risk deliver the recommended actions that improve outcomes.
      • WellframeWellframe flips the script by delivering interactive care programs directly to patients on a mobile device. Its portfolio of clinical modules, developed based on the evidence-based care, enables Care Team to provide a personalized experience for any patient.
      • Enclitic: Patient triaging solutions scan the incoming cases for multiple clinical findings, determine their priority, and route them to the most appropriate doctor in the network.
    • Personalized medications and care: Find the best treatment plans according to patient data reducing cost and increasing effectiveness of care
      • GNS Healthcare: The company uses machine learning to match the patients with the treatments that prove the most effective for them.
      • Oncora Medicals: The software structure, analyze and learn from the data that health systems have to enable them to provide personalized treatment.
    • Patient Data Analytics: Analyze patient and/or 3rd party data to discover insights and suggest actions.  AI allows the institution (hospital, etc…) analyze clinical data and generate deep insights into patient health. It provides an opportunity to reduce cost of care, use efficiently resource and manage population health easily.
    Zakipoint HealthThe company displays all the relevant healthcare data at a member level on a dashboard to understand risk and cost, provide tailored programs and improve patient engagement

Medical Imaging and Diagnostic

  • Early diagnosis: Analyze chronic conditions leveraging lab data and other medical data to enable early diagnosis
    • Amara Health Analytics: Amara provides real-time predictive analytics to support clinicians in the early detection of critical disease states.
  • Medical imaging insight: Advanced medical imaging to analyze and transform images and model possible situations.

            SkinVision: SkinVision enables you to find skin cancer early by taking photos of your skin with your phone and get to a doctor at the right time.

R&D

  • Drug discovery: Find new drugs based on previous data and medical intelligence.
    • NuMediiBiopharma company, NuMedii has built a technology, AIDD (Artificial Intelligence for Drug Discovery) that harnesses Big Data and AI to rapidly discover connections between drugs and diseases at a systems level.
  • Gene analysis and editing: Understand gene and its component. Predict the impact of gene edits.

                Desktop Genetics: Desktop Genetics is an international biotechnology company to help researchers discover and treat the root genetic causes of human disease.

  • Device and drug comparative effectiveness

              4Quant: The company utilizes the latest Big Data and Deep Learning technology to extract meaningful, actionable information from images and videos for experiment design to help pick and choose which components make the most sense for the needs.

Healthcare Management

. Brand management and marketing: Create an optimal marketing strategy for the brand based on market perception and target segment.

Healint: Company’s product Migraine Buddy has recorded terabytes of data that helps patients, doctors and researchers better understand the real-world causes and effects of neurological disorders.

. Pricing and risk: Determine the optimal price for treatment and other service according to competition and other market conditions.

. Market research: Prepare hospital competitive intelligence.

MD Analytics: MD analytics is a global provider of health and pharmaceutical marketing research solutions.

. Operations: Process automation technologies such as intelligent automation and RPA help hospitals automate routine front office and back office operations such as reporting.

Future Applications

Below is a list of applications which are gaining momentum with the help of today’s funding and research focus.

i)Personalized Medicine

 If your child gets their wisdom teeth pulled, it’s likely they’ll be prescribed a few doses of Vicodin. For a urinary tract infection (UTI), it’s likely they’ll get Bactrim. In the hopefully-not-too-distant future, few patients will ever get exactly the same dose of any drug. In fact, if we know enough about the patient’s genetics and history, few patients may even be prescribed the same drug at all.

 The promise of personalized medicine is a world in which everyone’s health recommendations and disease treatments are tailored based on their medical history, genetic lineage, past conditions, diet, stress levels, and more.

 While eventually this might apply to minor conditions (i.e. giving someone a slightly lesser dose of Bactrim for a UTI, or a completely unique variation of Bactrim formulated to avoid side effects for a person with a specific genetic profile), it is likely to make much of its initial impact in high-stakes situations (i.e. deciding whether or not to go into chemotherapy, based on a person’s age, gender, race, genetic makeup, and more). 

ii)Automatic Treatment or Recommendation

 In the diabetes video created by Medtronic and IBM, (Medtronic’s own Hooman Hakami states that at some point, Medtronic wants to have their insulin checking pumps work autonomously, monitoring blood-glucose levels and injecting insulin as needed, without disturbing the user’s daily life.

 This, of course, is a microcosm of a much larger picture of autonomous treatment. Imagine a machine that could adjust a patient’s dose of pain killers or antibiotics by tracking data about their blood, diet, sleep, and stress. Instead of counting on distractible human beings to remember how many pills to take, a small kitchen table machine learning “agent” (think Amazon’s Alexa) might dole out the pills, monitor how many you take, and call a doctor if your condition seems dire or you haven’t followed its directions.

 The legal constraints of putting so much power in the “hands” of an algorithm are not trivial, and like any other innovation in healthcare, autonomous treatments of any kind will likely undergo long trails to prove their viability, safety, and superiority to other treatment methods.

iii)Improving Performance (Beyond Amelioration)

 Orreco and IBM recently announced a partnership to boost athletic performance, and IBM has set up a similar partnership with Under Armor in January 2016. While western medicine has kept its primary focus on treatment and amelioration of disease, there is a great need for proactive health prevention and intervention, and the first wave of IoT devices (notably the Fitbit) is pushing these applications forward.

 One can imagine that disease prevention or athletic performance won’t be the only applications of health-promoting apps. Machine learning may be implemented to track worker performance or stress levels on the job, as well as for seeking positive improvements in at-risk groups (not just relieving symptoms or healing after setbacks).

 The ethical concerns around “augmenting” human physical and (especially) mental abilities are intense, and will likely be increasingly pressing the coming 15 years as enhancement technologies become viable.

iv)Autonomous Robotic Surgery

 At present, robots like the da Vinci are mostly an extension of the dexterity and trained ability of a surgeon. In the future, machine learning could be used to combine visual data and motor patterns within devices such as the da Vinci in order to allow machines to master surgeries. Machines have recently developed the ability to model beyond-human expertise in some kinds of visual art and painting:

 If a machine can be trained to replicate the legendary creative capacity of Van Gough or Picasso, we might imagine that with enough training, such a machine could “drink in” enough hip replacement surgeries to eventually perform the procedure on anyone, better than any living team of doctors. 

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