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Artificial Intelligence in Medicine: The Innovation Landscape

Ms. R. Sri Sai Shilpa

Intern

Mr. P. Amruth Sagar

Senior Knowledge Scientist
INTRODUCTION

What do self-driving cars, face recognition, industrial robots, missile guidance and tumor detection have in common? They are all complex, real-world problems being solved with applications of intelligence. The Robot series by science fiction author Isaac Asimov, perceived as foundation to artificial intelligence, is now being crafted into reality. Since 1920, scientists have dreamed of creating an “electronic brain.”
Artificial intelligence is the study of computerized systems or programs that are capable of rational thinking. According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs.”[1] An intelligent machine is one that mimics cognitive functions and learning abilities of a human brain, such that it can be applied for solving problems.
For AI technologies, health care has long been viewed as a promising domain. AI-based applications have the potential to improvise interaction in a clinical care-related environment, thus having an inevitable impact on the health outcomes and quality of life. AI systems have now been employed to assist health-care professionals in managing enormous amounts of patient data, provide guidance and decision support, and improve clinical workflow. Despite the recent developments, these systems are limited when it comes to the need of monitoring and updating by clinicians.

OBJECTIVE
  • To spot the top medical companies and research institutes working in the AI domain.
  • To identify the potential of AI-based technologies in assisting the medical personnel.
  • To recognize the type of diseases that can be diagnosed, treated or predicted with the assistance of AI tools.
  • To estimate the risks or challenges we need to consider while deploying AI systems for clinical purposes.
  • To approximate further growth and future prospects of AI in the health-care industry.

ASSIGNEES & UNIVERSITIES
The pie chart below shows the distribution of patent filings based on type of assignees

Overall, corporates have accounted for the majority of filings at 68%. Interestingly, research institutes and universities have contributed 20% of the R&D that, by all means, can be considered as comparatively on the higher side. Other minor contributions of independent investors and collaborations account for 9% and 3%, respectively.
Siemens, Philips and GE Healthcare are the leading health-care organizations in the AI domain in terms of patent filings. Siemens is at the forefront of inventing decision support systems for computerized detection and treatment of breast cancer. It has come up with automated methods for distinguishing benign and malignant lesions. The well-established health-care organization, Philips, developed clinical decision support systems for both diagnosing and treating cancer as well as neurological disorders (Alzheimer’s disease).
GE Healthcare has shown notable progress by developing computer-aided image processing systems for diagnosis, particularly, in the branch of oncology.
Intriguingly, Samsung Electronics has come forward with computer intelligent systems to assist in diagnosis. Arch Development Corporation and Hologic Corporation have precisely developed apparatus for computer-aided detection of breast cancer, while IBM Corporation has designed decision support systems for assistance in diagnosis and treatment of cancer and neurological disorders.
University of Chicago, University of South Florida and University of Michigan are the noteworthy research universities that have contributed by deploying automated methods for early detection of lung and breast cancer.

TECHNOLOGY INSIGHTS

The primary aim of utilizing AI tools in the medical platform is to provide better diagnosis, cure and treatment of critical patient conditions. For such decision support, the intelligent systems must be integrated with various machine learning algorithms and approaches involving fuzzy set theory, Bayesian networks and artificial neural networks. These intelligent systems are implemented for diagnosis of early stages of cancer, suggesting efficient treatment plans, predicting future adverse conditions and providing risk assessment of surgical procedures and health record management.

On analysis, prominent development has been witnessed in the area of diagnostic assistance. AI in medical diagnosis has incredible potential for detecting various types of cancers, pulmonary, cardiovascular and neurological disorders. Acceptable development has been noted in terms of laying out effective treatment plans and predicting forthcoming disorders. Finally, the most obvious application is the management of medical records and, to lesser extent, robots designed to assist in surgical procedures.

We have encountered comparatively more number of patent filings focusing on detection of breast and lung cancer.

Scientists have developed better diagnostic systems by employing intelligent machines in medical imaging equipment for efficient screening. Research and development focused towards the diagnostic applications in pulmonary (pneumonia, emphysema, COPD), cardiovascular (coronary artery diseases) and neurological (Parkinson’s disease, Alzheimer’s disease) disorders has also been distinguished. There is satisfactory development in case of computer-aided diagnosis of bone abnormalities (rheumatoid arthritis, osteoporosis) and surgical assistance during joint replacement procedures.

RISKS/CHALLENGES
Though AI is transforming the face of the health-care industry, certain risks it involves need to be discussed. The development of AI has an immediate and direct impact on the economy. The pursuit of improvising the quality of life comes with its own set of challenges: [2]

  1. Memory storage: The system being utilized for the diagnosis or treatment purposes needs to be trained on multiple cases, hence huge amount of data assimilation capacity is required.
  2. High-definition images: To minimize the risk of false diagnosis and avoid even minute discrepancies, the systems need to be fed with images of high resolution.
  3. Data collection: In case of diagnosing and suggesting treatment plans for such specific diseases as cancer or neurological disorders, evidence-based decision support systems are used, which need to be frequently upgraded with latest information. Thus, the collection of patient specific content from all regions is a task.
  4. Quality of decision: Intelligent systems, despite their cognitive capabilities, might not come up with satisfactory solutions when they encounter cases on which they have not been trained on.
  5. Reliability: It is difficult to convince the patients on getting accustomed to computerized systems. Many of the medical personnel involved require training on the use of these systems.
  6. Affordability: All leading organizations must prioritize on offering affordable AI medical solutions.

THE PRESENT SCENARIO
The evolution of the medical industry due to the involvement of AI is extraordinary. It is quite intriguing how AI is already transforming the face of the health-care industry. Artificial Intelligence is rapidly evolving and redefining the workflow of patient care. It has the potential to improve the quality of cancer diagnosis, treatment, drug discoveries, prognosis and management of medical records. These intelligent systems have successfully elevated the reputations of many innovative companies in the clinical field.
Multinational corporations have taken an immense forward leap to revolutionize the health-care industry. In 2013, IBM Corporation presented the Watson computer. Since then, MedTech companies have been hustling to collaborate with it.[3] Apple, Medtronic, Johnson & Johnson have teamed with IBM for research to develop cognitive health assistants.[4] IBM has also acquired Merge Healthcare to boost the capabilities of Watson.[5] Welltok is utilizing the Watson supercomputer and its cognitive capabilities to optimize consumer health care.[6] [7]
Such collaborations have turned out to be prolific so far. In a broad sense, the IBM Watson Health unit wants to enable cloud-based supercomputers for increasing the pace of detection and treatment.
Moorfields Eye Hospital in London could successfully detect diabetic retinopathy by recognizing the minute symptoms that even an experienced doctor may miss. Machine learning technology of Google DeepMind was utilized for this scanning.[8]
Arterys and VisExcell are two medical imaging companies integrating AI-powered technologies and machine learning to improvise detection of anomalies.[6] AiCure has deployed AI monitoring systems to ensure regular intake of medication by patients.[9] Onyx Healthcare is working to bridge the gap between health-care providers and patients,[10] while Babylon Health aims to provide personalized health-care services that are affordable.[8]

CONCLUSION
The incorporation of AI in the clinical field has now become a trend. Since AI powered systems have the potential to vastly improve the quality of diagnosis and treatment, multinational corporations have started to invest in this domain. More than 90 start-ups have been identified in this area of expertise.[11] Organizations have been applying machine-learning algorithms and predictive analytics to redefine clinical care at every level.
Clearly, one can anticipate the establishment of AI health-care units on a global scale. “The goal is to support the physician, not replace him or her” said Anil Jain, vice president of IBM’s Watson Health and an internist and medical informatics specialist at the Cleveland Clinic.[12] Having stated so, he clarifies that IBM aims only at providing cognitive health assistants for medical specialists. Nevertheless, the fear of unemployment prevails.
Medical companies assimilating AI cognitive systems are continually contributing to the change in revenue. Technological innovations in the health-care industry have the incredible potential to improve the quality of life.

Acknowledgements:
Ms. R. Sri Sai Shilpa, Mr. P. Amruth Sagar, Mrs. Uma Parameswaran
Disclaimer:
Views expressed in this document are based on collective analysis of independent research, information retrieved from various sources and tools used for compilation. This document is for general information purposes only. The document was compiled with reasonable attention. However, no liability is accepted by SciTech Patent Art Services Pvt. Ltd for any inaccuracies or from using or relying upon the information in this document.
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References:
  1. https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_overview.htm
  2. https://ea-stiftung.org/files/ai-opportunities-and-risks.pdf
  3. https://en.wikipedia.org/wiki/Watson_(computer)
  4. http://www.industryweek.com/technology/ibm-teams-apple-medtronic-johnson-johnson-artificial-intelligence-health-program
  5. http://www.merge.com/News/Article.aspx?ItemID=667
  6. http://media.bemyapp.com/6-ai-startups-revolutionizing-healthcare/
  7. http://venturebeat.com/2014/10/22/how-welltok-tapped-ibms-watson-to-upgrade-its-health-optimization-platform/
  8. http://www.allaboutcircuits.com/news/can-an-artificial-intelligence-diagnose-disease-better-than-a-doctor/
  9. http://medicalfuturist.com/artificial-intelligence-will-redesign-healthcare/
  10. http://www.onyxhealthcareusa.com/rise-intelligent-medical-device/
  11. https://www.cbinsights.com/blog/artificial-intelligence-startups-healthcare/
  12. http://www.worldhealth.net/news/5-ways-ai-changing-healthcare/