The human brain is a fantastic work of art; it has very complex neural circuits, and the way it registers, stores, processes, and analyzes information and takes decisions has always been a matter of fascination. To even attempt to replicate the human brain or to “teach” a machine to do that is a hugely ambitious endeavor fraught with controversy. Many a scientist has been fascinated with this concept and thus was born “artificial intelligence” and “deep machine learning.”

“Artificial intelligence” is a familiar buzzword for many in the tech sector today. It has been used in the airline industry for years now, to assist pilots to make decisions under difficult, high-pressure, complex situations which can be too difficult for an individual to handle or when the experience of a pilot, or the lack of it, get in the way of the safety of hundreds of passengers.

AI Use-Cases in Oncology

AI in Healthcare is a promising, still-emerging concept, mostly focused on programs that perform and assist with diagnosis, decision-making, therapy recommendations, and healthcare management.

The Use Cases listed below are not comprehensive yet, but it can still give you insights about the activities in this field. It is ever-improving.

Regulation is a hot topic.

While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues. These challenges of the clinical use of AI has brought upon a potential need for regulations.

Currently, no regulations exist specifically for the use of AI in healthcare. In May 2016, the White House announced its plan to host a series of workshops and formation of the National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence. In October 2016, the group published The National Artificial Intelligence Research and Development Strategic Plan, outlining its proposed priorities for Federally-funded AI research and development (within government and academia). The report notes that a strategic R&D plan for the subfield of health information technology is in development stages. (source: Wikipedia)

AI in Drug Discovery & Development

Finally, here are some Use-Cases:

Diagnosis in Medical Imaging

Early diagnosis: Analyze chronic conditions leveraging lab data and other medical data to enable early diagnosis

Medical imaging insight: Advanced medical imaging to analyze and transform images and model possible situations.

Research & Development

Drug discovery: Find new drugs based on previous data and medical intelligence.

Gene analysis and editing: Understand gene and its component. Predict the impact of gene edits.

Healthcare Management

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

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

Market research: Prepare hospital competitive intelligence.

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

Patient Care

Assisted or automated diagnosis & prescription: AI audit systems minimize prescription errors and give a chance to find certain diseases.

Real-time prioritization and triage: Prescriptive analytics on patient data to enable accurate real-time case prioritization and triage.

Personalized medications and care: Find the best treatment plans according to patient data reducing cost and increasing the effectiveness of care

Patient Data Analytics: Analyze patient and 3rd party data to discover insights and suggest actions. AI allows the institution (hospital, etc…) to analyze clinical data and generate deep insights into patient health. It provides an opportunity to reduce the cost of care, use an efficient resource, and manage population health easily.

Murat

Published By

Murat Durmus

CEO & Founder @ AISOMA AG

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