Skin cancer can spread and develop into other types of cancers, affecting organs such as the brain, liver, and kidneys. It’s the 17th most common cancer globally. If it's not identified early, the survival rate can drop below 50%. However, when melanoma is detected promptly, its five-year survival rate is an impressive 99%.
The worldwide market for skin cancer therapies was valued at $7.2 billion in 2021. Forecasts show that this figure might escalate to $14.5 billion by 2031, with a compound annual growth rate (CAGR) of 7.3%, spanning from 2022 to 2031. This figure underscores the pressing need for heightened awareness and improved skin cancer screening. The global populace is in dire need of an affordable, convenient solution that facilitates swift, economical, and straightforward skin cancer diagnosis, enabling timely surgical intervention before the disease advances.
However, the path is fraught with challenges, including public unawareness of skin cancer symptoms, a general hesitation towards routine medical examinations, limited access to diagnostic centers, a shortage of trained professionals combined with a strained healthcare infrastructure, particularly in less developed nations, and underreporting of cases in regions with weaker healthcare setups. Another significant hurdle is the isolated nature of patient records, which hampers a holistic understanding for both medical practitioners and researchers.
In this article, we explore the robust capabilities of Oracle Cloud Infrastructure (OCI) Artificial Intelligence (AI) Services, emphasizing its potent use in thoroughly analyzing patient data for precise skin cancer detection, all at a simple click. We introduce a prototype clinical decision support system (CDSS) that's seamlessly integrated with an electronic health record (EHR). This system provides users with effortless access to the application, making the skin cancer diagnostic process quick, cost-effective, and user-centric. With early detection facilitated by this setup, patients can receive timely surgical treatments, curtailing the progression of the ailment. Additionally, the blog features a demo link, illustrating the straightforward development process of such a CDSS using OCI AI Services.
An integrated CDSS combined with an EHR can use Oracle OCI AI Services to thoroughly examine patient data, providing highly precise skin cancer detection at your fingertips. The implications for both patients and organizations include the following examples:
The usual user experience with an integrated CDSS for skin cancer detection consists of the following processes:
The design of this integrated CDSS illustrates how various components interrelate, centering around OCI Vision. An end user goes through the following process:
The machine learning (ML) model behind this process is designed to categorize skin lesions into eight distinct classes, including melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, vascular lesion, and squamous cell carcinoma.
The solution is built using the following Oracle AI services. Oracle's AI services provide pretrained models that you can custom-train with data to improve model quality, making it easier for developers to adopt and use AI technology.
The return on investment (ROI) is notably high, characterized by the following features:
In this post, we delved deep into the powerful functionalities of OCI AI services, highlighting its significance in the meticulous analysis of patient data to accurately diagnose skin cancer with just a single click. We discussed the user experience, the app's design and framework, and the integration of OCI AI services in its construction.
For access to the source code and any further inquiries, contact Vivek Acharya.
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A passionate digital transformation architect, author/writer, AI & Healthcare evangelist and a blogger. Responsible for promoting digital transformation; modernizing core business systems, and deriving value. Fifteen plus years of experience in problem solving in the realm of architecture, design, mentoring, C- suite relationships, and navigating projects through stakeholders.
Vivek is pursuing an MBA from Boston University, holds a postgraduate certification on Artificial Intelligence and Machine Learning from UT Austin, digital transformation certificate from Cornell and is a certified IBM Design Thinking coach. Also certified from MIT and Stanford on "Healthcare and AI".