Last updated on June 27th, 2023 at 06:04 pm
Artificial intelligence (AI) has revolutionized the field of healthcare, offering significant advancements in disease detection, diagnosis, and treatment. With the power of deep learning, AI applications now rival the performance of trained professionals, particularly in areas like medical image interpretation and drug discovery.
However, the successful implementation of AI in healthcare hinges upon addressing critical ethical and practical challenges. These challenges include exploitability and algorithmic bias, which must be resolved to ensure the efficacy and fairness of AI-driven solutions.
Artificial intelligence for global health (AI) has demonstrated great progress in the detection, diagnosis, and treatment of diseases. Deep learning has enabled applications with performance levels approaching those of trained professionals in tasks including the interpretation of medical images and the discovery of drug compounds.
The ability of AI to deliver on its promises, however, depends on successfully resolving the ethical and practical issues identified, including that of exploitability and algorithmic bias.
The Definition of Artificial Intelligence (AI)
According to “The New scientist”, AI is a groundbreaking technology with the potential to transform the world. From cancer treatment to autonomous vehicle control, AI has the capability to cure diseases, enhance human intelligence, and simulate various aspects of human cognition.
Overcoming the Toughest Challenges with AI
Computing Power: The processing power required for AI is immense, especially for tasks like deep learning. Obtaining and funding the necessary computing power can pose a significant challenge for businesses, particularly startups.
AI Integration into Existing Systems: Proper implementation of AI into existing systems demands expertise and experience in the field. Collaborating with AI solution providers who specialize in AI development from conception to deployment is essential for seamless integration.
Expertise Gap: Due to the emerging nature of AI, there is a shortage of professionals with the necessary skills and training for AI development. This scarcity necessitates additional budget allocation for training or hiring AI specialists in the software development industry.
Legal and Regulatory Issues: AI’s unique funding, research, and development processes create regulatory challenges. Collaboration between private sector AI developers and governments is crucial to ensure responsible and ethical AI deployment.
AI's Transformational Role in Healthcare: Case Study
Case n#1 of Artificial Intelligence for global health
Nature.com has published a scientific report about ” Blood pressure measurements with the OptiBP smartphone app”, saying that High blood pressure (BP) remains the leading risk factor for death and disability in both high and low-income countries.
Its complications are responsible for the deaths of approximately ten million people annually, a 50% increase over the estimates from 1990.
By 2025, the number of people suffering from hypertension will reach 1.5 billion. The impact of this disease represents a daunting burden to any healthcare system.
WHO issues first global report on Artificial Intelligence (AI) in health
WHO’s Global Report on AI in Health
The World Health Organization (WHO) has released its first global report on the ethics and governance of AI in health. The report emphasizes the need to prioritize ethics and human rights in the design, deployment, and utilization of AI for healthcare. By adhering to ethical guidelines, AI has the potential to enhance the speed and accuracy of diagnosis, clinical care, health research, and public health interventions. WHO’s report serves as a valuable resource for countries seeking to maximize the benefits of AI while mitigating risks.
Case #2: OptiBp
Mobile health diagnostics have been shown to be effective and scalable for chronic disease detection and management.
By maximizing the smartphones’ optics and computational power, they could allow the assessment of physiological information from the morphology of pulse waves and thus estimate cuffless blood pressure (BP).
Methodology for training OptiBP
We trained the parameters of an existing pulse wave analysis algorithm (oBPM), previously validated in anesthesia on pulse oximeter signals, by collecting optical signals from 51 patients’ fingertips via a smartphone while simultaneously acquiring BP measurements through an arterial catheter.
We then compared smartphone-based measurements obtained on 50 participants in an ambulatory setting via the OptiBP app against simultaneously acquired auscultatory systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) measurements.
Patients were normotensive (70.0% for SBP versus 61.4% for DBP), hypertensive (17.1% vs. 13.6%) or hypotensive (12.9% vs. 25.0%).
The difference in BP (mean ± standard deviation) between both methods was within the ISO 81,060–2:2018 standard for SBP (− 0.7 ± 7.7 mmHg), DBP (− 0.4 ± 4.5 mmHg), and MBP (− 0.6 ± 5.2 mmHg). These results demonstrate that BP can be measured with accuracy at the finger using the OptiBP smartphone app.
This may become an important tool to detect hypertension in various settings, for example in low-income countries, where the availability of smartphones is high but access to health care is low.
Sources & Credits:
https://www.accessdata.fda.gov/
https://www.chathamhouse.org/2022/03/challenges-ai
https://www.cybiant.com/resources/the-challenges-of-ai-implementation/
It is clear that AI is Revolutionizing Healthcare: Transforming Lives for the Better
Artificial Intelligence is revolutionizing the way we approach healthcare. AI is transforming the industry
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