top of page
  • Team ODA

Artificial intelligence (AI) in Healthcare: A Critical Appraisal


ai-in-healthcare-a-critical-appra

Jeremy Farrar, WHO chief scientist, recently called for making the process of deploying LLMs (large language models) transparent. His suggestions come in the wake of times when the use of artificial intelligence (AI) in healthcare has been rising rapidly.


AI has opened new course in healthcare and so its use in the industry on the rise. Valued at

around USD 22.45 billion in 2023, the healthcare AI is anticipated to grow at an annual rate of 36.4% from 2024 to 2030. We look into more about AI-based predictions in one of the most rapidly transforming industries – healthcare – today.


AI Models in Healthcare


AI-based prediction models have emerged as powerful in healthcare, offering promise to make pharmaceutical-hospital industry more efficient and significantly improve healthcare delivery. Here are AI models being heavily used in healthcare.


Ensemble Learning


Ensemble Learning aggregates diverse algorithms, mitigates biases and improves overall

diagnostic accuracy while considering a range of perspectives.


Convolutional Neural Networks (CNNs)


With their capability of superior spatial understanding, CNNs excel at identifying patterns

and abnormalities in medical images.


Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Models


RNNs and LSTMs specialize in sequential data analysis, crucial for time-series healthcare

data. They are pivotal in predicting patient outcomes and tailoring personalized treatment

plans.


Generative Adversarial Networks (GANs) and Reinforcement Learning


GANs aid in molecular design by creating diverse chemical structures and are important to

drug discovery. Likewise, reinforcement learning optimizes decision-making through trial and error, also playing a key role in drug development strategies and treatment optimization.


Natural Language Processing (NLP)


NLP transforms unstructured medical text, such as clinical notes and electronic health

records, into actionable insights and supports natural communication between systems and

healthcare professionals.


Advantages of AI Predictions in Healthcare


  • Contribute to the development of personalized treatment plans, ensuring that patients receive interventions tailored to their unique genetic makeup and health history.

  • Reduces the risk of misinterpretation on the part of healthcare professionals.

  • Automates routine tasks – administrative processes and data management – diverts complete focus on patient care.

  • Streamlines the pharmaceutical supply chain, minimizing shortages and ensuring the availability of essential medications.

  • Contributes to the growth of telemedicine by enhancing remote consultations, diagnostics, and treatment monitoring.

  • Helps insurers predict and manage risks, allowing for more accurate underwriting and pricing of healthcare policies.

  • Helps identify fraudulent claims, reducing financial losses for insurance companies and promoting fair and transparent practices.


Limitations and Challenges of AI in Healthcare


Despite the promise, AI in healthcare grapples with many challenges and is subject to various limitations as discussed:


Limited Availability of Data


AI models heavily rely on vast datasets for training and validation. In cases where data is

limited or biased, the predictive accuracy of these models may be compromised.


Regulatory Guidelines and Framework


The healthcare industry is subject to stringent norms, and the integration of AI requires

compliance with these guidelines.


Interoperability Issues


The lack of standardization and interoperability between different healthcare systems and

devices hinders the integration of AI into existing workflows.


Ethical Concerns


AI in healthcare raises ethical concerns related to patient privacy, consent, and the

responsible use of sensitive health data.


Algorithmic Bias


AI models may inherit biases present in the training data, leading to disparities in healthcare

outcomes.


Conclusion


To fulfil the promise of AI in healthcare, having support from an AI specialist will be a wise

decision. Bringing expertise on overcoming challenges, in this pursuit, Optimum Data Analytics (ODA) will assist you script success stories. To add value to your healthcare business and rise rapidly to the top, connect with our consultants.

Recent Posts

See All

Comentarios


bottom of page