AI and health: How machine learning can improve diagnosis and treatment

Artificial intelligence (AI) is transforming the world of health care, offering new possibilities to enhance diagnosis and treatment, improve patient outcomes, and reduce costs. One of the most promising areas of AI in health care is machine learning (ML), which is the process of teaching computers to learn from data and perform tasks that would otherwise require human intelligence.  

Machine learning can help health care professionals and researchers in many ways, such as: 

  • Detecting diseases and conditions early, before they become more serious or costly to treat. For example, ML algorithms can analyze medical images, such as X-rays, CT scans, or MRIs, to identify signs of cancer, fractures, or infections. ML can also help diagnose rare or complex diseases, such as Alzheimer’s, Parkinson’s, or autism, by finding patterns in genetic, behavioral, or environmental data. 
  • Recommending the best treatment options for each patient, based on their individual characteristics, preferences, and medical history. For example, ML can help personalize drug dosages, radiation therapy, or surgical plans, to optimize efficacy and minimize side effects. ML can also help monitor patients’ responses to treatment and adjust accordingly, or suggest alternative therapies if needed. 
  • Predicting the risk of complications, adverse events, or readmissions, and taking preventive measures to avoid them. For example, ML can help identify patients who are more likely to develop infections, bleeding, or other problems after surgery, and provide them with extra care or follow-up. ML can also help predict the likelihood of patients developing chronic conditions, such as diabetes, heart disease, or stroke, and provide them with lifestyle interventions or preventive medications. 
  • Discovering new insights and innovations from large and complex data sets, such as electronic health records, clinical trials, or genomic databases. For example, ML can help find new associations between diseases, genes, drugs, or environmental factors, that can lead to new hypotheses, diagnostics, or treatments. ML can also help design and conduct more efficient and effective clinical trials, by selecting the most suitable participants, endpoints, and outcomes.

However, implementing ML in health care is not without challenges, such as: 

  • Ensuring the quality, reliability, and security of the data and algorithms used for ML. For example, ML models need to be trained and tested on high-quality, representative, and unbiased data, to avoid errors, inaccuracies, or discrimination. ML models also need to be transparent, explainable, and accountable, to ensure that their decisions can be understood, verified, and challenged by humans. ML models also need to be protected from unauthorized access, manipulation, or misuse, to ensure the privacy and safety of patients and providers. 
  • Integrating ML with existing health care systems, workflows, and regulations. For example, ML models need to be compatible and interoperable with the current health care infrastructure, such as electronic health records, devices, or networks. ML models also need to be aligned and coordinated with the current health care processes, such as diagnosis, treatment, or billing. ML models also need to comply with the current health care standards, such as ethics, quality, or legality. 

To overcome these challenges, health care organizations need to adopt a strategic and collaborative approach to ML, involving multiple stakeholders, such as clinicians, researchers, patients, regulators, and vendors. One way to do this is to leverage the expertise and resources of external partners, such as staff augmentation companies, that can provide specialized talent and solutions for ML projects. 

Staff augmentation is a flexible and cost-effective way to hire skilled and experienced professionals, such as data scientists, ML engineers, or AI consultants, on a temporary or project-based basis, to supplement the existing staff of an organization. Staff augmentation can help health care organizations to: 

  • Access the latest technologies and methodologies for ML, such as deep learning, natural language processing, or computer vision, that may not be available or affordable in-house.
  • Accelerate the development and deployment of ML solutions, by reducing the time and cost of hiring, training, or managing permanent staff, or outsourcing entire projects. 
  • Enhance the quality and performance of ML solutions, by benefiting from the diverse perspectives, backgrounds, and expertise of external professionals, who can bring fresh ideas, insights, and innovations.
  • Adapt to the changing needs and demands of ML projects, by scaling up or down the number and type of professionals, as required, without affecting the core staff or operations. 

One example of a staff augmentation company that specializes in ML and AI is Global Triangles, a global IT company that offers nearshore and staff augmentation services, as well as AI integration and automation, e-commerce solutions, and custom software development. Global Triangles has a team, with expertise in various fields, such as data science, ML, AI, web development, cybersecurity, etc. 

Cynthia Martinez, Head of HR at Global Triangles, said: “I think that ML and AI are amazing technologies that can make a huge difference in health care, by making diagnosis and treatment faster, better, and cheaper, and by finding new insights and innovations that can lead to new cures and therapies. I am fascinated by the potential of ML and AI to improve the health and well-being of people around the world.” 

As Philip Hunter wrote in his article “AI and machine learning in healthcare: diagnosis and research”  “AI and machine learning (ML) in healthcare have a long history dating back to the 1980s that started with rule-based systems followed by hierarchical clustering and linear regression algorithms. However, neither the algorithms nor the computers themselves were yet sufficiently powerful to enable effective ML. Over the past 5 years though, advances in computational power combined with new algorithms based on neural network techniques have enabled enormous progress in ML, which is now impacting on many fields, including research and healthcare. Two fundamental capabilities of ML make it particularly interesting for diagnostics and research: the abilities to detect weak signals amidst noise and to enhance low-resolution images. However, there are also concerns about potential for abuse with potentially fatal consequences for applications in science and healthcare”. 

AI and ML are indeed transforming health care, and we can expect to see more examples of their applications and benefits in the near future. However, we also need to be aware of the challenges and risks that they pose, and to adopt a responsible and ethical approach to their development and use. By doing so, we can harness the power of AI and ML to improve the health and well-being of ourselves and others.