Artificial intelligence (AI) is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.
As a matter of fact, AI has become a key factor of production across many sectors with the potential to introduce new sources of growth and change the way work is done across industries. While healthcare has been slow to adopt and apply AI across the continuum of care, this is changing fast. Right now, AI-enabled solutions are being applied in many aspects of healthcare including in areas that were considered almost impossible just a few years ago.
What is even more impressive is that many applications of AI in healthcare have led to improved efficiencies and better outcomes, way more than was originally expected. However, there have also been lots of unsubstantiated claims about the potential of AI in healthcare over the years which have not materialized as hyped. Well, at least not yet.
For starters, the basics of AI include understanding the various subfields of AI, such as machine learning, deep learning, natural language processing, computer vision, image recognition among other fundamental techniques. Additionally, it is crucial to comprehend the AI concepts and building blocks that underlie AI, including neural networks, algorithms, and data structures.
Understanding the distinction between Narrow AI (Weak AI), designed for specific tasks, and General AI (Strong AI), possessing human-like cognitive abilities, is also fundamental. Most AI applications in healthcare and life sciences fall under Narrow AI.
We have covered some of these in an earlier post on understanding the basics of AI, which we highly recommend you read through to have stronger foundations. If you are interested, you can take some of our courses to gain even deeper understanding and build stronger foundations to better navigate some of these applications.
In the ever-evolving landscape of healthcare and life sciences, AI stands as a transformative force, reshaping the way clinicians and life scientists approach their work. As we delve deeper and gain better understanding of some of the fundamentals, we will more easily be able to unveil and appreciate the immense potential it holds for revolutionizing patient care, diagnostics, and research.
Here, we will look at some of the AI applications in healthcare and share some thoughts on how AI is helping and could further help clinicians deliver improved care to more patients at more affordable costs. We will consider some of the ways professionals who work in various facets of healthcare can begin to think about the recent applications in medical practice and research and what the future might hold as things unfold.
AI Replacing Medical Professionals
There is this apparent mix up of tasks and jobs which has led to some misunderstanding about AI leading to loss of jobs. While AI does lead to loss of jobs, it is usually the right way many people see it and most media represent it. This has also contributed to the reluctance in some fields as regards adopting AI solutions with people fearing AI displacement. We have discussed this more extensively in another post, explaining the difference between tasks and jobs and emphasizing that while AI may be good in some tasks, AI remain far from doing most of the tasks that constitute most jobs especially in healthcare.
Hence there is hardly any reason to fear that “AI will take over the job of a medical professional” even as we deploy AI systems to augment the efforts of clinicians and medical professionals to derive efficiency gains and achieve even better outcomes.
Use Cases for Clinicians
Clinicians can harness AI for diagnostic assistance, predicting patient outcomes, and recommending personalized treatment plans. For instance, IBM’s Watson Health analyzes vast medical literature to support clinicians in making data-driven decisions.
For now, we will quickly go over some of the current applications of AI in healthcare. We will be going deeper into some of these areas and more in subsequent posts and resources in greater detail.
Diagnostic Assistance
Among the areas AI has found extensive use and lead to realization of massive efficiencies is in many diagnostic procedures and decision support systems. Some specific applications of AI in diagnoses include:
Medical imaging: AI can analyze medical images such as X-rays, CT scans, and MRIs to detect patterns and abnormalities that may be difficult for human radiologists to identify.
Diagnostic Insights: AI can analyze patient data such as medical records, lab results, and genetic information to assist in diagnosis and treatment planning.
Administrative Assistance
AI programs have been assisting with everyday administrative tasks like billing, scheduling, and data entry.
AI-enable digital scribes have even been shown to be acceptable, accurate, and relatively easy to use and can alleviate a lot of burdens, decrease burnout, and make things better for both physicians and patients
Personalized medicine
AI can analyze patient data to develop personalized treatment plans that take into account individual patient characteristics and medical history.
Patient Monitoring
AI-enabled systems can help to monitor patients who have left the hospital, and can help detect high-risk patients in particular. For example, a chatbot can check-in on a patient on a daily basis and track the patient’s progress, and can alert clinicians if there are any deviations in their path to recovery.
AI in Biotech and Life Sciences
In life sciences, AI has found many impressive applications with tremendous results. For AI has emerged as an important “catalyst” propelling drug discovery, genomics research, and biomedicine investigations to unprecedented speeds.
The ability of AI systems to process massive datasets, unraveling the complexities of diseases and of biology have accelerated various aspects of medical research, leading to breakthroughs in understanding diseases, identify potential new drug targets and development of novel therapies.
Let’s look at some of these briefly.
Drug Discovery Acceleration
Molecule Screening: AI-driven platforms like BenevolentAI analyze vast chemical databases to identify potential drug candidates, streamlining and speeding up the initial stages of drug discovery.
Predictive Modeling: Insilico Medicine utilizes AI to predict the effectiveness of drug compounds, reducing the time and resources traditionally required for preclinical trials.
Predicting Molecular Binding: A prime example is Atomwise, a company utilizing AI for drug discovery. By predicting the binding of small molecules to proteins, Atomwise accelerates the identification of potential drug candidates.
Genomics and Personalized Medicine
Genomic Sequencing: AI plays a crucial role in interpreting vast genomic datasets, aiding researchers in understanding genetic variations and their implications for personalized treatment.
Clinical Decision Support: IBM’s Watson for Genomics analyzes genomic data to provide clinicians with insights into potential targeted therapies tailored to an individual’s genetic makeup.
Biomedical Research Advancements
Disease Prediction: AI algorithms analyze diverse data sources, including patient records and molecular data, to predict disease risks and aid in preventive interventions.
Biomarker Discovery: Platforms like Tempus use AI to sift through clinical and molecular data, accelerating the discovery of biomarkers for early disease detection.
Drug Repurposing and Optimization
Repurposing Existing Drugs: AI models identify existing drugs with the potential to treat new conditions, expediting the drug development process.
Optimizing Treatment Protocols: AI algorithms analyze patient responses to medications, optimizing treatment plans based on real-world data.
Robotics in Life Sciences
Laboratory Automation: AI-driven robotics automate repetitive laboratory tasks, enhancing efficiency and accuracy in experiments.
High-Throughput Screening: AI-powered robotic systems facilitate high-throughput screening of compounds for drug discovery.
Challenges and Considerations
Like any other tool used in healthcare there are questions and considerations that need to be taken care of to ensure safe and effective use. AI by its nature comes with even more significant challenges to adoption and adequate use for clinical practice and medical research. Some the challenges more peculiar to healthcare include:
Ethical Considerations:
While AI brings remarkable advancements, ethical considerations abound. There is a need to ensure that AI developments align with the principles of responsible and compassionate healthcare. Striking a balance between innovation and ethical practices is paramount to ensure responsible use in patient care and research.
Data Privacy and Security:
As AI relies heavily on data, safeguarding patient information is critical. Robust measures must be in place to protect sensitive healthcare and research data from unauthorized access.
For example, there are privacy concerns, including how AI works against or in tandem with HIPAA rules.
Data confidentiality with AI is a rapidly evolving and complicated landscape. However, there is need to continue testing and deploying secure models while privacy concerns continue being resolved.
Risk of Errors:
There are major concerns about the use of AI applications and the risk for errors which result from many factors including data bias, poor algorithms, degradation of the program over time, and “hallucinations,” wherein the AI will just make up answers without facts.
There have been cases of diagnostic errors in healthcare and the last thing anyone wants is for AI to increase these already alarming rates with worse consequences.
There are other questions regarding errors and malpractice such as whether AI may increase or decrease the risk for medical malpractice that leads to lawsuits. And what happens if an AI model makes the correct diagnosis but a physician erroneously overrules the AI’s recommendation?
While AI could practically help reduce chances of errors in day-to-day medical care, at the end of the day, it is always going to be the responsibility of the clinician to ensure adequate and responsible use.
Starting and Moving Forward with AI
There are lots of educational resources out there on AI and “AI in Healthcare” that offer comprehensive insights, including courses, webinars, or other resources where clinicians and life scientists can learn more about AI.
We highly recommend some of the “living” courses developed by Medneed and partners on AI and other related health and biotech topics. These courses are mostly developed by professionals working in various health specialties and are constantly updated with the latest information to ensure that clinicians and life scientists remain up-to-date with AI advances and other essential progress in healthcare.
Continuous learning is essential to staying abreast of AI’s evolving applications. Embracing a mindset of continuous learning is essential for clinicians and life scientists. Staying informed about AI advancements ensures readiness for future innovations and opportunities.
Collaboration Opportunities
The synergy between AI experts and healthcare professionals is important to ensure that due every necessary clinical and ethical consideration are made as we tap AI to improve healthcare and patient outcomes. Collaborative efforts can lead to innovative solutions, bridging the gap between cutting-edge technology and real-world healthcare challenges.
Interested professionals can also reach out and follow Medneed to get publications about potential collaboration opportunities as they come up.
Final Thoughts
At its core, AI refers to the capability of machines to imitate intelligent human behavior. These machines are programmed to analyze data, learn from it, and make informed decisions.
AI is revolutionizing healthcare by improving diagnosis, treatment, and patient outcomes among many other immense benefits and efficiency gains.
We are witnessing ever-increasing ways AI is being utilized in life sciences, including drug discovery, genomics, and biomedical research.
As AI continues to evolve, trends like Explainable AI (XAI) and Federated Learning promise more transparency and decentralized data processing. These trends are poised to shape the future of AI in healthcare and life sciences.
The fusion of AI with healthcare and life sciences opens doors to unparalleled possibilities. As clinicians and life scientists navigate this AI-powered landscape, the commitment to ethical practices, data security, and continuous learning will drive success. The journey towards a future where AI enhances patient care and catalyzes groundbreaking research is indeed an exciting one. We should embrace this future and control it for maximum benefit to humanity and healthcare in particular.
We are encouraged to continue exploring AI applications in our respective fields and embrace the opportunities it presents for innovation and improvement.