Artificial Intelligence (AI) is a field that brings together both computer science and substantial datasets to enable problem resolution. This sector has experienced tremendous growth recently, with more financing and research, and the demand for it skyrocketed last year. ChatGPT-like AI has gained the attention of mainstream media and has become a market sensation. Everyone was curious about this new technology and wanted to try it. By the 1950s, we had a generation of scientists, mathematicians, and philosophers culturally assimilated to the concept of artificial intelligence in their minds. In particular, Alan Turing, a young British polymath at the time, and known today as the father of artificial intelligence, investigated the mathematical viability of artificial intelligence. Turing suggested that human beings solve problems through applying the knowledge at their disposal and logic; why shouldn’t machines be able to do the same? This served as the logical foundation for his 1950 paper ‘Computing Machinery and Intelligence’ (Alan Turing, 1950), in which he addressed how to create intelligent machines and how to assess their intelligence – known infamously today as The Turing Test In the years since then, artificial intelligence has advanced significantly, and the healthcare sector has begun implementing it at an early level. As an illustration, dermatologists in the USA have started implementing AI modules in their practices to assist with the diagnosis of a patient with skin cancer. These modules are considerably faster, quicker, more accurate, and more effective than a dermatologist. However, there are significant ethical concerns with the use of artificial intelligence in medicine due to the sensitive nature of medical data and the absence of global rules governing such matters.
Different Uses of AI in the Vast Field of Medicine:
Different Uses of AI in the Vast Field of Medicine: In 2023, the integration of artificial intelligence has become integral to numerous facets within the expansive domain of medical sciences. Notably, the foremost applications of artificial intelligence in medicine encompass the following key areas:
1. Disease Diagnosis
Traditional diagnostics are time-consuming and often need more experts. Machine Learning, particularly Deep Learning algorithms, have significantly enhanced the efficiency of disease diagnosis. These algorithms, capable of learning patterns akin to human experts, require a substantial volume of meticulously digitized examples to acquire proficiency. Machine Learning excels in areas where diagnostic information is digitized, such as:
● Detecting Lung Cancer or Strokes: Leveraging Computerized Tomography (CT) scans for precise identification.
● Assessing Cardiovascular Risks: Analysing electrocardiograms (ECG or EKG) and cardiac Magnetic Resonance Imaging (MRI) images to evaluate the risk of sudden cardiac death and other heart diseases.
● Classifying & Identifying Skin Lesions: Utilizing machine learning for the classification and identification of skin lesions based on detailed images.
● Identifying Diabetic Retinopathy: Examining eye images to identify indicators of diabetic retinopathy.
Example Specific Uses of AI in Disease Diagnosis (Source:
https://www.datarevenue.com/en-blog/artificial-intelligence-in-medicine)
2. Accelerated Drug Development
Drug development is notoriously time-consuming and expensive. With low success rates and high costs, even minor improvements can save companies millions of dollars. Machine learning can help make many analytical procedures in drug development more economical and effective, which will speed up, reduce costs, and improve the accuracy of the drug search. All major stages of drug development have already seen the successful application of AI, with some of its most noteworthy accomplishments being as follows:
Drug development process showing the application of AI at each stage (Source: Victor et al., 2021)
3. Personalized Patient Treatment Approaches
Personalized treatment is challenging due to the variability of patient responses to treatments. Machine Learning automates the complex statistical work, identifying factors that influence treatment choices. By cross-referencing similar patients, algorithms predict probable treatment responses, assisting doctors in designing effective treatment plans.
4. Advancements in Gene Editing
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), specifically, CRISPR-Cas9 gene editing is a breakthrough, but guide Ribonucleic Acid (RNA) selection poses challenges. Machine Learning models excel in predicting guide-target interactions and off-target effects, facilitating in the accelerated development of precise guide RNA for editing human Deoxyribonucleic Acid (DNA).
Ethical Considerations Involving the Usage of Artificial Intelligence:
In the ever-evolving landscape of AI in healthcare, ethics are the guiding moral compass for responsible practices. Now, let’s explore the critical ethical facets influencing this intersection. Key considerations include:
1. Privacy Concerns
It is paramount to protect patient confidentiality and sensitive medical data. To ensure the security and ethical handling of personal health information, developers should ensure that AI applications adhere to robust privacy measures.
2. Bias and Fairness
Achieving equitable healthcare outcomes requires the elimination of bias in AI algorithms. During the development process, developers must work hard to eliminate biases that may disproportionately influence particular demographic groups. By fostering trust among patients and promoting equity in healthcare, fairness in AI contributes to unbiased decision-making.
3. Accountability and Transparency
AI systems must be held transparently and firmly accountable. For this, policymakers, developers, and healthcare organizations must clearly define their roles in the event of system errors or unfavourable results. Transparency is essential to fostering public confidence in AI systems and ensuring that decisions made by AI are understandable and supported.
4. Informed Consent and Patient Autonomy
Transparency is necessary when it comes to AI applications in healthcare to respect patient autonomy. Policymakers and healthcare providers must ensure that patients are well-informed about the use of AI in their care. By obtaining informed consent, patients can understand how AI can be incorporated into their medical treatment and make informed decisions.
5. Impact on Employment and Workforce
As AI transforms healthcare workflows, healthcare professionals’ roles and employment must be considered. Policymakers should anticipate and address workforce challenges, ensuring that AI complements rather than displaces human expertise. A sustainable evolution of healthcare systems requires balancing technological advancements with workforce needs.
Ethical and Legal Challenges Involved with the Usage of AI in Healthcare (Source: Nitesh et al., 2022)
Regulations in the Implementation and Usage of AI in Healthcare:
The United States Department of Health and Human Services (HHS) and the Food and Drug Administration (FDA) are two government organizations that have lately started creating rules on the application of AI in the healthcare sector. The FDA released a discussion paper in 2019 that included a proposed regulatory framework for changes to AI-based software as a medical device (SaMD). The FDA defines AI-based SaMD as software “intended to treat, diagnose, cure, mitigate, or prevent disease.”. In the agency’s discussion paper, the FDA “asserted” that it will make sure AI-based SaMD “will deliver safe and effective software functionality that improves the quality of care that patients receive.”. In addition, the United States is not the only country attempting to monitor and regulate the use of artificial intelligence in healthcare. Similar recommendations and guidelines have been produced by other nations, including South Korea, China, Japan, and others, to guarantee patient safety. A comprehensive report from the World Health Organization (WHO), published in June 2021, talks about the use of artificial intelligence in healthcare, which offers six guiding principles for establishing AI regulations for all industry stakeholders to consider: safeguarding autonomy, encouraging safety, maintaining openness, encouraging accountability, guaranteeing fairness, and advancing sustainable artificial intelligence. As the use of artificial intelligence within health care grows in prominence, regulatory agencies in the United States and elsewhere are considering more robust regulations in order to protect patients and ensure the quality of their care.
Conclusion:
In essence, AI in healthcare is a game-changer, promising better patient outcomes, increased efficiency, and accelerated research. The collaborative efforts of healthcare providers, software developers, and researchers continue to drive innovation, challenging existing regulatory frameworks. The FDA is actively working to balance innovation and public health protection, but several questions linger. Navigating this dynamic landscape requires evolving policies and, potentially, legislative action to address regulatory uncertainties. As we chart the course forward, the synergy between ethical considerations, robust regulations, and ongoing collaboration remains paramount. The transformative potential of AI in healthcare is vast, but ensuring its responsible and ethical integration is the key to unlocking its full benefits for patients and the entire healthcare ecosystem.
Saurish Kapoor
About the author: I’m Saurish Kapoor, a 15-year-old high school sophomore, passionate about advancements in the field of STEM, through the development and usage of Artificial Intelligence (AI) to answer biological questions. I’m fascinated by the seamless integration of AI and Machine Learning into patient diagnosis and treatment, leading to improved outcomes and enhanced health. With a new era of technology arising, my goal is to be at the forefront of this industry, by gaining hands-on experience working under professionals, and conducting research for the betterment of the society we live in today.
2 Comments
This article really opened up my eyes to the idea of AI in the medical field. I think you encapsulate AI’s potential in the medical field perfectly, especially with accelerated drug development and personalized patient treatment approaches. I am excited to see what the future of AI in the medical field is, and I am excited to see the future of your writing as well Mr. Kapoor!
Hi Saurish my brother👍🇮🇳😂😃