Where patient information goes through apps/ systems that use artificial intelligence that process the information and guide or provide further insight into health or even used to predict when there may be an issue further down the line.
For AI:
One could argue that AI technologies, such as machine learning algorithms, have demonstrated remarkable capabilities in analysing medical data and images, leading to improved diagnostic accuracy and timely identification of diseases and issues (AI is increasingly used in radiology for prevention and prediction. E.g. Keith Tucker and Vipin Asopa’s work - use of AI to predict potential issues with newly replaced joints, such as hip or knee replacements, before they become visible to the human eye. This early detection enables timely intervention and preventive measures, ultimately improving patient outcomes and reducing the need for additional surgeries or treatments.).
Similarly, AI-driven surveillance systems and predictive models enable early detection of disease outbreaks, epidemiological trends, and individual health risks, facilitating proactive public health interventions and disease prevention strategies.
Junior doctor’s jobs are heavily admin-oriented. AI-driven automation and predictive analytics may streamline administrative tasks, optimise resource allocation, and reduce healthcare costs, ultimately improving efficiency and accessibility of healthcare services.
AI-powered precision medicine holds promise in tailoring treatment plans based on individual patient characteristics, genetic profiles, and real-time data insights, potentially leading to more effective and targeted interventions.
AI technologies, such as virtual health assistants and remote monitoring devices, could possibly empower patients to actively participate in their healthcare management, improve health literacy, and foster greater engagement with healthcare providers.
Against AI:
Concerns about the ethical implications of AI in healthcare include issues related to data privacy, consent, bias in algorithmic decision-making, and potential breaches of patient confidentiality.
One could argue that AI technologies may exacerbate existing healthcare disparities by disproportionately benefiting privileged populations with access to advanced technology, while marginalising underserved communities with limited resources or digital literacy.
At the end of the day, AI is not human and won’t be able to be empathetic/ pick on micro expressions / subtle clues / give that human touch which can change a patient’s diagnosis, health experience and so on. Thus, there is potential depersonalisation of healthcare encounters and erosion of the patient-doctor relationship in an increasingly technology-driven healthcare landscape, ultimately demonstrating the irreplaceable value of human empathy and communication.
Many question the reliability, safety, and robustness of AI algorithms in critical healthcare decision-making processes, citing instances of algorithmic errors, biases, and unintended consequences that may compromise patient safety and quality of care.
Widespread adoption of AI in healthcare may lead to job displacement or deskilling of healthcare professionals, particularly in roles that involve routine tasks or data interpretation, raising concerns about workforce dynamics and job security.
With any hot topic, sometimes you can easily structure an answer by using the four pillars of medical ethics e.g. for using AI in healthcare delivery: under the pillar of beneficence it is clear that artificial intelligence can enhance healthcare delivery, including enhancing diagnostic accuracy, possible detect diseases earlier than trained professionals, arguably being able to improve patient outcomes and promote timely interventions. Under the pillar of non-maleficence doctors would be wary of using AI as there are potential issues with data, security and bias, which can also make the public wary of doctors who promote AI and impact the doctor-patient relationship, additionally AI lacks the ability to empathise like humans and can negatively impact patient experiences. Under the pillar of justice, easy AI healthcare access e.g. via apps could make healthcare and help more accessible and empower patients but could also leave those who use technology at a disadvantage. Under autonomy, one could argue that AI could help personalise healthcare and empower a patient in making informed decisions.
