Future Of Medicine (Will Artificial intelligence Replace Doctors in the Future)

22 Sept

Written By Hashaam

Abstract

The research I have conducted as part of this project analyses the possible potential impact on the future of medicine and how AI will play a role in diagnosing diseases as well as independently carrying out simple to complex surgical procedures. There will be a specific focus on whether AI (Artificial Intelligence) could possibly replace doctors in the coming decades. We also touch up on areas such as AI ethics, how they complement and benefit to the medical industry, if they are even fit to be allowed anywhere near patients. It is structured based around the projects revised objectives. Furthermore, using secondary qualitative methods to obtain data this piece of work critically evaluates each source and how this and deceive compelling arguments of all possible sides of the arguments. My conducted research synthesises academic literature, opinion bases arguments from unbiased experts and case studies to deeply analyse the new gen of AI and how it could be a complementary helpful and strong tool to enhance the world of medicine. The chosen approach allows for in-depth analysis and evaluation rather than the usage of primary data collection. Furthermore, most importantly according to my outstanding research AI is one of, if not the most advances and effective technology that helps numerous and multiple industries including the pharmaceutical industry which is heavily supported by AI to increase organisational growth resulting in heavy success. On the other hand, Ai does not bear the emotional aspect to replace human doctors from any industry even in the foreseeable future.

Introduction

Background of the topic

Through my heavy interest in medicine and high usage of AI throughout my daily life I ought to wonder before and during buy time e period how the future of medicine will look like and if robots will be a sure thing in the future. AI is currently one of the most advanced technologies created by humans and one of the most powerful tools used across the globe in many professions including engineering and retail. We usually see the usage of AI to increase personal growth of companies and certain success. Even though in recent years, most pharmaceutical industries adopted some advances technologies in this case we are talking about AI to develop effective medicine for its consumers, research has proven to be a good way to deliver improvements as well as a handful of opportunities for clinical training and basic healthcare training. Additionally, as one of my research tasks to outline the usage of AI in certain medical industries as per a global survey which is conducted in the year 2024. Almost 70% of the pharmaceutical industry has adopted AI technology in their chosen and current area of research (statista.com).

As one of the newest highly emerging technology trends across the wider globe though in medicine AI has seemed to be a success especially in the healthcare sectors, pharmaceutical industry and diagnosing sectors such as diseases viruses and cancer. The use of this advances intelligence service we have seen a rise in data analysis and enhancements in accuracy of the overall medical imaging, as well as seeing supported surgeries such as robotic surgeries and it is even contributing to the design of personalised medicine. However, these innovations bring a huge amount of uncertainty.

This begs the question within the population. As AI continues to grow and become more advanced, could AI operated machines programmed to carry out surgical procedures replace surgeons, could AI and all its non-existent technical flaws replace doctors in diagnosing certain if not all possible diseases by learning and storing all possible content. As we know AI would not forget information as a human does instead it is stored in their hard drive easily accessible for the technology to pick up. Or the final question, is AI merely a supportive system that enhances what doctors can already perform or carry out.

Justification of the topic

The main issue is that in recent years, most of the pharmaceutical industries have adopted some advanced technologies such as AI, and they have increased their dependency on these technologies (Sultana et al., 2023). However, because of this reason, the pharmaceutical industry sees an effective future through AI, while AI technology can replace doctors within the industry (Lee and Yoon, 2021). Most importantly, as per Statista, the global pharmaceutical industry is projected to invest around 13 billion dollars by 2032 to adopt AI technology within the industry (statista.com, 2023). This research aims to significantly identify the potential impact of AI on the future of medicine, with a specific focus on whether AI will replace doctors. Moreover, to effectively justify the overall research and examine the impact of AI, the researcher will analyse different types of researchers' perspectives regarding the topic.

Aim

My extended project aims to critically evaluate further points and answer these questions through backed up research and a wide range of academic sources and medical literature. This piece of work evaluates the current most advanced and up to date capabilities usage of Artificial Intelligence within the healthcare world and examines where human input remains and how AI could manifest their way into certain procedures or if they will simply remain a tool. It is important to acknowledge human input is still necessary when it comes to life changing detrimental procedures and other possible factors where lives are being faced. The project further aims to unfold topics such as precisely diagnosing patients, usage of AI in robotic surgeries such as hip replacements, the significance of the relationship between doctors and their patients as well as the ethical dilemmas surrounding the topic of data collection and data use. In this project I do not seek to argue that one extreme side or another but further break down all possible angles on this contradicting discussion however explore the brutal and truthful reality: What are we expecting from the future of medicine, will AI possibly replace Doctors?

Objectives

To evaluate the outstanding ability of AI in terms of diagnosing diseases and treatment capabilities of AI compared to doctors.
To analyse the significant pros and cons of Artificial Intelligence being used in Pharmaceutical and clinical sectors
To further gauge ethical, societal and legal concerns and challenges around AI being implemented in medicine.
Recommending how AI can be integrated into the healthcare system without replacing doctors

Methodology

This Extended Project Qualification adopts a secondary qualitative research methodology. This means I extracted bits of data that were already published to back up my claims and further break down my points. Unfortunately I was met with challenges and under my circumstances I could not go out my way to conduct much primary research, despite a fractional proportion of data that includes interviews, it focuses on gathering and analysing information from numerous multiple sources including academic journals, governmental publications, peer-reviewed articles as well as industry reports. These sources were identified and extracted from famous databases such as:

Google Scholar

PubMed

ScienceDirect

Furthermore, along with publications from outstanding institutions well known for their healthcare services and reliability such as NHS, WHO and statista.

Priorities take place in the understanding of human experiences alongside the perspective of given experts in the field; contextual meanings related to the role and significance of AI in healthcare. These philosophical views support the critical evaluation and deeper understanding/ reflection on numerous issues mainly the highly contradicting issues such as bonding with the patient, ethical judgements as well as the emotional sectors with things such as empathy, compassion, passion and understanding from one human to another in certain situations.

As the essay progresses, we can see that there is an approach in analytical rather than experimental. The cover of key themes and areas such as the diagnostic efficiency, empathy in patient care, risks opposing ethical norms alongside the fear of technical limitations, are thoroughly explored through close reading and comparing existing studies through better judgement. A few things to look out for in these sources which have a detrimental effect on my research and its analysis, and these are things such as the credibility of the sources, relevancy and recency. There is a particular attention to ensuring a balance of different perspectives which reduces any one-sided arguments and bias.

The outstanding considerations were maintained throughout by avoiding the use of unverified or biased sources. All references were cited using the Harvard referencing style (for no reason except personal preference).

AI (Artificial Intelligence in diagnosing and Treatment):

Due to Ai’s ability to professionally fixate itself onto diagnostics and treatment marks one of its significant traits and allows it to dominate in the medical industry. We see high rises of AI in these areas constantly. AI has integrated its way into clinics across the world in many different countries as medicine rapidly advances therefore being posed within these settings for assistance is a key attribute. Furthermore, in some cases we can see that humans and AI often compete where one outperforms the other. So, for AI in these settings we can receive a sped-up version of judgement in decision making. So, moving forward this section evaluates the usage of AI in diagnostics and surgical procedures including how it is used and why it is used instead of other remarkable methods to carry out these tasks. Moving on we will conclude whether these advances could render doctors and cause doctors becoming obsolete.

One reason why AI is seen as highly effective is its strong sense of ability to to consume large volumes of data further analysing it all whilst maintaining a high velocity. From our human understanding of the field of medicine, every second could possibly count within the clinics and surgeries therefore the quick break down of huge amounts of data could possibly increase efficiency therefore allowing more productivity. Through thorough research we see high advanced technologies such as the Convolutional Neural Network (CNN’s) have been developed to analyse X-rays, MRI’s, CT scans and ultrasounds with almost accurate precision. (e.g.) A landmark study credits go to Leibig et al. (2022) found that AI Algorithms outperformed experienced radiologists in detecting early-stage lung cancer on CT scans, especially when the lesions were smaller than 10mm. These models were more efficient as they spent less and reduced time reviewing each scan therefore resulting in the improvement of workflow in overstretched clinical environments.

Following on from the previous point, to further support the argument looking at what Ahmad et al. (2021), CNN Powered diagnostics have shown potential to reduce a 20-minute scan carried out manually to just under a minute with equal if not possibly higher accuracy. This speed not only increases efficiency but as we know how piled up and over flooded surgical clinics and standard clinics can get with patients the usage of this intelligence device (CNN) could allow for a better flow of patients meaning more patients could be seen and treated by doctors. This can be seen as a crucial benefit to the national health systems especially those with staff shortages. Ethically speaking, viewing more patients could be seen as better therefore people and professionals may argue for the usage of AI specifically pointing out the CNN Technology in diagnostics.

Moreover, AI has advanced from image analysis into predictive diagnostics. Meta-Analyses (published in 2025) demonstrate that generative AI (a different form of artificial intelligence) can predict oncoming diseases using numerous data mostly the using past, current and family records with a staggering 85-92% accuracy in a controlled clinical setting, however this is dependent of the conditions and quality of data which could alter accuracy. Generative AI can pick up early signs of chronic diseases and recommend insightful and helpful remedies and certain measures as well as highlight patient risks before the occurrence of rising symptoms. This is something that qualified skilled physicians may miss without extended time and high-level, time-consuming tests (Wheatley, 2024.)

Even so, usage of AI is not only restricted to clinical aspects yet alone diagnostics. Further studies have proven the successful use of advanced technological intelligence service in other sectors within the medical world. For instance, we can see that the field of surgeries has undergone influence from AI specifically taking a deeper look at orthopaedics. A huge uprise in robotic surgeries has flooded the field with its significance and highly assisted complementary skills. AI is a powerful tool paired up with doctors this makes surgeries easier and less risky. According to Nich et al. (2022), these robotic systems use current real time data from patients and anatomically map out surgeries personalised to clients and patients through predictive modelling to ensure low risk precision placements of implants during the highly skill dominant joint replacement surgeries. Due to the assistance this tends to reduce variability and reduced recovery time minus the possible effects of infections or common misalignments.

To further argue the previous point, Iftikhar et al (2024) notes that AI-assisted robotic systems have surpassed human surgeons in precision, consistency and pain reduction during orthopaedic surgeries. Robotic systems used in hip replacements can repeatedly adjust to a patient's muscle tension and bone density (this is seen as near to impossible for the average surgeon unaided).

Though still, these advantages do not equal to the complete independence from human doctors. Sezgin (2023) outlines how though AI surpasses standard expectations in data-driven and repetitive tasks it lacks the emotional intelligence a key one being empath as well as lacking ethical reasoning required in overall complex or emotionally complex cases. This simply means those human beings who could possibly be on the spectrum which requires emotional and professional intelligence as well as a different approach to certain situations at many different points throughout the procedures. Diagnosis is not entirely image analysis, it very often involves holistic patient assessments, understanding complexity suited to human emotions. These are key places where AI lacks the ability to equate with doctors.

Combi et al (2022) highlights many high-performing AI Models operate as “black-boxes”, offering accurate outputs without transparent logic, in Medicine the idea around reasoning and sensible justification are legally and ethically required or expected so another limitation of AI is the level of explainability. The ‘why’ aspect behind decisions is highly relevant in all cases. Though we see enhancements in the ‘thinking and reflection’ process of AI which is the thorough breakdown of the thinking process of AI generated answers this is still an undeveloped area and seen as a flaw.

There are also documented failures where AI systems made dangerous and inaccurate suggestions when applied in real life hospitals. These suggestions were often biased or even technical issues such as incomplete training data, computer or system bugs or simply a lack of knowledge all which could have been caught or overruled by a human professional. (Van Baal et al, 2023)

Ultimately, to conclude, AI’s diagnostics and surgical abilities offer a strong and powerful basis to complement medical expertise but are not yet, or possibly never will be a replacement of doctors. This is patients and life's we are talking about. AI can be seen as a supportive tool, but the judgement and care of trained medical professionals are more reliable than AI.

Advantages of AI in Clinical and Pharmaceutical Practise

Despite the public discussions around AI in medicine focussing on whether it has the power to overtake doctors in the foreseeable future; a clear reality is that AI brings large amounts of benefits to clinical and pharmaceutical sectors. There is a clear improvement in accuracy and reduction in delays whilst serving a wider range of access to treatment all while cutting costs. This section explores how AI seems to deliver these advantages currently and possibly in the future, how and why these improvements strengthen the case of a medical collaboration between AI and humans rather than the replacement. AI is not the enemy or outlier, and this is important to understand. AI was not made to overtake humans but to help and aid them and one of these ways is its aid in medicine.

One evident advantage of AI is the speed and efficiency as seen before its ability to rapidly analyse data producing insights which typically takes hours for a team of trained professional medics. This is highly critical in time sensitive fields like: oncology, emergency medicine and critical care. Seen in the 2025 industry analysis that through the implementation of AI tools have reduced average diagnostic times by over 40% in hospitals that have adopted full electronical AI supported workflows (Weilhams et al. 2025). This reduces the backlog and a huge amount of pressure of healthcare workers who are often highly pressurised due to intense shifts. This could lead to better productivity and higher quality output from workers.

Tools such as IBM Watson, DeepMind’s AlphaFold and AI driven pathology platforms outlines the superlative accuracy in the identification of cancers, cardiac conditions and neurologic anomalies comparatively being up against a standard GP (general practioner) (Wheatley. 2024). This brilliantly shows AI can be used as a second set of eyes to outline errors or anomaly results which may be accidentally overlooked by specialists. Overlooking could sometimes bring heavy consequences so double and triple checking is a key. More reason to implement AI.

Additionally, AI helps revolutionise the discovery of drugs helping unfold new drugs in the market. Traditional drug development is lengthy and costly though with the help of AI pharmacists could develop new drugs at a faster rate. On average a standard development of a medical drug could cost up to billions of dollars and take up to a decade long (LSE. 2025), all this to bring a single drug to the market. Adaptive models of AI simulate molecular behaviours and estimate a response of a drug with a fraction of the cost and time. Experts may argue that there is no harm in trying these models as if it is a failure, it would not be brought to the market though if it is a success then a newly developed and effective drug has been introduced to the market put in place to help people. Tiwary et al. 2023 argues that platforms powered by AI reduces the early-stage drug screening process over 60% cutting back on millions of dollars being spent which pharmaceutical companies could spend elsewhere.

Personalised medicine typically helps fight off diseases faster as it is created with the sole purpose to complement the anti-functions of a disease. Analysing an individual's genetic profile alongside other required data such as medical records and overall health data, AI can tailor treatments that are specific to each disease or even individuals. For many especially the pharmaceutical industry this could be seen as a massive accomplishment and advantageous as this reduces the likelihood of adverse drug reactions and provide accurate detailed information such as dosage maximising its effectiveness for the good. Alowais et al. 2023 highlights how AI tools are being used to match cancer patients with targeted therapies based on individual genetic sequencing. This would be very hard to manually achieve with no given rate of success.

Beyond personalised treatment there are more non-medical factors within a hospital such as admin word that could be done or assisted by AI. These typically include managing stock levels, automate administrative tasks or predict patient admission rates. By reducing workload for medical staff, they could feel more focused on clinical care and treating patients. This improves efficiency within clinics. Chatbots are a known and reliable way of implementing AI though they are ongoing through trials to support patient queries (most of the time these are very similar repeated queries that could be dealt with programmed chatbots). Furthermore, appointment books could have never been easier. Other repeated patient-based tasks such as prescription renewal could look to be dealt with AI in the future too. Haapalainen 2025 believes the patient cantered AI tools could improve patient experience by offering interactive education and even follow up care.

AI are programmed machines meaning they can't get fatigued unlike humans. Further external factors which may affect work drive and motivation such as stress are non-existent in computer-based programs comparatively to humans who may suffer from stress or bad days influencing their work performance or even lack of motivation. We from our own understanding can agree that doctors and even the wider healthcare workers are heavily over worked constantly. So, as a human there are boundaries and limits so AI could be seen as a stronger option within the healthcare industry due to its lack of fatigue. Burnouts are a real issue within all sort of work; AI does not suffer from these sorts of attributes. Within healthcare especially the NHS we see a shortage of staff which a grave issues, easily tackled by the existence of AI which can fill in for staff shortages. This allows the smooth running of off services and carry out treatments efficiently. Though, it is highly important to note that the availability does not equal to full independence meaning hospitals cannot set up programmed machines to check off patients for them, this process would still require a qualified doctor to approve of decision making as we know there is a lack of situational judgement and decision making in high intense situations despite AI being an operative program 24/7 working tirelessly.

Finally, as previously mentioned, modern algorithms can detect risk in individuals using data. Health devices that are (wearable) often powered by AI alerts those who need to be notified of irregular heartbeats and very early signs of infection usually overlooked. This helps in providing necessary treatment to prevent further worsening of conditions resulting in better outcomes. Wheatley 2024. Identifies there is a key shift from reactive medicine to preventative medicine. This is better as it eliminates risks of rising infections within patients leading to no problem at all rather than getting diagnosed and taking medication which is a more painful and less appeasing route to take for treatment.

In summary, AI is not just used as a convenience, but clinical and pharmaceutical industries benefit from quicker diagnostics alongside safer quicker drug development. However, from over viewing arguments and points made we can safely conclude these advantages function best when paired with the expertise with human qualified doctors so no, we cannot see a substitution within the foreseeable future.

Ethical Societal and Legal Challenges of AI in Healthcare

The continuous advancements and evolving of artificial intelligence it brings a wide range of problems across the wider population, these challenges include factors such as sets of ethical, societal and legal issues. These are highly important factors when we come onto medicine as they directly affect human lives. With trust playing a vital rode in treatment and patient care, some may neglect the usage of programmed machines to treat them and request a qualified skilled doctor. This is a huge problem within the chosen industry, and we don't normally see a trend within age groups or ethnic groups as all patients would rather receive the support of a trained human being. Furthermore, though it is unlikely, AI is currently being reviewed for safety precautions and can mess up occasionally. This would tend to strike fear into patients resulting in neglection of AI treatment. So, despite its promise in delivering high quality treatment there is a common problem about its fairness, transparency and impact on relations in healthcare settings.

Another issue which may fall under legal and ethical categories is the data privacy problem. Across many professions and sectors within the workforce there are very often ‘data breaks’ or ‘data leaks’ so we can see that idea of trust within the doctor and patient to remain strictly confidential and unexposed.

Esmaeilzadeh et al. (2021) points out that many patients are wary of AI applications due to concerns how their data is collected and used. This mistrust reduces willingness to engage with AI driven services. As mentioned previously, AI currently produces results and outcomes without giving a thought process and this has been given a name called ‘black box’. So, humans have no idea whether the thought process of certain things is ethical or not such as how to create medicine and certain sources used and to point out a specific reference Combi et al. (2022) argue the lack of transparency available between the computer and medics as in this field both doctors and patient receiving the news need to be aware about the ‘why’s’ and the ‘how’s’ as well as the rationale behind decisions, especially in life threatening cases. Patients also have a legal right to understand the cause of their symptoms and what is happening with their own body and with AI this causes legal trouble in medicine.

Rightfully so another concern is the algorithmic bias due to AI learning from historical data so it can unintentionally assess existing inequalities reflecting onto troubled patient care.

Lastly, if there is any sort of problems with the outcomes it would be unclear who is responsible for the damages. The developers? Doctors? AI itself? These things create wider problem showing that it is more significant than just patient care though treatment of patient would be the number one priory there may be unjustness in the justice system and one that is not at faulty may receive punishment. For example, it would be unjust to ban and remove AI from medicine as a whole due to a fault in the programming etc. As AI brings critical and crucial benefit to our system also improving treatment and diagnostics tremendously. This goes to shows relying on AI would be a big mistake and should remain as a complementary tool to aid doctors and pharmacists in their work.

Recommendations: Integrating AI Without Replacing Doctors

As per many researchers, it was easy to understand that without a human brain, it is not possible to operate the AI technology effectively for decision-making and strategy-making. However, to appropriately implement the AI technology in replacing doctors, some effective and appropriate recommendations are presented below,

Find the Right Partners

Finding the right partners for implementing AI technology within the pharmaceutical industry is the most effective thing because, without some effective partners, the industry can not sufficiently invest in AI implementation (Arden et al., 2021). Moreover, through a right partnership, the industry can properly maintain a balance between doctors and technology.

Leverage AI-Driven Solutions

If the industry effectively uses the technology for some specific work, such as data analysis, decision making, strategy making, and drug discovery, and doctors care for patients, then it is easy to properly combine technology and doctors.

Train Staff on AI

Moreover, if the pharmaceutical industry effectively generates good quality of staff training regarding AI technology, the industry can properly help combine the technology can doctors.

Effective Change Management

Effective change management process, the pharmaceutical industry can effectively maintain the AI technology and doctors for the industrial growth and success (Kulkov, 2021). Most importantly, by implementing effective change management, the industrial doctors can properly adopt the changes that are mostly happening within the industry.

Enhance the Cybersecurity

If the pharmaceutical industry accurately points some specific doctors to effectively enhance the cybersecurity, then the industry can properly combine doctor and technology within the organisation.

Critical reflection

During the research I face different types of challenges such as selecting an appropriate method to collect data and information. Maintain the timeline to conduct the overall research and selecting some effective articles or journals to properly justify the research topic. However, I also achieve some specific knowledge throughout research such as finding data from journals, articles, and websites. The research also helps to understand an effectiveness of an appropriate data collection methods to justify the research’s effectiveness.

Theoretical Framework

The relationship between AI and medical practice can be assessed through theoretical frameworks, which are discussed below.

Technology Acceptance Model (TAM): The framework is used to analyse the thoughts of the doctors and patients regarding any new technology. The tool can be used to measure the perceived view of them regarding new AI tools (Rouidi et al., 2022).

Diffusion of Innovations Theory: The theory is very effective in judging how new technologies and their ideas spread within the organisation. It can be used to examine the position of AI usage in the pharmaceutical industry (Patnaik and Bakkar, 2024).

Research Gaps

In the context of the project topic, researchers often meet several gaps, mainly in terms of available research or studies on this viewpoint. The gaps are outlined below

The limited availability of studies exploring the impacts of AI on the doctor-patient connection and patient trust limits critical exploration.

Extended data are required mainly for the study of long-term impacts of AI incorporation in the medicine sector, pharmaceutical industry, and aligning with healthcare systems.

Absence of constant regulations mainly on the adoption of AI across the world.

Evaluation of findings

The capabilities of AI in diagnostics and treatment in the pharmaceutical industry compared to doctors

AI is the most effective and advanced technology. By implementing this technology, most industries can enhance their organisational performance and efficiency. Most importantly, as per Statista, it was easy to understand that 70% of pharmaceutical industries are appropriately adopting AI. Therefore, this technology has an effective company to reduce product-making time, enhance the accuracy of personalised medicine, drug discovery, and workflow automation (Palvadi et al., 2025). However, to appropriately achieve these benefits, the technologies need human brains and skills because, without human support, AI technology cannot operate itself. Most importantly, through technological support of AI, the pharmaceutical industry can properly make some effective diagnostics and treatment plans by quickly analysing the data of patients while keeping privet the overall information. That is the main reason it was easy to say AI has an effective capability compared to doctors. AI can also enhance diagnostics and treatment planning accuracy because it analyses the information regarding the patient (Rashid and Sharma, 2025). It also helps in developing effective medicines by understanding consumers' needs and analysing the positive and negative impacts of medicines on the development of drugs. This kind of specification of AI also allows the pharmaceutical industry to reduce or avoid some major human errors. Initially, AI also help doctors to effectively focus on patient care; however, through this overall analysis, it was easy to understand that the combination of AI technology and human brains makes it easy to enhance the pharmaceutical industry’s growth and success worldwide. However, as per Statista, the global pharmaceutical industry experienced an effective growth of around 100 billion dollars in the year 2023 by appropriately combining AI technology and the human brain (statista.com, 2025)

Critical Reflections

This part mainly helps to properly analyse the challenges and achievements that are mainly gathered during the overall research. During the conduct of this research first challenge that was mainly faced was the topic selection because this is independent research, which is why an effective and justified topic selection is necessary. Moreover, to select a specific research topic researcher appropriately gathers some websites and articles, then the researcher understands that in recent years, most pharmaceutical organisations have adopted advanced technologies such as AI (Kulkov, 2021). Therefore, as per an opportunity or achievement that was gathered through this research is when some specific data and information collected for justify the research it was easy to realise that most researchers have already done their research regarding this topic, and different researchers have different types of perspectives. During the phase of study planning and organisation, it was easy to recognise that study planning is the most essential part because it helps and supports to appropriately conduct the overall research in a specific time and in an organised way (Hancock et al., 2021). In this research, it was also relatively easy to conclude the research by properly evaluating each part of this research and representing own perspective on this research.

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Conclusion

Based on the overall analysis it was easy to recognise that AI is the most effective technology that help the support the pharmaceutical industry to increase its organisational growth and success. However, most of the researchers also indicates that by implementing AI technology within the organisation the pharmaceutical industry not only achieve good quality of advantages it also faces some specific challenges that generate negative impact on organisational stability. Most importantly, this research explored the question will artificial intelligence (AI) replace doctors in the future of medicine. However, the overall findings mainly suggest that while AI is transforming healthcare, it is unlikely to fully replace human doctors. Additionally, AI is evolving as a collaborative tool that complements, rather than replaces, the expertise and empathy of medical professionals. Therefore, through this overall analysis it was easy to understand AI excels in data-driven, repetitive tasks. However, AI cannot replicate the human aspects of medicine such as empathy, ethical judgment, and the doctor-patient relationship that remain crucial for patient care. Furthermore, to effectively combine doctor and AI the pharmaceutical industry effectively needs to properly follow some specific recommendations such as Find the Right Partners, Leverage AI-Driven Solutions, Effective Change Management, Enhance the Cybersecurity, and Train Staff on AI.

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