Artificial intelligence in healthcare delivery: Prospects and pitfalls

This review provides a comprehensive examination of the integration of Artificial Intelligence (AI) into healthcare, focusing on its transformative implications and challenges. Utilising a systematic search strategy across electronic databases such as PubMed, Scopus, Embase, and ScienceDirect, relevant peer-reviewed articles published in English between January 2010 till date were identified. Findings reveal AI's significant impact on healthcare delivery, including its role in enhancing diagnostic precision, enabling treatment personalisation, facilitating predictive analytics, automating tasks, and driving robotics. AI algorithms demonstrate high accuracy in analysing medical images for disease diagnosis and enable the creation of tailored treatment plans based on patient data analysis. Predictive analytics identify high-risk patients for proactive interventions, while AI-powered tools streamline workflows, improving efficiency and patient experience. Additionally, AI-driven robotics automate tasks and enhance care delivery, particularly in rehabilitation and surgery. However, challenges such as data quality, interpretability, bias, and regulatory frameworks must be addressed for responsible AI implementation. Recommendations emphasise the need for robust ethical and legal frameworks, human-AI collaboration, safety validation, education, and comprehensive regulation to ensure the ethical and effective integration of AI in healthcare. This review provides valuable insights into AI's transformative potential in healthcare while advocating for responsible implementation to ensure patient safety and efficacy

Anatomic Characterization and Profilometry of Tissues with Natural Shape: A Real-time Approach for Robotic-Assisted Minimally Invasive Surgery

This master thesis is divided into two major sections. First, anatomic characterization and profilometry of tissues with natural shape: a real-time approach for robotic-assisted minimally invasive surgery (RMIS); and second, design and characterization of a novel tactile array sensor capable of differentiating among different viscoelastic tissues that exhibit time-dependent behaviour. The first part of this thesis is focused on a tissue characterization system for RMIS applications. RMIS has gained immense popularity with the advent of high-precision robotic systems. The lack of haptic feedback, however, is considered as being one of the main drawbacks of present-day RMIS systems. In order to compensate for this deficiency, a novel tissue characterization system is proposed which is inspired from the human haptic system. Hence, kinesthetic and tactile feedback which are constitutive components of human haptic system are used to characterize naturally shaped tissues. Toward this goal, a 5-degree-of-freedom robot which is called Catalys5 is equipped with a ball caster force-cell. The system is used to simulate robotic surgery maneuvers in which an admittance control approach is implemented to design the force feedback controller. The proposed method characterizes naturally shaped tissues, which is capable of touching and palpating to: a) Identify the 2D or 3D surface profile of the target tissue (profilometry), b) Measure the modulus of elasticity of any desired point on the tissue’s surface, c) Find and map the location of any lump in the tissue, and d) Map hardness distribution around the lump. Initially, silicon-rubber materials were used to build tissue phantoms with different curvatures and degrees of softness. The surface profiles were obtained using the developed profilometry algorithm and validated using a 3D scanner. In addition, several experiments were conducted on bovine tissues to evaluate all above mentioned capabilities of the system. The results of experiments on real tissues were also compared to those that are available in current literature. The results indicate that the proposed approach can be used for reliable material characterization for RMIS application. The second part of this thesis is focused on developing an array tactile sensor for distinguishing softness of viscoelastic tissues with time-dependent behaviour for use in MIS and RMIS. Review of literature on tactile sensors reveals that the vast majority deals with determining the applied contact force and object elasticity. In this research, a novel idea is proposed in which a tactile sensor array can measure rate of displacement in addition to force and displacement of any viscoelastic material during the course of a single touch. In order to verify this new array sensor, several experiments were conducted on a range of biological tissues. It was concluded that this novel tactile sensor can distinguish among the softness of real biological tissue with time-dependent behaviour

Artificial Intelligence in Healthcare Delivery: Prospects and Pitfalls

This review provides a comprehensive examination of the integration of Artificial Intelligence (AI) into healthcare, focusing on its transformative implications and challenges. Utilising a systematic search strategy across electronic databases such as PubMed, Scopus, Embase, and Sciencedirect, relevant peer-reviewed articles published in English between January 2010 till date were identified. Findings reveal AI's significant impact on healthcare delivery, including its role in enhancing diagnostic precision, enabling treatment personalisation, facilitating predictive analytics, automating tasks, and driving robotics. AI algorithms demonstrate high accuracy in analysing medical images for disease diagnosis and enable the creation of tailored treatment plans based on patient data analysis. Predictive analytics identify high-risk patients for proactive interventions, while AI-powered tools streamline workflows, improving efficiency and patient experience. Additionally, AI-driven robotics automate tasks and enhance care delivery, particularly in rehabilitation and surgery. However, challenges such as data quality, interpretability, bias, and regulatory frameworks must be addressed for responsible AI implementation. Recommendations emphasise the need for robust ethical and legal frameworks, human-AI collaboration, safety validation, education, and comprehensive regulation to ensure the ethical and effective integration of AI in healthcare. This review provides valuable insights into AI's transformative potential in healthcare while advocating for responsible implementation to ensure patient safety and efficacy

Microfabricated tactile sensors for biomedical applications: a review

During the last decades, tactile sensors based on different sensing principles have been developed due to the growing interest in robotics and, mainly, in medical applications. Several technological solutions have been employed to design tactile sensors; in particular, solutions based on microfabrication present several attractive features. Microfabrication technologies allow for developing miniaturized sensors with good performance in terms of metrological properties (e.g., accuracy, sensitivity, low power consumption, and frequency response). Small size and good metrological properties heighten the potential role of tactile sensors in medicine, making them especially attractive to be integrated in smart interfaces and microsurgical tools. This paper provides an overview of microfabricated tactile sensors, focusing on the mean principles of sensing, i.e., piezoresistive, piezoelectric and capacitive sensors. These sensors are employed for measuring contact properties, in particular force and pressure, in three main medical fields, i.e., prosthetics and artificial skin, minimal access surgery and smart interfaces for biomechanical analysis. The working principles and the metrological properties of the most promising tactile, microfabricated sensors are analyzed, together with their application in medicine. Finally, the new emerging technologies in these fields are briefly described

Haptics in Robot-Assisted Surgery: Challenges and Benefits

Robotic surgery is transforming the current surgical practice, not only by improving the conventional surgical methods but also by introducing innovative robot-enhanced approaches that broaden the capabilities of clinicians. Being mainly of man-machine collaborative type, surgical robots are seen as media that transfer pre- and intra-operative information to the operator and reproduce his/her motion, with appropriate filtering, scaling, or limitation, to physically interact with the patient. The field, however, is far from maturity and, more critically, is still a subject of controversy in medical communities. Limited or absent haptic feedback is reputed to be among reasons that impede further spread of surgical robots. In this paper objectives and challenges of deploying haptic technologies in surgical robotics is discussed and a systematic review is performed on works that have studied the effects of providing haptic information to the users in major branches of robotic surgery. It has been tried to encompass both classical works and the state of the art approaches, aiming at delivering a comprehensive and balanced survey both for researchers starting their work in this field and for the experts

Haptic assessment of tissue stiffness in locating and identifying gynaecological cancer in human tissue

Gynaecological surgeons are not able to gather adequate tissue feedback during minimal access surgery for cancer treatment. This can result in failure to locate tumour boundaries and to ensure these are completely resected within tumour-free resection margins. Surgeons achieve significantly better surgical and oncological outcomes if they can identify the precise location of a gynaecological tumour. Indeed, the true nature of tumour, whether benign or cancerous, is often not known prior to surgery. If more details were available in relation to the characteristics that differentiate gynaecological cancer in tumours, this would enable more accurate diagnosis and help in the planning of surgery. HYPOTHESIS: Haptic technology has the potential to enhance the surgeon’s degree of perception during minimal access surgery. Alteration in tissue stiffness in gynaecological tumours, thought to be associated with the accelerated multiplication of cancer cells, should allow their location to be identified and help in determining the likelihood of malignancy. METHOD: Setting: (i) Guy's & St Thomas' Hospital (ii) Dept of Informatics (King's College London).Permission from the National Research Ethics Committee and Research & Development (R&D) approval were sought from the National Health Service. The Phantom Omni, capable of 3D motion tracking, attached to a nano-17 force sensor, was used to capture real-time position data and force data. Uniaxial indentation palpation behaviour was used. The indentation depth was calculated using the displacement of the probe from the surface to the deepest point for each contact. The tissue stiffness (TS) was then calculated.The haptic probe was tested first on silicone models with embedded nodules mimicking tumour(s). This was followed by assessing TS ex-vivo using a haptic probe on fresh human gynaecological organs that had been removed in surgery. Tissue stiffness maps were generated in real time using the haptic device by converting stiffness values into RGB values. Surgeons also manually palpated and recorded the site of the tumour. Histology was used as the gold standard for location and cancer diagnosis. Manual palpation and haptic data were compared for accuracy on tumour location. The tissue stiffness calculated by the haptic probe was compared in cancer and control specimens. Several data analysis techniques were applied to derive results.CONTRIBUTIONS: Haptic indentation probe was tested for the first time on fresh human gynaecological organs to locate cancer in a clinical setting. We are the first one to evaluate the accuracy of cancer diagnosis in human gynaecological organs with a force sensing haptic indentation probe measuring tissue stiffness

Advances in Haptics, Tactile Sensing, and Manipulation for Robot-Assisted Minimally Invasive Surgery, Noninvasive Surgery, and Diagnosis

The developments of medical practices and medical technologies have always progressed concurrently. The relatively recent developments in endoscopic technologies have allowed the realization of the “minimally invasive” form of surgeries. The advancements in robotics facilitate precise surgeries that are often integrated with medical image guidance capability. This in turn has driven the further development of technology to compensate for the unique complexities engendered by this new format and to improve the performance and broaden the scope of the procedures that can be performed. Medical robotics has been a central component of this development due to the highly suitable characteristics that a robotic system can purport, including highly optimizable mechanical conformation and the ability to program assistive functions in medical robots for surgeons to perform safe and accurate minimally invasive surgeries. In addition, combining the robot-assisted interventions with touch-sensing and medical imaging technologies can greatly improve the available information and thus help to ensure that minimally invasive surgeries continue to gain popularity and stay at the focus of modern medical technology development. This paper presents a state-of-the-art review of robotic systems for minimally invasive and noninvasive surgeries, precise surgeries, diagnoses, and their corresponding technologies

Force Sensing Surgical Scissor Blades using Fibre Bragg Grating Sensors

This thesis considers the development and analysis of unique sensorised surgical scissor blades for application in minimally invasive robotic surgery (MIRS). The lack of haptic (force and tactile) feedback to the user is currently an unresolved issue with modern MIRS systems. This thesis presents details on smart sensing scissor blades which enable the measurement of instrument-tissue interaction forces for the purpose of force reflection and tissue property identification. A review of current literature established that there exists a need for small compact, biocompatible, sterilisable and robust sensors which meet the demands of current MIRS instruments. Therefore, the sensorised blades exploit the strain sensing capabilities of a single fibre Bragg grating (FBG) sensor bonded to their surface. The nature and magnitude of the strain likely to be experienced by the blades, and consequently the FBG sensor, while cutting soft tissue samples were characterised through the use of an application specific test-bed. Using the sensorised blades to estimate fracture properties is proposed, hence two methods of extracting fracture toughness information from the test samples are assessed and compared. Investigations were carried out on the factors affecting the transfer of strain from the blade material to the core of the FBG sensor for surface mounted or partially embedded arrangements. Results show that adhesive bond length, thickness and stiffness need to be carefully specified when bonding FBG sensors to ensure effective strain transfer. Calibration and dynamic cutting experiments were carried out using the characterisation test-bed. The complex nature of the blade interaction forces were modelled, primarily for the purpose of decoupling the direct, lateral, friction and fracture strains experienced by the bonded FBG sensor during cutting. The modelled and experimental results show that the approach taken in sensorising the blade enables detailed cutting force data to be obtained and consequently leads to a unique method in estimating the kinetic friction coefficient for the blades. The forces measured using the FBG are validated against a commercial load cell used in the test-bed. This research work demonstrates that this unique approach of placing a single optical fibre onto the scissor blades can, in an unobtrusive manner, measure interblade friction forces and material fracture properties occurring at the blade-tissue interface